VidTwin: Video VAE with Decoupled Structure and Dynamics (2412.17726v2)

Abstract: Recent advancements in video autoencoders (Video AEs) have significantly improved the quality and efficiency of video generation. In this paper, we propose a novel and compact video autoencoder, VidTwin, that decouples video into two distinct latent spaces: Structure latent vectors, which capture overall content and global movement, and Dynamics latent vectors, which represent fine-grained details and rapid movements. Specifically, our approach leverages an Encoder-Decoder backbone, augmented with two submodules for extracting these latent spaces, respectively. The first submodule employs a Q-Former to extract low-frequency motion trends, followed by downsampling blocks to remove redundant content details. The second averages the latent vectors along the spatial dimension to capture rapid motion. Extensive experiments show that VidTwin achieves a high compression rate of 0.20% with high reconstruction quality (PSNR of 28.14 on the MCL-JCV dataset), and performs efficiently and effectively in downstream generative tasks. Moreover, our model demonstrates explainability and scalability, paving the way for future research in video latent representation and generation. Check our project page for more details: https://vidtwin.github.io/.

• The paper introduces VidTwin, a video autoencoder that decouples video content into separate structure and dynamics latent spaces for interpretable representations.
• It employs a Spatial-Temporal Transformer along with a Q-Former to capture low-frequency structural trends and fine-grained dynamic details efficiently.
• Experimental results demonstrate high compression (0.20%) and robust reconstruction quality (PSNR 28.14) on the MCL-JCV dataset, highlighting its scalability and practical utility.

VidTwin: Video VAE with Decoupled Structure and Dynamics

The paper under review, titled "VidTwin: Video VAE with Decoupled Structure and Dynamics," introduces a novel approach to the field of video autoencoders. The authors propose VidTwin, an efficient video autoencoder specifically designed to decouple video content into two separate latent spaces: Structure Latent vectors and Dynamics Latent vectors. This paper provides a detailed analysis of the architecture and methodology behind VidTwin, presenting a significant contribution to video representation learning.

Methodological Overview

VidTwin employs an Encoder-Decoder architecture, leveraging a Spatial-Temporal Transformer to extract meaningful video representations. The primary innovation lies in its ability to split video information into two latent spaces:

1. Structure Latent Vectors: These vectors capture the overall content and global movements within the video. The approach utilizes a Q-Former to extract low-frequency motion trends from temporal dimensions and employs downsampling techniques to remove redundant details, focusing on preserving essential structural content.
2. Dynamics Latent Vectors: These vectors are tasked with representing fine-grained details and dynamic changes. This component averages latent vectors across spatial dimensions, effectively capturing rapid motion while maintaining computational efficiency.

The process results in two distinct latent spaces that collectively reconstruct high-quality video frames, each serving a unique role in video representation.

Results and Implications

The experimental results demonstrate VidTwin's capability to achieve a high compression rate of 0.20% while maintaining impressive reconstruction quality, with a PSNR of 28.14 on the MCL-JCV dataset. The model shows superior performance in downstream generative tasks, indicating its robustness and flexibility.

The authors stress the implications of VidTwin's architecture in several domains:

• Explainability: By decoupling structure and dynamics, VidTwin allows for an interpretable model wherein each latent vector serves a distinct purpose. This enhances the understanding of how the model processes video data and aids in debugging and improving the model.
• Scalability: The architecture showcases scalability through its adaptable latent space, potentially serving various video analysis tasks beyond reconstruction and generation.
• Efficiency: By achieving high compression rates, VidTwin mitigates memory and computational burdens, offering a more efficient approach to handling extensive video data.

Theoretical and Practical Implications

The decoupling of latent spaces opens new avenues for theoretical research in video representation learning. The approach could inspire the development of more sophisticated video autoencoders that further disentangle video components, leading to richer and more descriptive representations.

Practically, VidTwin offers significant benefits for applications in video compression, transmission, and real-time video analysis. Its scalable and explainable nature makes it particularly suited for environments where computational resources are limited, yet high-quality video output is essential.

Future Directions

Moving forward, the research community might explore the potential integration of VidTwin with other modalities, enhancing its application in areas such as multi-modal learning and video-text understanding. Furthermore, fine-tuning the model for specific applications like video conferencing or streaming could lead to tailored solutions that fully leverage the benefits of structured and dynamic decoupling.

In conclusion, VidTwin presents a compelling advancement in video VAE architecture, offering both theoretical insights and practical efficiencies. Its introduction marks a valuable addition to the toolkit of researchers and practitioners working on video data.