Vision Foundation Models as Effective Visual Tokenizers for
  Autoregressive Image Generation
Authors: name: Anlin Zheng, name: Xin Wen, name: Xuanyang Zhang, name: Chuofan Ma, name: Tiancai Wang, name: Gang Yu, name: Xiangyu Zhang, name: Xiaojuan Qi

Leveraging the powerful representations of pre-trained vision foundation
models -- traditionally used for visual comprehension -- we explore a novel
direction: building an image tokenizer directly atop such models, a largely
underexplored area. Specifically, we employ a frozen vision foundation model as
the encoder of our tokenizer. To enhance its effectiveness, we introduce two
key components: (1) a region-adaptive quantization framework that reduces
redundancy in the pre-trained features on regular 2D grids, and (2) a semantic
reconstruction objective that aligns the tokenizer's outputs with the
foundation model's representations to preserve semantic fidelity. Based on
these designs, our proposed image tokenizer, VFMTok, achieves substantial
improvements in image reconstruction and generation quality, while also
enhancing token efficiency. It further boosts autoregressive (AR) generation --
achieving a gFID of 2.07 on ImageNet benchmarks, while accelerating model
convergence by three times, and enabling high-fidelity class-conditional
synthesis without the need for classifier-free guidance (CFG). The code will be
released publicly to benefit the community.