# VideoFlexTok：灵活长度的粗到细视频Token化方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-14 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnzj0j7u03oqsl0fyo9kfc21
- 原文链接：https://arxiv.org/abs/2604.12887

## AI 摘要

VideoFlexTok提出可变长度粗到细视频token化方法，早期token捕获语义与运动等抽象信息，后期逐步添加细节。相比传统3D网格表示，该方法在保持相当生成质量（gFVD/ViCLIP）的同时，将模型规模从5.2B压缩至1.1B，实现5倍效率提升。其生成流解码器支持任意长度token重建，仅需672个token即可处理10秒81帧长视频，较同类方法减少8倍token用量，显著降低长视频生成成本。

## 正文

Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what information is preserved and how it is organized. A de facto standard approach to video tokenization is to represent a video as a spatiotemporal 3D grid of tokens, each capturing the corresponding local information in the original signal. This requires the downstream model that consumes the tokens, e.g., a text-to-video model, to learn to predict all low-level details "pixel-by-pixel" irrespective of the video's inherent complexity, leading to high learning complexity. We present VideoFlexTok, which represents videos with a variable-length sequence of tokens structured in a coarse-to-fine manner -- where the first tokens (emergently) capture abstract information, such as semantics and motion, and later tokens add fine-grained details. The generative flow decoder enables realistic video reconstructions from any token count. This representation structure allows adapting the token count according to downstream needs and encoding videos longer than the baselines with the same budget. We evaluate VideoFlexTok on class- and text-to-video generative tasks and show that it leads to more efficient training compared to 3D grid tokens, e.g., achieving comparable generation quality (gFVD and ViCLIP Score) with a 5x smaller model (1.1B vs 5.2B). Finally, we demonstrate how VideoFlexTok can enable long video generation without prohibitive computational cost by training a text-to-video model on 10-second 81-frame videos with only 672 tokens, 8x fewer than a comparable 3D grid tokenizer.