Deficiency-Aware Masked Transformer for Video Inpainting

Abstract: Recent video inpainting methods have made remarkable progress by utilizing explicit guidance, such as optical flow, to propagate cross-frame pixels. However, there are cases where cross-frame recurrence of the masked video is not available, resulting in a deficiency. In such situation, instead of borrowing pixels from other frames, the focus of the model shifts towards addressing the inverse problem. In this paper, we introduce a dual-modality-compatible inpainting framework called Deficiency-aware Masked Transformer (DMT), which offers three key advantages. Firstly, we pretrain a image inpainting model DMT_img serve as a prior for distilling the video model DMT_vid, thereby benefiting the hallucination of deficiency cases. Secondly, the self-attention module selectively incorporates spatiotemporal tokens to accelerate inference and remove noise signals. Thirdly, a simple yet effective Receptive Field Contextualizer is integrated into DMT, further improving performance. Extensive experiments conducted on YouTube-VOS and DAVIS datasets demonstrate that DMT_vid significantly outperforms previous solutions. The code and video demonstrations can be found at github.com/yeates/DMT.

• The paper introduces a deficiency-aware framework that leverages an image inpainting model to address missing cross-frame content in videos.
• It employs a masked transformer with a selective token mechanism and a receptive field contextualizer to enhance efficiency and reduce noise.
• Experimental results on DAVIS and YouTube-VOS demonstrate significant gains in PSNR, SSIM, and VFID, surpassing state-of-the-art methods.

Deficiency-Aware Masked Transformer for Video Inpainting: A Review

Overview

The paper introduces a novel framework termed Deficiency-aware Masked Transformer (DMT) designed to tackle video inpainting challenges, particularly those arising from deficient scenarios where cross-frame recurrence is limited. The proposed model distinguishes itself from traditional methods by integrating dual-modality compatibility to pre-train an image inpainting model, which subsequently enhances video inpainting. This approach leverages the generative power of image inpainting models to address cases where masked video content is absent across all frames.

Methodology

The DMT framework consists of several innovative components that enhance its performance over previous models:

• Pre-training and Knowledge Transfer: The authors pre-train an image inpainting model, DMT$_\text{img}$, which then serves as a prior for distilling knowledge to the video model, DMT$_\text{vid}$. This transfer aims to exploit the hallucination capabilities of image inpainting to compensate for deficiencies in video frames.
• Masked Transformer and Token Selection Mechanism: Key to the DMT architecture is a masked transformer framework that selectively incorporates spatiotemporal tokens. The token selection mechanism ensures that only relevant tokens contribute to inference, improving computational efficiency and reducing noise.
• Receptive Field Contextualizer (RFC): The RFC component enhances the learning of high-frequency signals by extending the receptive field of the model, pairing the benefits of transformers and convolutional networks.

Experimental Results

The paper reports extensive experiments on the YouTube-VOS and DAVIS datasets, demonstrating that DMT$_\text{vid}$ surpasses state-of-the-art methods in terms of PSNR, SSIM, and VFID metrics. Notably, it improves upon the best prior method by 0.81 dB on DAVIS and 0.56 dB on YouTube-VOS. Qualitative evaluations further reinforce the model's efficacy in maintaining spatiotemporal coherence and generating high-quality inpainted frames.

Notable Contributions

The authors present several significant contributions and insights:

1. Cross-Modality Knowledge Distillation: The innovative use of an image inpainting model to inform video inpainting is a central contribution, addressing the deficiency problem by capitalizing on expertise from a related domain.
2. Dynamic Mask Activation and Efficiency Gains: Through the selective token mechanism, the model not only accelerates inference but also minimizes the likelihood of propagating irrelevant information.
3. Practical Application Enhancements: The proposed model exhibits flexibility in adapting to one-shot object removal scenarios. This highlights its robustness in applying to real-world applications needing minimal input from users, such as text-based or stroke-based guidance.

Implications and Future Directions

The integration of image and video inpainting techniques presents an exciting direction for future research, where models could potentially learn from large-scale image datasets to optimize video processing tasks. Enhancements in masked transformers and context-aware learning modules like the RFC also open avenues for exploring better efficiency and accuracy in various computer vision applications.

Future research might explore minimizing the computational overhead further and exploring the scalability of such frameworks to high-resolution video data. Additionally, the framework's adaptability to unseen domains and sparse annotation scenarios presents an opportunity to widen its application beyond the current datasets.

Overall, the DMT framework advances the capabilities of video inpainting, particularly in scenarios challenged by masking deficiencies, and sets a foundation for subsequent innovations in leveraging cross-modality synergies.