Learnable Gated Temporal Shift Module for Deep Video Inpainting (1907.01131v2)

Abstract: How to efficiently utilize temporal information to recover videos in a consistent way is the main issue for video inpainting problems. Conventional 2D CNNs have achieved good performance on image inpainting but often lead to temporally inconsistent results where frames will flicker when applied to videos (see https://www.youtube.com/watch?v=87Vh1HDBjD0&list=PLPoVtv-xp_dL5uckIzz1PKwNjg1yI0I94&index=1); 3D CNNs can capture temporal information but are computationally intensive and hard to train. In this paper, we present a novel component termed Learnable Gated Temporal Shift Module (LGTSM) for video inpainting models that could effectively tackle arbitrary video masks without additional parameters from 3D convolutions. LGTSM is designed to let 2D convolutions make use of neighboring frames more efficiently, which is crucial for video inpainting. Specifically, in each layer, LGTSM learns to shift some channels to its temporal neighbors so that 2D convolutions could be enhanced to handle temporal information. Meanwhile, a gated convolution is applied to the layer to identify the masked areas that are poisoning for conventional convolutions. On the FaceForensics and Free-form Video Inpainting (FVI) dataset, our model achieves state-of-the-art results with simply 33% of parameters and inference time.

• The paper introduces a Learnable Gated Temporal Shift Module (LGTSM) that empowers 2D CNNs to effectively use temporal context in video inpainting.
• A gating mechanism distinguishes masked, inpainted, and unmasked regions to prevent errors and enhance inpainting quality.
• The method achieves a 67% reduction in model size and inference time while delivering state-of-the-art performance on FaceForensics and FVI datasets.

Learnable Gated Temporal Shift Module for Deep Video Inpainting

This paper discusses a novel approach to video inpainting, a task of filling arbitrary missing regions in a video by leveraging temporal information. The authors introduce a Learnable Gated Temporal Shift Module (LGTSM) designed to enhance 2D convolutional neural networks' (CNNs) ability to utilize temporal information without relying on computationally intensive 3D convolutions. This approach not only addresses the temporal inconsistencies often observed with 2D CNNs when applied to videos but also significantly reduces parameter and inference time requirements.

Key Contributions

1. Learnable Gated Temporal Shift Module (LGTSM): LGTSM is proposed to allow 2D convolutions to utilize temporal neighbors by shifting some feature channels to adjacent frames. This enables the model to handle videos more efficiently and effectively.
2. Gating Mechanism: A gated convolution is incorporated to distinguish between masked, inpainted, and unmasked areas to address potential poisoning caused by masks.
3. Efficient Design: The LGTSM approach achieves state-of-the-art results on datasets with a 67% reduction in model size and computational time compared to 3D convolution models.

Results

The proposed method was evaluated on the FaceForensics and Free-form Video Inpainting (FVI) datasets, showcasing leading performance in terms of perceptual metrics like LPIPS and FID. The quantitative results indicated that the LGTSM-powered model parallels existing state-of-the-art methods while leveraging only a fraction of the resources. Specifically, the reduction in parameters and inference time makes this approach highly feasible for real-time applications without sacrificing performance.

Implications and Future Directions

The results indicate that LGTSM serves as a viable alternative to 3D convolutions in contexts where computational resources are limited. The implications extend to various real-world applications, from video editing to damaged footage recovery, where efficient and effective inpainting is crucial.

Future research could explore enhancing the temporal modeling further, potentially incorporating adaptive learning strategies to deepen the understanding of temporal dynamics. Moreover, extending the model’s applicability to higher or variable-resolution videos could broaden its usage in professional video production environments.

Conclusion

The Learnable Gated Temporal Shift Module marks a significant step towards more resource-efficient video inpainting methodologies. By strategically leveraging temporal shifts within a gated framework, the approach provides a practical substitute for more resource-intensive methods, aligning with the needs of scalable and efficient video processing systems.

In summary, this paper demonstrates that through innovative architectural design, it is possible to achieve superior performance in video inpainting while significantly reducing computational demands, potentially paving the way for real-time video editing solutions and beyond.