Conceitos essenciais
Optical flow guidance enhances video inpainting quality in FGT++.
Resumo
The content discusses the challenges of video inpainting, introduces the Flow-Guided Transformer (FGT) and its limitations, and proposes an enhanced version, FGT++, with improved features like flow completion network and flow-guided feature integration. The article details the architecture of FGT++, including Temporally Deformable MHSA and Dual Perspective MHSA. Experimental results show superior performance compared to existing methods.
Introduction
Video inpainting aims to fill corrupted regions in videos.
Transformers are used for video inpainting due to their spatiotemporal modeling ability.
Flow Completion Network
Local aggregation improves flow completion accuracy.
Edge loss sharpens motion boundaries in completed flows.
Flow-Guided Feature Propagation
FGFP module propagates features based on completed flows.
Deformable convolution refines motion trajectories.
Flow-Guided Transformer Architecture
Temporally deformable MHSA refines attention retrieval.
Dual perspective MHSA combines local and global tokens.
Loss Function
Reconstruction loss, amplitude loss, and adversarial loss are used for training supervision.
Experiments
Evaluation on Youtube-VOS and DAVIS datasets shows FGT++ outperforms existing methods quantitatively.
Results
Qualitative comparisons demonstrate superior visual quality of FGT++ over other baselines under various mask settings.
Estatísticas
Transformers have been integrated into various computer vision tasks [29], [30], [31].
Optical flows play a crucial role in guiding attention retrieval in video inpainting [25].
Citações
"The completed optical flows serve as a strong indicator for spatiotemporal coherence."
"FGT++ demonstrates superior performance qualitatively and quantitatively."