Large Motion Video Autoencoding with Cross-modal Video VAE (2412.17805v1)

Abstract: Learning a robust video Variational Autoencoder (VAE) is essential for reducing video redundancy and facilitating efficient video generation. Directly applying image VAEs to individual frames in isolation can result in temporal inconsistencies and suboptimal compression rates due to a lack of temporal compression. Existing Video VAEs have begun to address temporal compression; however, they often suffer from inadequate reconstruction performance. In this paper, we present a novel and powerful video autoencoder capable of high-fidelity video encoding. First, we observe that entangling spatial and temporal compression by merely extending the image VAE to a 3D VAE can introduce motion blur and detail distortion artifacts. Thus, we propose temporal-aware spatial compression to better encode and decode the spatial information. Additionally, we integrate a lightweight motion compression model for further temporal compression. Second, we propose to leverage the textual information inherent in text-to-video datasets and incorporate text guidance into our model. This significantly enhances reconstruction quality, particularly in terms of detail preservation and temporal stability. Third, we further improve the versatility of our model through joint training on both images and videos, which not only enhances reconstruction quality but also enables the model to perform both image and video autoencoding. Extensive evaluations against strong recent baselines demonstrate the superior performance of our method. The project website can be found at~\href{https://yzxing87.github.io/vae/}{https://yzxing87.github.io/vae/}.

• The paper introduces a cross-modal VAE architecture that integrates temporal-aware spatial compression with text guidance to reduce motion blur and preserve details.
• It demonstrates significant improvements in reconstruction quality, as evidenced by enhanced PSNR and SSIM metrics through joint image-video training.
• The work offers a modular framework that decouples spatial and temporal compression, enabling applications in real-time video streaming and dynamic content creation.

Analysis of "Large Motion Video Autoencoding with Cross-modal Video VAE"

This paper introduces an advanced architecture for video Variational Autoencoders (VAEs) that aims to address specific technical challenges in the domain of video compression and generation, particularly focusing on large-motion scenarios. The proposed methodology integrates a cross-modal Video VAE to enhance the spatial and temporal fidelity of video reconstruction, while also exploring text-driven guidance to improve encoding and decoding processes. It proposes strategic improvements over existing frameworks by overcoming the limitations of temporal inconsistency and reconstruction performance, prevalent in current video VAEs.

Key Contributions

The research outlines several innovative approaches:

1. Temporal-Aware Spatial Compression: The authors challenge the prevalent method of extending image VAEs to 3D VAEs for video encoding, which often leads to motion blur and detail distortion. They propose a novel stepwise approach that involves temporal-aware spatial compression coupled with a succinct motion compression model. This decouples spatial and temporal compression, ensuring improved motion consistency and detail retention.
2. Cross-modal Guidance with Textual Information: The study introduces a unique angle by integrating textual information from text-to-video datasets into the VAE framework. This cross-modal approach leverages text as a tool for guiding video encoding, significantly boosting detail preservation and temporal consistency in the resulting videos.
3. Joint Image-Video Training: The versatility of the model is further enhanced through joint training on both images and videos. By imparting the ability to process both media types, the model can efficiently perform autoencoding tasks across domains, thus maximizing efficiency and reconstruction quality.
4. Modular Framework for Spatiotemporal Modeling: The authors propose a new architectural design by examining simultaneous vs. sequential spatiotemporal compression strategies. Their solution that combines these approaches provides better video reconstructions, especially in scenarios with significant motion like sports.

Numerical Results and Claims

The paper offers an extensive evaluation against recent strong baselines, with significant performance improvements. Specifically, it demonstrates superior PSNR and SSIM metrics in various test scenarios, thus showcasing its enhanced capability in handling large-scale motion in videos. The cross-modal component, using textual information, shows measurable improvements in both detail accuracy and temporal coherence. The proposed model outperforms existing methods, setting new benchmarks in video VAE reconstruction quality.

Implications and Future Work

The implications of this paper are significant in both theoretical and practical scopes. Theoretically, the decomposition of spatiotemporal compression could guide future models to separate complex multi-dimensional features more effectively. Practically, the integration of text as a modality suggests new ways to enhance video generation systems, potentially improving applications in multimedia editing, content creation, and more.

Looking forward, the model's ability to manage large motion efficiently opens up prospects for its application in real-time video streaming and dynamic videographic applications. The exploration of additional modalities, beyond text, could further refine the efficacy of the VAE system. Moreover, the architecture could be scaled or adapted to accommodate higher-dimensional data inputs, such as volumetric video, thereby broadening its utility across various AI-driven fields.

In conclusion, this paper presents a technically robust and versatile approach to video autoencoding, which will likely stimulate further research and development in cross-modal AI frameworks and advanced video processing techniques.