Last Updated: 2025-06-19
- Overview
- Video Inpainting Challenges
- State-of-the-Art Methods
- Implementation Guide
- Temporal Consistency Techniques
- Flow-Guided Inpainting
- Deep Learning Approaches
- Evaluation Metrics
- Applications
- Future Directions
Video inpainting is the process of filling in missing or corrupted regions in video sequences with visually plausible content. Unlike image inpainting, video inpainting must maintain temporal coherence across frames while handling complex motion patterns, occlusions, and appearance changes.
- Temporal Consistency: Smooth transitions across frames
- Motion Coherence: Realistic motion in inpainted regions
- Texture Preservation: Maintaining visual quality
- Semantic Correctness: Plausible content generation
- Efficiency: Reasonable processing time for practical use
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class TemporalCoherenceModule(nn.Module):
def __init__(self, feature_dim=256, memory_size=5):
super(TemporalCoherenceModule, self).__init__()
# Temporal memory
self.memory_size = memory_size
self.feature_dim = feature_dim
# Temporal attention
self.temporal_attention = nn.MultiheadAttention(
embed_dim=feature_dim,
num_heads=8,
dropout=0.1
)
# Temporal consistency loss
self.consistency_net = nn.Sequential(
nn.Conv3d(feature_dim, 128, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(128, 64, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(64, 1, kernel_size=(3, 3, 3), padding=1)
)
def forward(self, features, temporal_memory=None):
"""
Args:
features: (B, T, C, H, W) current features
temporal_memory: Previous frame features
"""
B, T, C, H, W = features.shape
# Reshape for attention
features_flat = features.permute(1, 0, 3, 4, 2).reshape(T, B*H*W, C)
if temporal_memory is not None:
# Concatenate with memory
memory_flat = temporal_memory.permute(1, 0, 3, 4, 2).reshape(-1, B*H*W, C)
# Apply temporal attention
attended_features, _ = self.temporal_attention(
features_flat,
memory_flat,
memory_flat
)
else:
# Self-attention within current features
attended_features, _ = self.temporal_attention(
features_flat,
features_flat,
features_flat
)
# Reshape back
attended_features = attended_features.reshape(T, B, H, W, C).permute(1, 0, 4, 2, 3)
# Compute temporal consistency score
consistency_score = self.consistency_net(attended_features.permute(0, 2, 1, 3, 4))
return attended_features, consistency_score
def compute_temporal_loss(self, inpainted_frames, original_frames, masks):
"""Compute temporal consistency loss"""
# Optical flow between consecutive frames
flow_loss = self.compute_flow_loss(inpainted_frames, masks)
# Feature similarity loss
feature_loss = self.compute_feature_similarity_loss(inpainted_frames)
# Motion smoothness loss
motion_loss = self.compute_motion_smoothness_loss(inpainted_frames, masks)
return flow_loss + 0.5 * feature_loss + 0.3 * motion_loss
def compute_flow_loss(self, frames, masks):
"""Ensure consistent optical flow"""
flow_loss = 0
for t in range(len(frames) - 1):
# Compute flow between consecutive frames
flow = self.estimate_flow(frames[t], frames[t+1])
# Warp frame t to t+1
warped = self.warp_frame(frames[t], flow)
# Compute difference in valid regions
valid_mask = masks[t] * masks[t+1]
diff = (warped - frames[t+1]) * valid_mask
flow_loss += torch.mean(torch.abs(diff))
return flow_loss / (len(frames) - 1)class MotionAwareInpainting(nn.Module):
def __init__(self, input_channels=3, hidden_dim=64):
super(MotionAwareInpainting, self).__init__()
# Motion encoder
self.motion_encoder = nn.Sequential(
nn.Conv3d(input_channels * 2, hidden_dim, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(hidden_dim, hidden_dim * 2, kernel_size=(3, 3, 3), padding=1),
nn.ReLU()
)
# Motion predictor
self.motion_predictor = MotionPredictor(hidden_dim * 2)
# Motion-guided inpainting
self.inpainting_net = MotionGuidedGenerator(hidden_dim * 2)
def forward(self, frames, masks, reference_frames=None):
"""
Args:
frames: (B, T, C, H, W) input frames with holes
masks: (B, T, 1, H, W) binary masks (1 for holes)
reference_frames: Optional reference frames for motion
"""
B, T, C, H, W = frames.shape
# Estimate motion patterns
if reference_frames is not None:
motion_input = torch.cat([frames, reference_frames], dim=2)
else:
# Use neighboring frames for motion estimation
motion_input = self.create_motion_pairs(frames)
# Encode motion
motion_features = self.motion_encoder(motion_input.permute(0, 2, 1, 3, 4))
# Predict motion in missing regions
predicted_motion = self.motion_predictor(motion_features, masks)
# Generate inpainted content
inpainted = self.inpainting_net(frames, masks, predicted_motion)
return inpainted, predicted_motion
def create_motion_pairs(self, frames):
"""Create frame pairs for motion estimation"""
T = frames.shape[1]
pairs = []
for t in range(T):
if t > 0:
prev_frame = frames[:, t-1]
else:
prev_frame = frames[:, t]
if t < T-1:
next_frame = frames[:, t+1]
else:
next_frame = frames[:, t]
pair = torch.stack([prev_frame, next_frame], dim=2)
pairs.append(pair)
return torch.stack(pairs, dim=1)
class MotionPredictor(nn.Module):
def __init__(self, feature_dim):
super(MotionPredictor, self).__init__()
# Flow estimation network
self.flow_net = nn.Sequential(
nn.Conv2d(feature_dim, 128, 3, padding=1),
nn.ReLU(),
nn.Conv2d(128, 64, 3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 2, 3, padding=1) # 2D flow field
)
# Motion refinement
self.refine_net = nn.Sequential(
nn.Conv2d(feature_dim + 2, 64, 3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 2, 3, padding=1)
)
def forward(self, motion_features, masks):
"""Predict motion in masked regions"""
B, C, T, H, W = motion_features.shape
predicted_flows = []
for t in range(T):
# Extract features for frame t
feat_t = motion_features[:, :, t]
# Initial flow prediction
flow = self.flow_net(feat_t)
# Refine flow in masked regions
mask_t = masks[:, t]
flow_input = torch.cat([feat_t, flow * mask_t], dim=1)
refined_flow = self.refine_net(flow_input)
# Combine flows
final_flow = flow * (1 - mask_t) + refined_flow * mask_t
predicted_flows.append(final_flow)
return torch.stack(predicted_flows, dim=1)class OcclusionAwareInpainting(nn.Module):
def __init__(self):
super(OcclusionAwareInpainting, self).__init__()
# Occlusion detection
self.occlusion_detector = OcclusionDetector()
# Layered representation
self.layer_decomposition = LayerDecomposition()
# Layer-wise inpainting
self.foreground_inpainter = InpaintingNetwork(name='foreground')
self.background_inpainter = InpaintingNetwork(name='background')
# Layer composition
self.compositor = LayerCompositor()
def forward(self, frames, masks):
"""Handle complex occlusions in video inpainting"""
# Detect occlusion boundaries and types
occlusion_maps = self.occlusion_detector(frames, masks)
# Decompose into layers
foreground, background, alpha_maps = self.layer_decomposition(
frames, masks, occlusion_maps
)
# Inpaint each layer separately
inpainted_fg = self.foreground_inpainter(foreground, masks * alpha_maps)
inpainted_bg = self.background_inpainter(background, masks * (1 - alpha_maps))
# Composite layers
final_frames = self.compositor(
inpainted_fg, inpainted_bg, alpha_maps, masks
)
return final_frames
class OcclusionDetector(nn.Module):
def __init__(self):
super(OcclusionDetector, self).__init__()
self.edge_detector = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 1, 3, padding=1),
nn.Sigmoid()
)
self.motion_boundary_detector = nn.Sequential(
nn.Conv3d(2, 32, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(32, 16, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(16, 1, kernel_size=(3, 3, 3), padding=1),
nn.Sigmoid()
)
def forward(self, frames, masks):
"""Detect occlusion boundaries"""
B, T, C, H, W = frames.shape
# Spatial edge detection
edge_maps = []
for t in range(T):
edges = self.edge_detector(frames[:, t])
edge_maps.append(edges)
edge_maps = torch.stack(edge_maps, dim=1)
# Motion boundaries
flow_pairs = []
for t in range(T-1):
flow = self.compute_flow(frames[:, t], frames[:, t+1])
flow_pairs.append(flow)
if len(flow_pairs) > 0:
flow_volume = torch.stack(flow_pairs, dim=2).permute(0, 1, 3, 4, 2)
motion_boundaries = self.motion_boundary_detector(flow_volume)
else:
motion_boundaries = torch.zeros_like(edge_maps)
# Combine cues
occlusion_maps = torch.max(edge_maps, motion_boundaries)
return occlusion_mapsclass DeepFlowGuidedInpainting(nn.Module):
def __init__(self):
super(DeepFlowGuidedInpainting, self).__init__()
# Flow completion network
self.flow_completion = FlowCompletionNetwork()
# Feature extraction
self.encoder = EncoderNetwork()
# Propagation network
self.propagation = PropagationNetwork()
# Content hallucination
self.hallucination = ContentHallucination()
# Decoder
self.decoder = DecoderNetwork()
def forward(self, frames, masks):
"""
Complete video inpainting pipeline
Args:
frames: (B, T, C, H, W) input frames
masks: (B, T, 1, H, W) inpainting masks
"""
B, T, C, H, W = frames.shape
# Step 1: Complete optical flow
flow_forward, flow_backward = self.complete_flow(frames, masks)
# Step 2: Extract features
features = self.encoder(frames)
# Step 3: Propagate features using completed flow
propagated_features = self.propagation(
features, flow_forward, flow_backward, masks
)
# Step 4: Hallucinate content for remaining holes
hallucinated_features = self.hallucination(
propagated_features, masks
)
# Step 5: Decode to frames
inpainted_frames = self.decoder(hallucinated_features)
# Step 6: Blend with original
output = frames * (1 - masks) + inpainted_frames * masks
return output
def complete_flow(self, frames, masks):
"""Complete optical flow in masked regions"""
flow_forward = []
flow_backward = []
# Forward flow
for t in range(len(frames) - 1):
flow = self.flow_completion(
frames[:, t], frames[:, t+1],
masks[:, t], masks[:, t+1]
)
flow_forward.append(flow)
# Backward flow
for t in range(len(frames) - 1, 0, -1):
flow = self.flow_completion(
frames[:, t], frames[:, t-1],
masks[:, t], masks[:, t-1]
)
flow_backward.append(flow)
return flow_forward, flow_backward
class FlowCompletionNetwork(nn.Module):
def __init__(self):
super(FlowCompletionNetwork, self).__init__()
# Initial flow estimation
self.flow_estimator = nn.Sequential(
nn.Conv2d(6, 64, 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(128, 256, 3, stride=2, padding=1),
nn.ReLU()
)
# Flow completion
self.completion_net = nn.Sequential(
nn.Conv2d(256 + 2, 256, 3, padding=1),
nn.ReLU(),
nn.Conv2d(256, 128, 3, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(32, 2, 4, stride=2, padding=1)
)
def forward(self, frame1, frame2, mask1, mask2):
"""Complete flow between two frames"""
# Concatenate frames
input_frames = torch.cat([frame1, frame2], dim=1)
# Extract features
flow_features = self.flow_estimator(input_frames)
# Combine masks
combined_mask = torch.max(mask1, mask2)
mask_resized = F.interpolate(combined_mask, size=flow_features.shape[-2:])
# Complete flow
completion_input = torch.cat([flow_features, mask_resized], dim=1)
completed_flow = self.completion_net(completion_input)
return completed_flow
class PropagationNetwork(nn.Module):
def __init__(self, feature_dim=256):
super(PropagationNetwork, self).__init__()
self.feature_dim = feature_dim
# Bidirectional propagation
self.forward_prop = nn.GRU(
feature_dim, feature_dim // 2,
bidirectional=False, batch_first=True
)
self.backward_prop = nn.GRU(
feature_dim, feature_dim // 2,
bidirectional=False, batch_first=True
)
# Fusion network
self.fusion = nn.Sequential(
nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
nn.ReLU(),
nn.Conv2d(feature_dim, feature_dim, 3, padding=1)
)
def forward(self, features, flow_forward, flow_backward, masks):
"""Propagate features bidirectionally"""
B, T, C, H, W = features.shape
# Reshape for RNN
features_flat = features.view(B, T, -1)
# Forward propagation
forward_out, _ = self.forward_prop(features_flat)
# Backward propagation
backward_input = features_flat.flip(dims=[1])
backward_out, _ = self.backward_prop(backward_input)
backward_out = backward_out.flip(dims=[1])
# Combine bidirectional features
combined = torch.cat([forward_out, backward_out], dim=-1)
combined = combined.view(B, T, C, H, W)
# Apply flow warping
warped_features = self.flow_warp(combined, flow_forward, flow_backward)
# Fusion
fused = self.fusion(warped_features.view(B*T, C, H, W))
fused = fused.view(B, T, C, H, W)
return fused
def flow_warp(self, features, flow_forward, flow_backward):
"""Warp features using optical flow"""
warped = features.clone()
# Forward warping
for t in range(len(flow_forward)):
if t < features.shape[1] - 1:
warped[:, t+1] = self.warp_frame(
features[:, t], flow_forward[t]
)
# Backward warping
for t in range(len(flow_backward)):
if t < features.shape[1] - 1:
warped[:, -(t+2)] = self.warp_frame(
features[:, -(t+1)], flow_backward[t]
)
return warpedclass OnionPeelNetwork(nn.Module):
def __init__(self, num_layers=3):
super(OnionPeelNetwork, self).__init__()
self.num_layers = num_layers
# Multi-scale encoder
self.encoders = nn.ModuleList([
nn.Sequential(
nn.Conv2d(4, 64 * (2**i), 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(64 * (2**i), 64 * (2**i), 3, padding=1),
nn.ReLU()
) for i in range(num_layers)
])
# Recurrent propagation modules
self.propagators = nn.ModuleList([
RecurrentPropagation(64 * (2**i)) for i in range(num_layers)
])
# Multi-scale decoder
self.decoders = nn.ModuleList([
nn.Sequential(
nn.ConvTranspose2d(64 * (2**i), 64 * (2**max(i-1, 0)),
4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(64 * (2**max(i-1, 0)), 3 if i == 0 else 64 * (2**max(i-1, 0)),
3, padding=1)
) for i in range(num_layers)
])
def forward(self, frames, masks):
"""
Onion-peel inpainting from boundaries to center
"""
B, T, C, H, W = frames.shape
# Prepare input
input_frames = torch.cat([frames, masks], dim=2)
# Multi-scale encoding
encoded_features = []
current_input = input_frames
for i, encoder in enumerate(self.encoders):
# Encode at current scale
features = []
for t in range(T):
feat = encoder(current_input[:, t])
features.append(feat)
features = torch.stack(features, dim=1)
encoded_features.append(features)
# Downsample for next scale
if i < self.num_layers - 1:
current_input = F.interpolate(
current_input.view(B*T, C+1, H, W),
scale_factor=0.5
).view(B, T, C+1, H//(2**(i+1)), W//(2**(i+1)))
# Recurrent propagation at each scale
propagated_features = []
for i, (features, propagator) in enumerate(
zip(encoded_features, self.propagators)
):
# Create dilated masks for onion peeling
scale_masks = self.create_dilated_masks(
masks,
scale=2**i,
num_iterations=5
)
# Propagate at current scale
prop_feat = propagator(features, scale_masks)
propagated_features.append(prop_feat)
# Multi-scale decoding
decoded = None
for i in range(self.num_layers - 1, -1, -1):
if decoded is None:
decoded = propagated_features[i]
else:
# Upsample and combine
decoded = F.interpolate(
decoded.view(B*T, -1, decoded.shape[-2], decoded.shape[-1]),
scale_factor=2
).view(B, T, -1, decoded.shape[-2]*2, decoded.shape[-1]*2)
# Skip connection
decoded = decoded + propagated_features[i]
# Decode
decoded_frames = []
for t in range(T):
frame = self.decoders[i](decoded[:, t])
decoded_frames.append(frame)
decoded = torch.stack(decoded_frames, dim=1)
return decoded
def create_dilated_masks(self, masks, scale, num_iterations):
"""Create dilated masks for onion peeling"""
dilated_masks = []
kernel_size = 3 * scale
kernel = torch.ones(1, 1, kernel_size, kernel_size).to(masks.device)
current_mask = masks
for _ in range(num_iterations):
# Dilate mask
dilated = F.conv2d(
current_mask.view(-1, 1, masks.shape[-2], masks.shape[-1]),
kernel,
padding=kernel_size//2
)
dilated = (dilated > 0).float()
dilated = dilated.view(masks.shape)
dilated_masks.append(dilated)
current_mask = dilated
return dilated_masks
class RecurrentPropagation(nn.Module):
def __init__(self, feature_dim):
super(RecurrentPropagation, self).__init__()
self.feature_dim = feature_dim
# Convolutional GRU
self.conv_gru = ConvGRU(
input_dim=feature_dim,
hidden_dim=feature_dim,
kernel_size=3
)
# Attention mechanism
self.attention = nn.Sequential(
nn.Conv2d(feature_dim * 2, feature_dim, 3, padding=1),
nn.ReLU(),
nn.Conv2d(feature_dim, feature_dim, 3, padding=1),
nn.Sigmoid()
)
def forward(self, features, masks):
"""Recurrent propagation with attention"""
B, T, C, H, W = features.shape
# Initialize hidden state
hidden = torch.zeros(B, C, H, W).to(features.device)
outputs = []
for t in range(T):
# Current features and mask
feat_t = features[:, t]
mask_t = masks[t] if isinstance(masks, list) else masks[:, t]
# Compute attention weights
attn_input = torch.cat([feat_t, hidden], dim=1)
attn_weights = self.attention(attn_input)
# Apply attention
attended_feat = feat_t * attn_weights
# Update hidden state
hidden = self.conv_gru(attended_feat, hidden, mask_t)
outputs.append(hidden)
return torch.stack(outputs, dim=1)class CopyPasteNetwork(nn.Module):
def __init__(self):
super(CopyPasteNetwork, self).__init__()
# Feature extractor
self.encoder = ResNetEncoder()
# Contextual attention
self.contextual_attention = ContextualAttention(
patch_size=3,
propagate_size=3,
stride=1
)
# Memory bank
self.memory_bank = MemoryBank(capacity=100)
# Refinement network
self.refinement = RefinementNetwork()
def forward(self, frames, masks):
"""Copy and paste inpainting"""
B, T, C, H, W = frames.shape
# Extract features
features = self.encoder(frames.view(B*T, C, H, W))
features = features.view(B, T, -1, features.shape[-2], features.shape[-1])
inpainted_frames = []
for t in range(T):
# Get current frame and mask
frame_t = frames[:, t]
mask_t = masks[:, t]
feat_t = features[:, t]
# Search for best patches in memory
if t > 0:
# Use previous frames as reference
ref_features = features[:, :t]
ref_frames = frames[:, :t]
# Contextual attention
matched_patches, attention_map = self.contextual_attention(
feat_t, ref_features, mask_t
)
# Copy and paste
pasted_frame = self.copy_paste(
frame_t, ref_frames, attention_map, mask_t
)
else:
# For first frame, use spatial inpainting
pasted_frame = self.spatial_inpaint(frame_t, mask_t)
# Refine result
refined_frame = self.refinement(pasted_frame, frame_t, mask_t)
inpainted_frames.append(refined_frame)
# Update memory bank
self.memory_bank.update(refined_frame, feat_t)
return torch.stack(inpainted_frames, dim=1)
def copy_paste(self, target_frame, ref_frames, attention_map, mask):
"""Copy patches from reference frames"""
B, T_ref, C, H, W = ref_frames.shape
# Reshape attention map
attn = attention_map.view(B, T_ref, H*W, H, W)
# For each pixel in mask, find best matching patch
pasted = target_frame.clone()
for b in range(B):
mask_indices = torch.nonzero(mask[b, 0] > 0)
for idx in mask_indices:
y, x = idx[0].item(), idx[1].item()
# Find best matching position
attn_scores = attn[b, :, :, y, x]
best_t, best_pos = torch.unravel_index(
attn_scores.argmax(),
attn_scores.shape
)
best_y, best_x = best_pos // W, best_pos % W
# Copy patch
patch_size = 3
y1 = max(0, y - patch_size // 2)
y2 = min(H, y + patch_size // 2 + 1)
x1 = max(0, x - patch_size // 2)
x2 = min(W, x + patch_size // 2 + 1)
ref_y1 = max(0, best_y - patch_size // 2)
ref_y2 = min(H, best_y + patch_size // 2 + 1)
ref_x1 = max(0, best_x - patch_size // 2)
ref_x2 = min(W, best_x + patch_size // 2 + 1)
# Ensure same size
h = min(y2-y1, ref_y2-ref_y1)
w = min(x2-x1, ref_x2-ref_x1)
pasted[b, :, y1:y1+h, x1:x1+w] = \
ref_frames[b, best_t, :, ref_y1:ref_y1+h, ref_x1:ref_x1+w]
return pasted
class ContextualAttention(nn.Module):
def __init__(self, patch_size=3, propagate_size=3, stride=1):
super(ContextualAttention, self).__init__()
self.patch_size = patch_size
self.propagate_size = propagate_size
self.stride = stride
# Feature transformation
self.feat_transform = nn.Conv2d(256, 128, 1)
def forward(self, query_features, ref_features, mask):
"""Compute attention between query and reference features"""
B, C, H, W = query_features.shape
B, T_ref, C, H_ref, W_ref = ref_features.shape
# Transform features
query_feat = self.feat_transform(query_features)
ref_feat = self.feat_transform(ref_features.view(B*T_ref, C, H_ref, W_ref))
ref_feat = ref_feat.view(B, T_ref, -1, H_ref, W_ref)
# Extract patches
query_patches = self.extract_patches(query_feat, self.patch_size)
ref_patches = self.extract_patches(
ref_feat.view(B*T_ref, -1, H_ref, W_ref),
self.patch_size
)
# Compute similarity
attention_maps = []
for t in range(T_ref):
# Compute cosine similarity
similarity = F.cosine_similarity(
query_patches.unsqueeze(2),
ref_patches[t].unsqueeze(1),
dim=3
)
# Apply mask
similarity = similarity * mask.view(B, -1, 1)
# Softmax normalization
attention = F.softmax(similarity, dim=2)
attention_maps.append(attention)
return ref_patches, torch.stack(attention_maps, dim=1)import cv2
import torch
import numpy as np
from collections import deque
class VideoInpaintingPipeline:
def __init__(self, model_type='flow_guided', device='cuda'):
self.device = torch.device(device)
self.model = self.load_model(model_type)
self.preprocessor = VideoPreprocessor()
self.postprocessor = VideoPostprocessor()
def load_model(self, model_type):
"""Load pre-trained inpainting model"""
models = {
'flow_guided': DeepFlowGuidedInpainting(),
'onion_peel': OnionPeelNetwork(),
'copy_paste': CopyPasteNetwork()
}
model = models[model_type]
# Load pre-trained weights
checkpoint = torch.load(f'weights/{model_type}_inpainting.pth')
model.load_state_dict(checkpoint['model_state_dict'])
model.to(self.device)
model.eval()
return model
def inpaint_video(self, video_path, mask_path, output_path):
"""Inpaint entire video"""
# Load video and masks
frames, fps, frame_size = self.load_video(video_path)
masks = self.load_masks(mask_path, len(frames))
# Preprocess
processed_frames, processed_masks = self.preprocessor(frames, masks)
# Split into batches for memory efficiency
batch_size = 16
inpainted_frames = []
for i in range(0, len(processed_frames), batch_size):
batch_frames = processed_frames[i:i+batch_size]
batch_masks = processed_masks[i:i+batch_size]
# Convert to tensors
frames_tensor = torch.from_numpy(batch_frames).to(self.device)
masks_tensor = torch.from_numpy(batch_masks).to(self.device)
# Inpaint
with torch.no_grad():
inpainted_batch = self.model(frames_tensor, masks_tensor)
# Convert back to numpy
inpainted_batch = inpainted_batch.cpu().numpy()
inpainted_frames.extend(inpainted_batch)
# Postprocess
final_frames = self.postprocessor(
np.array(inpainted_frames),
frames,
masks
)
# Save video
self.save_video(final_frames, output_path, fps, frame_size)
def load_video(self, video_path):
"""Load video frames"""
cap = cv2.VideoCapture(video_path)
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
cap.release()
return np.array(frames), fps, (width, height)
def load_masks(self, mask_path, num_frames):
"""Load inpainting masks"""
if os.path.isdir(mask_path):
# Load frame-by-frame masks
masks = []
for i in range(num_frames):
mask_file = os.path.join(mask_path, f'mask_{i:05d}.png')
if os.path.exists(mask_file):
mask = cv2.imread(mask_file, cv2.IMREAD_GRAYSCALE)
mask = (mask > 127).astype(np.float32)
else:
# Use previous mask or zeros
mask = masks[-1] if masks else np.zeros((height, width))
masks.append(mask)
else:
# Single mask for all frames
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
mask = (mask > 127).astype(np.float32)
masks = [mask] * num_frames
return np.array(masks)
def save_video(self, frames, output_path, fps, frame_size):
"""Save inpainted video"""
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, frame_size)
for frame in frames:
frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
out.write(frame_bgr)
out.release()
class VideoPreprocessor:
def __init__(self, target_size=(432, 240)):
self.target_size = target_size
def __call__(self, frames, masks):
"""Preprocess video frames and masks"""
# Resize
processed_frames = []
processed_masks = []
for frame, mask in zip(frames, masks):
# Resize frame
frame_resized = cv2.resize(frame, self.target_size)
processed_frames.append(frame_resized)
# Resize mask
mask_resized = cv2.resize(
mask.astype(np.float32),
self.target_size
)
mask_resized = (mask_resized > 0.5).astype(np.float32)
processed_masks.append(mask_resized)
# Normalize frames
processed_frames = np.array(processed_frames).astype(np.float32) / 255.0
processed_masks = np.array(processed_masks)
# Add channel dimension to masks
processed_masks = np.expand_dims(processed_masks, axis=-1)
# Convert to NCHW format
processed_frames = processed_frames.transpose(0, 3, 1, 2)
processed_masks = processed_masks.transpose(0, 3, 1, 2)
return processed_frames, processed_masks
class VideoPostprocessor:
def __init__(self):
self.temporal_filter = TemporalFilter()
self.color_corrector = ColorCorrector()
def __call__(self, inpainted_frames, original_frames, masks):
"""Post-process inpainted frames"""
# Convert back to HWC format
inpainted_frames = inpainted_frames.transpose(0, 2, 3, 1)
# Denormalize
inpainted_frames = (inpainted_frames * 255).astype(np.uint8)
# Resize to original size
H, W = original_frames.shape[1:3]
resized_frames = []
for frame in inpainted_frames:
resized = cv2.resize(frame, (W, H))
resized_frames.append(resized)
inpainted_frames = np.array(resized_frames)
# Apply temporal filtering
filtered_frames = self.temporal_filter(inpainted_frames, masks)
# Color correction
corrected_frames = self.color_corrector(
filtered_frames,
original_frames,
masks
)
return corrected_frames
class TemporalFilter:
def __init__(self, window_size=5):
self.window_size = window_size
def __call__(self, frames, masks):
"""Apply temporal filtering for smoothness"""
filtered = np.zeros_like(frames)
T = len(frames)
for t in range(T):
# Get temporal window
start = max(0, t - self.window_size // 2)
end = min(T, t + self.window_size // 2 + 1)
# Weighted average
weights = np.exp(-np.abs(np.arange(start, end) - t) / 2)
weights = weights / weights.sum()
for i, w in enumerate(weights):
filtered[t] += w * frames[start + i]
return filtered.astype(np.uint8)class MultiFrameAggregation(nn.Module):
def __init__(self, num_frames=5):
super(MultiFrameAggregation, self).__init__()
self.num_frames = num_frames
# Feature alignment
self.alignment_net = nn.Sequential(
nn.Conv2d(6, 64, 3, padding=1),
nn.ReLU(),
nn.Conv2d(64, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 2, 3, padding=1)
)
# Feature fusion
self.fusion_net = nn.Sequential(
nn.Conv3d(256, 128, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(128, 64, kernel_size=(3, 3, 3), padding=1),
nn.ReLU(),
nn.Conv3d(64, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1))
)
def forward(self, features, reference_frame_idx):
"""
Aggregate features from multiple frames
Args:
features: (B, T, C, H, W) frame features
reference_frame_idx: Index of reference frame
"""
B, T, C, H, W = features.shape
# Select frames around reference
start_idx = max(0, reference_frame_idx - self.num_frames // 2)
end_idx = min(T, reference_frame_idx + self.num_frames // 2 + 1)
selected_features = features[:, start_idx:end_idx]
ref_features = features[:, reference_frame_idx]
# Align features to reference frame
aligned_features = []
for i in range(selected_features.shape[1]):
if start_idx + i == reference_frame_idx:
aligned_features.append(selected_features[:, i])
else:
# Compute alignment
feat_pair = torch.cat([
selected_features[:, i],
ref_features
], dim=1)
alignment_flow = self.alignment_net(feat_pair)
# Warp features
aligned = self.warp_features(
selected_features[:, i],
alignment_flow
)
aligned_features.append(aligned)
# Stack aligned features
aligned_features = torch.stack(aligned_features, dim=1)
# Fuse features
fused = self.fusion_net(aligned_features.permute(0, 2, 1, 3, 4))
fused = fused.squeeze(2)
return fusedclass TemporalPyramidInpainting(nn.Module):
def __init__(self, pyramid_levels=3):
super(TemporalPyramidInpainting, self).__init__()
self.pyramid_levels = pyramid_levels
# Inpainting at each temporal scale
self.inpainters = nn.ModuleList([
TemporalInpainter(scale=2**i) for i in range(pyramid_levels)
])
# Fusion network
self.fusion = PyramidFusion()
def forward(self, frames, masks):
"""Multi-scale temporal inpainting"""
# Build temporal pyramid
pyramid_frames = [frames]
pyramid_masks = [masks]
for level in range(1, self.pyramid_levels):
# Temporal downsampling
downsampled_frames = frames[:, ::2**level]
downsampled_masks = masks[:, ::2**level]
pyramid_frames.append(downsampled_frames)
pyramid_masks.append(downsampled_masks)
# Inpaint at each level
inpainted_pyramid = []
for level in range(self.pyramid_levels):
inpainted = self.inpainters[level](
pyramid_frames[level],
pyramid_masks[level]
)
# Upsample to original temporal resolution
if level > 0:
inpainted = self.temporal_upsample(
inpainted,
target_length=frames.shape[1]
)
inpainted_pyramid.append(inpainted)
# Fuse pyramid levels
final_inpainted = self.fusion(inpainted_pyramid)
return final_inpainted
def temporal_upsample(self, frames, target_length):
"""Upsample frames temporally"""
B, T, C, H, W = frames.shape
# Linear interpolation
upsampled = F.interpolate(
frames.permute(0, 2, 1, 3, 4).reshape(B*C, T, H*W),
size=target_length,
mode='linear',
align_corners=True
)
upsampled = upsampled.reshape(B, C, target_length, H, W).permute(0, 2, 1, 3, 4)
return upsampledclass OpticalFlowCompletion(nn.Module):
def __init__(self):
super(OpticalFlowCompletion, self).__init__()
# Edge-aware flow completion
self.edge_detector = EdgeDetector()
# Flow diffusion
self.flow_diffusion = FlowDiffusion()
# Flow refinement
self.flow_refiner = FlowRefinement()
def forward(self, incomplete_flow, frame1, frame2, mask):
"""Complete optical flow in masked regions"""
# Detect edges for guidance
edges1 = self.edge_detector(frame1)
edges2 = self.edge_detector(frame2)
edge_map = torch.max(edges1, edges2)
# Initial flow completion by diffusion
diffused_flow = self.flow_diffusion(
incomplete_flow,
mask,
edge_map
)
# Refine flow
refined_flow = self.flow_refiner(
diffused_flow,
frame1,
frame2,
mask
)
return refined_flow
class FlowDiffusion(nn.Module):
def __init__(self, num_iterations=50):
super(FlowDiffusion, self).__init__()
self.num_iterations = num_iterations
# Learnable diffusion parameters
self.diffusion_weight = nn.Parameter(torch.tensor(0.25))
self.edge_weight = nn.Parameter(torch.tensor(0.1))
def forward(self, flow, mask, edge_map):
"""Diffuse flow from known to unknown regions"""
# Create diffusion kernel
kernel = torch.tensor([
[0, 1, 0],
[1, 0, 1],
[0, 1, 0]
], dtype=torch.float32).view(1, 1, 3, 3)
kernel = kernel.repeat(2, 1, 1, 1).to(flow.device)
# Iterative diffusion
current_flow = flow.clone()
for _ in range(self.num_iterations):
# Compute Laplacian
padded_flow = F.pad(current_flow, (1, 1, 1, 1), mode='replicate')
laplacian = F.conv2d(padded_flow, kernel, groups=2)
# Edge-aware diffusion
diffusion_rate = self.diffusion_weight * (1 - self.edge_weight * edge_map)
# Update flow in masked regions
update = laplacian * diffusion_rate * mask
current_flow = current_flow + update
# Keep known values fixed
current_flow = flow * (1 - mask) + current_flow * mask
return current_flowclass Video3DCNN(nn.Module):
def __init__(self, in_channels=4, out_channels=3):
super(Video3DCNN, self).__init__()
# 3D Encoder
self.encoder = nn.Sequential(
nn.Conv3d(in_channels, 64, kernel_size=3, padding=1),
nn.BatchNorm3d(64),
nn.ReLU(),
nn.Conv3d(64, 128, kernel_size=3, stride=(1, 2, 2), padding=1),
nn.BatchNorm3d(128),
nn.ReLU(),
nn.Conv3d(128, 256, kernel_size=3, stride=(1, 2, 2), padding=1),
nn.BatchNorm3d(256),
nn.ReLU(),
nn.Conv3d(256, 512, kernel_size=3, stride=(1, 2, 2), padding=1),
nn.BatchNorm3d(512),
nn.ReLU()
)
# Bottleneck with temporal attention
self.bottleneck = TemporalAttentionBottleneck(512)
# 3D Decoder
self.decoder = nn.Sequential(
nn.ConvTranspose3d(512, 256, kernel_size=4, stride=(1, 2, 2), padding=1),
nn.BatchNorm3d(256),
nn.ReLU(),
nn.ConvTranspose3d(256, 128, kernel_size=4, stride=(1, 2, 2), padding=1),
nn.BatchNorm3d(128),
nn.ReLU(),
nn.ConvTranspose3d(128, 64, kernel_size=4, stride=(1, 2, 2), padding=1),
nn.BatchNorm3d(64),
nn.ReLU(),
nn.Conv3d(64, out_channels, kernel_size=3, padding=1),
nn.Tanh()
)
def forward(self, frames, masks):
"""
3D CNN for video inpainting
Args:
frames: (B, T, C, H, W)
masks: (B, T, 1, H, W)
"""
# Concatenate frames and masks
x = torch.cat([frames, masks], dim=2)
# Permute to (B, C, T, H, W) for 3D convolutions
x = x.permute(0, 2, 1, 3, 4)
# Encode
encoded = self.encoder(x)
# Apply temporal attention
attended = self.bottleneck(encoded)
# Decode
decoded = self.decoder(attended)
# Permute back to (B, T, C, H, W)
output = decoded.permute(0, 2, 1, 3, 4)
return output
class TemporalAttentionBottleneck(nn.Module):
def __init__(self, channels):
super(TemporalAttentionBottleneck, self).__init__()
# Temporal self-attention
self.temporal_attn = nn.Sequential(
nn.Conv3d(channels, channels // 8, kernel_size=1),
nn.BatchNorm3d(channels // 8),
nn.ReLU(),
nn.Conv3d(channels // 8, channels, kernel_size=1),
nn.Sigmoid()
)
# Channel attention
self.channel_attn = nn.Sequential(
nn.AdaptiveAvgPool3d(1),
nn.Conv3d(channels, channels // 16, kernel_size=1),
nn.ReLU(),
nn.Conv3d(channels // 16, channels, kernel_size=1),
nn.Sigmoid()
)
def forward(self, x):
# Temporal attention
t_attn = self.temporal_attn(x)
x_t = x * t_attn
# Channel attention
c_attn = self.channel_attn(x_t)
x_out = x_t * c_attn
return x_outclass VideoTransformerInpainting(nn.Module):
def __init__(self, d_model=512, nhead=8, num_layers=6):
super(VideoTransformerInpainting, self).__init__()
# Patch embedding
self.patch_embed = VideoPatchEmbedding(
patch_size=(2, 8, 8),
in_channels=4,
embed_dim=d_model
)
# Positional encoding
self.pos_encoding = SpatioTemporalPositionalEncoding(d_model)
# Transformer encoder-decoder
self.transformer = nn.Transformer(
d_model=d_model,
nhead=nhead,
num_encoder_layers=num_layers,
num_decoder_layers=num_layers,
dim_feedforward=2048,
dropout=0.1
)
# Mask token
self.mask_token = nn.Parameter(torch.randn(1, 1, d_model))
# Output projection
self.output_proj = nn.Linear(d_model, 3 * 8 * 8 * 2) # 3 channels * patch size
def forward(self, frames, masks):
"""
Transformer-based video inpainting
"""
B, T, C, H, W = frames.shape
# Patch embedding
x = torch.cat([frames, masks], dim=2)
patches, patch_info = self.patch_embed(x)
# Replace masked patches with mask token
mask_patches = F.interpolate(
masks.permute(0, 2, 1, 3, 4),
size=(T//2, H//8, W//8),
mode='nearest'
).permute(0, 2, 3, 4, 1)
mask_patches = mask_patches.reshape(B, -1, 1)
patches = patches * (1 - mask_patches) + self.mask_token * mask_patches
# Add positional encoding
patches = self.pos_encoding(patches, patch_info)
# Transformer processing
memory = self.transformer.encoder(patches.transpose(0, 1))
output = self.transformer.decoder(patches.transpose(0, 1), memory)
output = output.transpose(0, 1)
# Project to pixel space
pixels = self.output_proj(output)
pixels = pixels.reshape(B, T//2, H//8, W//8, 3, 2, 8, 8)
pixels = pixels.permute(0, 1, 4, 5, 2, 6, 3, 7)
pixels = pixels.reshape(B, T, 3, H, W)
return pixels
class VideoPatchEmbedding(nn.Module):
def __init__(self, patch_size, in_channels, embed_dim):
super(VideoPatchEmbedding, self).__init__()
self.patch_size = patch_size
self.proj = nn.Conv3d(
in_channels,
embed_dim,
kernel_size=patch_size,
stride=patch_size
)
def forward(self, x):
B, T, C, H, W = x.shape
x = x.permute(0, 2, 1, 3, 4)
# Extract patches
patches = self.proj(x)
# Flatten patches
patches = patches.flatten(2).transpose(1, 2)
# Patch information for positional encoding
patch_info = {
'temporal_patches': patches.shape[0] // self.patch_size[0],
'height_patches': H // self.patch_size[1],
'width_patches': W // self.patch_size[2]
}
return patches, patch_infoclass VideoInpaintingMetrics:
def __init__(self):
self.lpips_model = self.load_lpips_model()
self.fvd_model = self.load_fvd_model()
def evaluate(self, inpainted_video, ground_truth_video, masks):
"""Comprehensive evaluation of video inpainting"""
metrics = {}
# Pixel-level metrics
metrics['psnr'] = self.compute_psnr(inpainted_video, ground_truth_video, masks)
metrics['ssim'] = self.compute_ssim(inpainted_video, ground_truth_video, masks)
# Perceptual metrics
metrics['lpips'] = self.compute_lpips(inpainted_video, ground_truth_video, masks)
# Video-specific metrics
metrics['temporal_consistency'] = self.compute_temporal_consistency(inpainted_video)
metrics['flow_warping_error'] = self.compute_flow_warping_error(
inpainted_video, ground_truth_video
)
# Distribution metrics
metrics['fvd'] = self.compute_fvd(inpainted_video, ground_truth_video)
return metrics
def compute_psnr(self, pred, gt, masks):
"""Peak Signal-to-Noise Ratio"""
mse = ((pred - gt) ** 2 * masks).sum() / masks.sum()
psnr = 20 * np.log10(1.0 / np.sqrt(mse))
return psnr
def compute_ssim(self, pred, gt, masks):
"""Structural Similarity Index"""
from skimage.metrics import structural_similarity
ssim_values = []
for i in range(len(pred)):
# Compute SSIM only in inpainted regions
mask = masks[i].squeeze()
if mask.sum() > 0:
ssim = structural_similarity(
pred[i], gt[i],
multichannel=True,
data_range=1.0
)
ssim_values.append(ssim)
return np.mean(ssim_values)
def compute_lpips(self, pred, gt, masks):
"""Learned Perceptual Image Patch Similarity"""
lpips_values = []
for i in range(len(pred)):
# Convert to tensor
pred_tensor = torch.from_numpy(pred[i]).permute(2, 0, 1).unsqueeze(0)
gt_tensor = torch.from_numpy(gt[i]).permute(2, 0, 1).unsqueeze(0)
# Compute LPIPS
with torch.no_grad():
lpips = self.lpips_model(pred_tensor, gt_tensor)
lpips_values.append(lpips.item())
return np.mean(lpips_values)
def compute_temporal_consistency(self, video):
"""Measure temporal consistency"""
consistency_scores = []
for t in range(1, len(video)):
# Compute optical flow
flow = cv2.calcOpticalFlowFarneback(
cv2.cvtColor(video[t-1], cv2.COLOR_RGB2GRAY),
cv2.cvtColor(video[t], cv2.COLOR_RGB2GRAY),
None, 0.5, 3, 15, 3, 5, 1.2, 0
)
# Warp previous frame
h, w = video[t].shape[:2]
flow_map = np.meshgrid(np.arange(w), np.arange(h))
flow_map = np.stack(flow_map, axis=-1).astype(np.float32)
flow_map += flow
warped = cv2.remap(
video[t-1],
flow_map[..., 0],
flow_map[..., 1],
cv2.INTER_LINEAR
)
# Compute consistency
diff = np.abs(warped - video[t]).mean()
consistency_scores.append(1 - diff)
return np.mean(consistency_scores)
def compute_flow_warping_error(self, pred, gt):
"""Flow warping error between consecutive frames"""
errors = []
for t in range(1, len(pred)):
# Compute flow in ground truth
flow_gt = cv2.calcOpticalFlowFarneback(
cv2.cvtColor(gt[t-1], cv2.COLOR_RGB2GRAY),
cv2.cvtColor(gt[t], cv2.COLOR_RGB2GRAY),
None, 0.5, 3, 15, 3, 5, 1.2, 0
)
# Compute flow in prediction
flow_pred = cv2.calcOpticalFlowFarneback(
cv2.cvtColor(pred[t-1], cv2.COLOR_RGB2GRAY),
cv2.cvtColor(pred[t], cv2.COLOR_RGB2GRAY),
None, 0.5, 3, 15, 3, 5, 1.2, 0
)
# Flow error
error = np.sqrt(((flow_pred - flow_gt) ** 2).sum(axis=-1)).mean()
errors.append(error)
return np.mean(errors)class VideoObjectRemoval:
def __init__(self):
self.segmentation_model = self.load_segmentation_model()
self.inpainting_model = self.load_inpainting_model()
self.tracker = ObjectTracker()
def remove_object(self, video_path, object_description=None, initial_mask=None):
"""Remove specified object from video"""
# Load video
frames = self.load_video(video_path)
# Get object masks
if initial_mask is not None:
# Track object through video
masks = self.tracker.track_from_mask(frames, initial_mask)
else:
# Segment object based on description
masks = self.segment_object(frames, object_description)
# Dilate masks for better removal
dilated_masks = self.dilate_masks(masks, kernel_size=15)
# Inpaint video
inpainted_frames = self.inpainting_model(frames, dilated_masks)
# Post-process for seamless integration
final_frames = self.post_process(inpainted_frames, frames, dilated_masks)
return final_frames
def segment_object(self, frames, description):
"""Segment object based on text description"""
masks = []
for frame in frames:
# Use CLIP or similar model for text-guided segmentation
mask = self.segmentation_model.segment_by_text(frame, description)
masks.append(mask)
return np.array(masks)
def dilate_masks(self, masks, kernel_size=15):
"""Dilate masks to ensure complete object removal"""
kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE,
(kernel_size, kernel_size)
)
dilated = []
for mask in masks:
dilated_mask = cv2.dilate(mask.astype(np.uint8), kernel, iterations=1)
dilated.append(dilated_mask)
return np.array(dilated)class VideoRestoration:
def __init__(self):
self.damage_detector = DamageDetector()
self.inpainting_model = VideoInpaintingPipeline()
def restore_damaged_video(self, video_path):
"""Restore damaged or corrupted video"""
frames = self.load_video(video_path)
# Detect damaged regions
damage_masks = self.damage_detector.detect_damage(frames)
# Classify damage types
damage_types = self.classify_damage(frames, damage_masks)
# Apply appropriate restoration
restored_frames = []
for i, (frame, mask, damage_type) in enumerate(
zip(frames, damage_masks, damage_types)
):
if damage_type == 'scratch':
restored = self.restore_scratch(frame, mask)
elif damage_type == 'stain':
restored = self.restore_stain(frame, mask)
elif damage_type == 'missing':
# Use temporal information
context_frames = self.get_temporal_context(frames, i)
restored = self.inpainting_model.inpaint_with_context(
frame, mask, context_frames
)
else:
restored = frame
restored_frames.append(restored)
# Apply temporal smoothing
smoothed_frames = self.temporal_smoothing(restored_frames, damage_masks)
return smoothed_frames
def restore_scratch(self, frame, mask):
"""Restore film scratches"""
# Use specialized scratch removal
# Typically involves line detection and interpolation
return self.scratch_removal_algorithm(frame, mask)
def restore_stain(self, frame, mask):
"""Restore stains or spots"""
# Color correction and blending
surrounding_color = self.sample_surrounding_color(frame, mask)
return self.blend_color(frame, mask, surrounding_color)class PrivacyProtectionInpainting:
def __init__(self):
self.face_detector = FaceDetector()
self.license_plate_detector = LicensePlateDetector()
self.inpainting_model = VideoInpaintingPipeline()
def anonymize_video(self, video_path, targets=['faces', 'license_plates']):
"""Remove privacy-sensitive information from video"""
frames = self.load_video(video_path)
# Detect sensitive regions
all_masks = np.zeros((len(frames), 1, frames[0].shape[0], frames[0].shape[1]))
if 'faces' in targets:
face_masks = self.detect_faces(frames)
all_masks = np.maximum(all_masks, face_masks)
if 'license_plates' in targets:
plate_masks = self.detect_license_plates(frames)
all_masks = np.maximum(all_masks, plate_masks)
# Apply consistent anonymization
anonymized_frames = self.apply_anonymization(frames, all_masks)
return anonymized_frames
def detect_faces(self, frames):
"""Detect and track faces across frames"""
face_masks = []
face_tracks = {}
for i, frame in enumerate(frames):
# Detect faces
faces = self.face_detector.detect(frame)
# Create mask
mask = np.zeros((frame.shape[0], frame.shape[1]))
for face in faces:
# Track face across frames for consistency
face_id = self.match_face_to_track(face, face_tracks, i)
# Add to mask with consistent ID
x, y, w, h = face['bbox']
mask[y:y+h, x:x+w] = 1
face_masks.append(mask)
return np.array(face_masks).reshape(-1, 1, *mask.shape)
def apply_anonymization(self, frames, masks):
"""Apply temporally consistent anonymization"""
# Use inpainting for natural-looking anonymization
inpainted = self.inpainting_model.inpaint_video(frames, masks)
# Ensure no residual information
verified = self.verify_anonymization(inpainted, masks)
return verifiedclass NeuralRenderingInpainting:
def __init__(self):
self.nerf_model = NeuralRadianceField()
self.inpainting_model = VideoInpaintingPipeline()
def inpaint_with_3d_understanding(self, video, masks, camera_params):
"""Inpaint using 3D scene understanding"""
# Reconstruct 3D scene from video
scene_representation = self.nerf_model.reconstruct_scene(
video, camera_params
)
# Identify missing 3D regions
missing_3d_regions = self.project_masks_to_3d(
masks, camera_params, scene_representation
)
# Complete 3D geometry
completed_scene = self.complete_3d_geometry(
scene_representation, missing_3d_regions
)
# Render completed views
rendered_frames = []
for i, cam_param in enumerate(camera_params):
rendered = self.nerf_model.render_view(completed_scene, cam_param)
rendered_frames.append(rendered)
# Blend with original frames
final_frames = self.blend_rendered_with_original(
video, rendered_frames, masks
)
return final_framesclass InteractiveVideoInpainting:
def __init__(self):
self.inpainting_model = VideoInpaintingPipeline()
self.user_interface = UserInterface()
def interactive_inpaint(self, video_path):
"""Interactive video inpainting with user guidance"""
frames = self.load_video(video_path)
# User draws initial mask
initial_mask = self.user_interface.draw_mask(frames[0])
# Propagate mask with user refinement
masks = self.propagate_mask_interactive(frames, initial_mask)
# Preview inpainting
preview = self.quick_preview(frames[:30], masks[:30])
# User provides guidance
guidance = self.user_interface.get_inpainting_guidance(preview)
# Apply guided inpainting
if guidance['type'] == 'reference':
# User provides reference frames
result = self.inpainting_model.inpaint_with_reference(
frames, masks, guidance['reference_frames']
)
elif guidance['type'] == 'style':
# User specifies style
result = self.style_guided_inpainting(
frames, masks, guidance['style']
)
else:
# Automatic inpainting
result = self.inpainting_model.inpaint_video(frames, masks)
return resultclass RealtimeVideoInpainting:
def __init__(self):
self.fast_model = self.load_optimized_model()
self.frame_buffer = deque(maxlen=5)
def stream_inpaint(self, video_stream, mask_stream):
"""Real-time video inpainting for streaming"""
for frame, mask in zip(video_stream, mask_stream):
# Add to buffer
self.frame_buffer.append((frame, mask))
if len(self.frame_buffer) >= 3:
# Inpaint center frame using context
center_idx = len(self.frame_buffer) // 2
# Fast inpainting
inpainted = self.fast_inpaint(
self.frame_buffer[center_idx][0],
self.frame_buffer[center_idx][1],
context_frames=[f[0] for f in self.frame_buffer if f != self.frame_buffer[center_idx]]
)
yield inpainted
else:
# Not enough context yet
yield frame
def fast_inpaint(self, frame, mask, context_frames):
"""Optimized inpainting for real-time performance"""
# Reduce resolution if needed
scale = 0.5 if frame.shape[0] > 720 else 1.0
if scale < 1:
small_frame = cv2.resize(frame, None, fx=scale, fy=scale)
small_mask = cv2.resize(mask, None, fx=scale, fy=scale)
small_context = [cv2.resize(f, None, fx=scale, fy=scale)
for f in context_frames]
else:
small_frame = frame
small_mask = mask
small_context = context_frames
# Fast inference
with torch.no_grad():
inpainted = self.fast_model(
torch.from_numpy(small_frame).unsqueeze(0),
torch.from_numpy(small_mask).unsqueeze(0),
torch.stack([torch.from_numpy(f) for f in small_context])
)
# Upscale if needed
if scale < 1:
inpainted = cv2.resize(
inpainted.squeeze().numpy(),
(frame.shape[1], frame.shape[0])
)
else:
inpainted = inpainted.squeeze().numpy()
return inpaintedVideo inpainting represents a complex challenge in computer vision, requiring sophisticated techniques to handle temporal consistency, motion coherence, and visual quality. The field has evolved from simple propagation methods to advanced deep learning approaches that leverage optical flow, 3D understanding, and transformer architectures.
Key takeaways:
- Temporal consistency is crucial for convincing video inpainting
- Flow-guided methods provide strong motion coherence
- Deep learning approaches enable complex pattern understanding
- Multi-scale and attention mechanisms improve quality
- Future directions include 3D understanding and real-time processing
As computational resources improve and new architectures emerge, video inpainting will continue to advance, enabling applications from content creation to video restoration and privacy protection.
Originally from umitkacar/awesome-video-inpainting repository