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arXiv · 2512.16975

InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression

Haotian Ye·Qiyuan He·Jiaqi Han·Puheng Li·Jiaojiao Fan·Zekun Hao·Fitsum Reda·Yogesh Balaji·Huayu Chen·Sheng Liu·Angela Yao·James Zou·Stefano Ermon·Haoxiang Wang·Ming-Yu Liu
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Abstract

Accurate and efficient discrete video tokenization is essential for long video sequences processing. Yet, the inherent complexity and variable information density of videos present a significant bottleneck for current tokenizers, which rigidly compress all content at a fixed rate, leading to redundancy or information loss. Drawing inspiration from Shannon's information theory, this paper introduces InfoTok, a principled framework for adaptive video tokenization. We rigorously prove that existing data-agnostic training methods are suboptimal in representation length, and present a novel evidence lower bound (ELBO)-based algorithm that approaches theoretical optimality. Leveraging this framework, we develop a transformer-based adaptive compressor that enables adaptive tokenization. Empirical results demonstrate state-of-the-art compression performance, saving 20% tokens without influence on performance, and achieving 2.3x compression rates while still outperforming prior heuristic adaptive approaches. By allocating tokens according to informational richness, InfoTok enables a more compressed yet accurate tokenization for video representation, offering valuable insights for future research.

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Haotian Ye, Qiyuan He, Jiaqi Han, Puheng Li, Jiaojiao Fan, Zekun Hao, Fitsum Reda, Yogesh Balaji, Huayu Chen, Sheng Liu, Angela Yao, James Zou, Stefano Ermon, Haoxiang Wang, Ming-Yu Liu. 2025-12-18. InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression. https://arxiv.org/abs/2512.16975

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