VTok: A Unified Video Tokenizer with Decoupled Spatial-Temporal Latents

• URL: http://arxiv.org/abs/2602.04202v1
• Date: Wed, 04 Feb 2026 04:39:46 GMT
• Title: VTok: A Unified Video Tokenizer with Decoupled Spatial-Temporal Latents
• Authors: Feng Wang, Yichun Shi, Ceyuan Yang, Qiushan Guo, Jingxiang Sun, Alan Yuille, Peng Wang,
• Abstract summary: This work presents VTok, a unified video tokenization framework that can be used for both generation and understanding tasks.<n>We propose to decouple the spatial and temporal representations of videos by retaining the spatial features of a single key frame while encoding each subsequent frame into a single residual token.<n>Our experiments suggest that VTok effectively reduces the complexity of video representation from the product of frame count and per-frame token count to their sum.
• Score: 33.80068883432077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents VTok, a unified video tokenization framework that can be used for both generation and understanding tasks. Unlike the leading vision-language systems that tokenize videos through a naive frame-sampling strategy, we propose to decouple the spatial and temporal representations of videos by retaining the spatial features of a single key frame while encoding each subsequent frame into a single residual token, achieving compact yet expressive video tokenization. Our experiments suggest that VTok effectively reduces the complexity of video representation from the product of frame count and per-frame token count to their sum, while the residual tokens sufficiently capture viewpoint and motion changes relative to the key frame. Extensive evaluations demonstrate the efficacy and efficiency of VTok: it achieves notably higher performance on a range of video understanding and text-to-video generation benchmarks compared with baselines using naive tokenization, all with shorter token sequences per video (e.g., 3.4% higher accuracy on our TV-Align benchmark and 1.9% higher VBench score). Remarkably, VTok produces more coherent motion and stronger guidance following in text-to-video generation, owing to its more consistent temporal encoding. We hope VTok can serve as a standardized video tokenization paradigm for future research in video understanding and generation.

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