EVATok：面向高效视觉自回归生成的自适应长度视频分词器
EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation
提出自适应视频分词器，根据视频内容动态分配词元数量，在保持重建质量的同时显著降低下游自回归生成的计算成本，为视频生成模型的高效化提供了可复用的分词与路由机制。
原文依据 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
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自回归视频生成模型依赖于视频分词器，将像素压缩为离散词元序列。这些词元序列的长度对于平衡重建质量与下游生成的计算成本至关重要。传统视频分词器对不同视频的时序块采用统一的词元分配，往往在简单、静态或重复的片段上浪费词元，而在动态或复杂的片段上分配不足。为了解决这种低效问题，我们提出了EVATok，一个生成高效视频自适应分词器的框架。我们的框架为每个视频估计最优词元分配，以实现最佳的质量-成本权衡，开发轻量级路由器来快速预测这些最优分配，并训练自适应分词器，根据路由器预测的分配对视频进行编码。我们证明，EVATok在视频重建和下游自回归生成方面显著提升了效率和整体质量。借助集成了视频语义编码器的先进训练方案，EVATok在UCF-101上实现了更优的重建和最优的类别到视频生成，与先前最优的LARP以及我们的固定长度基线相比，平均词元使用量至少节省24.4%。
英文原文摘要 1340 字符
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 **E**fficient **V**ideo **A**daptive **Tok**enizers. 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.