EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation
Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce EVATok, a framework to produce Efficient Video Adaptive Tokenizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state-of-the-art class-to-video generation on UCF-101, with at least 24.4% savings in average token usage compared to the prior state-of-the-art LARP and our fixed-length baseline.
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Tianwei Xiong
claimed_verified
2026-03-13T08:12:30.557Z
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Tianwei Xiong
YuuTennYi
user
YuuTennYi
69b37d8a2ad6c5d4cf486abc
Jun Hao Liew
admin_assigned
2026-03-13T09:02:59.143Z
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Jun Hao Liew
junhao910323
user
junhao910323
69b37d8a2ad6c5d4cf486abd
Zilong Huang
69b37d8a2ad6c5d4cf486abe
Zhijie Lin
69b37d8a2ad6c5d4cf486abf
Jiashi Feng
admin_assigned
2026-03-13T09:03:20.299Z
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Jiashi Feng
jshfeng
user
jshfeng
69b37d8a2ad6c5d4cf486ac0
Xihui Liu
admin_assigned
2026-03-13T09:03:26.074Z
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Xihui Liu
XihuiLiu
user
XihuiLiu
EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation
Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce EVATok, a framework to produce Efficient Video Adaptive Tokenizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state-of-the-art class-to-video generation on UCF-101, with at least 24.4% savings in average token usage compared to the prior state-of-the-art LARP and our fixed-length baseline.
EVATok is a framework for efficient video tokenization that adapts token assignment based on video content, improving reconstruction quality and generation efficiency through learned routers and adaptive encoding.
autoregressive video generative models video tokenizers discrete token sequences temporal blocks uniform token assignment video semantic encoders lightweight routers adaptive tokenizers video reconstruction class-to-video generation