Efficient Long Video Tokenization via Coordinated-based Patch
  Reconstruction
Authors: name: Huiwon Jang, name: Sihyun Yu, name: Jinwoo Shin, name: Pieter Abbeel, name: Younggyo Seo

Efficient tokenization of videos remains a challenge in training vision
models that can process long videos. One promising direction is to develop a
tokenizer that can encode long video clips, as it would enable the tokenizer to
leverage the temporal coherence of videos better for tokenization. However,
training existing tokenizers on long videos often incurs a huge training cost
as they are trained to reconstruct all the frames at once. In this paper, we
introduce CoordTok, a video tokenizer that learns a mapping from
coordinate-based representations to the corresponding patches of input videos,
inspired by recent advances in 3D generative models. In particular, CoordTok
encodes a video into factorized triplane representations and reconstructs
patches that correspond to randomly sampled (x,y,t) coordinates. This allows
for training large tokenizer models directly on long videos without requiring
excessive training resources. Our experiments show that CoordTok can
drastically reduce the number of tokens for encoding long video clips. For
instance, CoordTok can encode a 128-frame video with 128times128 resolution
into 1280 tokens, while baselines need 6144 or 8192 tokens to achieve similar
reconstruction quality. We further show that this efficient video tokenization
enables memory-efficient training of a diffusion transformer that can generate
128 frames at once.