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arXiv · 2507.04559

MambaVideo for Discrete Video Tokenization with Channel-Split Quantization

Dawit Mureja Argaw·Xian Liu·Joon Son Chung·Ming-Yu Liu·Fitsum Reda
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Abstract

Discrete video tokenization is essential for efficient autoregressive generative modeling due to the high dimensionality of video data. This work introduces a state-of-the-art discrete video tokenizer with two key contributions. First, we propose a novel Mamba-based encoder-decoder architecture that overcomes the limitations of previous sequencebased tokenizers. Second, we introduce a new quantization scheme, channel-split quantization, which significantly enhances the representational power of quantized latents while preserving the token count. Our model sets a new state-of-the-art, outperforming both causal 3D convolutionbased and Transformer-based approaches across multiple datasets. Experimental results further demonstrate its robustness as a tokenizer for autoregressive video generation.

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Dawit Mureja Argaw, Xian Liu, Joon Son Chung, Ming-Yu Liu, Fitsum Reda. 2025-07-06. MambaVideo for Discrete Video Tokenization with Channel-Split Quantization. https://arxiv.org/abs/2507.04559

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