ProPainter: Improving Propagation and Transformer for Video Inpainting (2309.03897v1)

Abstract: Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms in video inpainting (VI). Despite the effectiveness of these components, they still suffer from some limitations that affect their performance. Previous propagation-based approaches are performed separately either in the image or feature domain. Global image propagation isolated from learning may cause spatial misalignment due to inaccurate optical flow. Moreover, memory or computational constraints limit the temporal range of feature propagation and video Transformer, preventing exploration of correspondence information from distant frames. To address these issues, we propose an improved framework, called ProPainter, which involves enhanced ProPagation and an efficient Transformer. Specifically, we introduce dual-domain propagation that combines the advantages of image and feature warping, exploiting global correspondences reliably. We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens. With these components, ProPainter outperforms prior arts by a large margin of 1.46 dB in PSNR while maintaining appealing efficiency.

• The paper presents a dual-domain propagation framework that enhances spatial and temporal coherence in video inpainting.
• It introduces a mask-guided sparse Transformer that significantly reduces computational costs while maintaining high-quality inpainting.
• Experimental results on YouTube-VOS and DAVIS show a 1.46 dB PSNR gain, underscoring the method's practical efficiency.

ProPainter: Enhancing Video Inpainting through Improved Propagation and Transformer Efficiency

The paper under review, "ProPainter: Improving Propagation and Transformer for Video Inpainting," by Shangchen Zhou et al., focuses on advancing video inpainting (VI) techniques through an innovative framework known as ProPainter. This framework amalgamates dual-domain propagation mechanisms and a mask-guided sparse video Transformer to address the limitations of current VI methods. By integrating flow-guided propagation and Transformer approaches, ProPainter seeks to enhance spatial and temporal coherence in video inpainting tasks.

Video Inpainting Overview and Challenges

Video inpainting is a procedure designed to fill in missing or corrupted regions within video frames to produce spatially and temporally coherent content. It finds applications in video completion, object removal, and restoration tasks. The challenge lies in ensuring that the inpainting results are seamless and realistic, requiring accurate correspondence across multiple frames. Traditional methods, including 3D Convolutional Neural Networks (CNNs) and temporal learning models, face limitations due to constrained receptive fields and excessive computational costs.

Contributions of ProPainter

ProPainter addresses the existing shortcomings in video inpainting by proposing a dual-domain propagation framework alongside an efficient Transformer design. Its primary components include:

1. Dual-Domain Propagation (DDP): This involves both global image and local feature propagation, allowing for the aggregation of correspondence information across extensive temporal spans. Image propagation benefits from pre-completion through flow consistency checks to mitigate spatial misalignment errors, while feature propagation uses flow-guided deformable alignment to improve robustness against occlusions and flow inaccuracies.
2. Mask-Guided Sparse Video Transformer (MSVT): This novel Transformer design reduces the computational demands that typify traditional Transformers. By implementing masked-guided sparsity, the Transformer discards non-essential tokens and focuses attention only on critical regions, thus maintaining inpainting performance while optimizing efficiency.

Experimental Evaluation and Results

The paper demonstrates ProPainter's superior performance on datasets such as YouTube-VOS and DAVIS, achieving notable improvements over existing methods. ProPainter achieves a 1.46 dB PSNR gain, indicative of its enhanced capability to produce visually consistent and high-quality inpainting results. Its efficient Transformer design significantly reduces FLOPs and runtime, proving advantageous for handling high-resolution, long-duration videos without undue computational expense.

Theoretical and Practical Implications

The proposed framework underscores the potential of integrating dual-domain approaches to leverage the strengths of both image-based and feature-based propagation techniques. The use of GPU-accelerated propagation and sparse attentional mechanisms reflects a careful consideration of computational constraints, paving the way for efficient and scalable video inpainting solutions.

Future Directions

The innovations introduced in ProPainter open several pathways for future research. Continued refinement of Transformer architectures to further enhance their efficiency and the exploration of even more sophisticated propagation models promise improvements in the fidelity and scalability of video inpainting techniques. Moreover, expanding the types of masks and artifacts that can be robustly handled by these techniques could extend their applicability to broader real-world scenarios.

In conclusion, ProPainter represents a meaningful advancement in the field of video inpainting, providing a framework that balances computational efficiency with high-quality output. Its dual-domain propagation strategy and sparse Transformer approach offer insightful contributions that are poised to influence subsequent research and application in video enhancement and restoration tasks.