Latent Denoising Makes Good Visual Tokenizers
Authors: name: Jiawei Yang, name: Tianhong Li, name: Lijie Fan, name: Yonglong Tian, name: Yue Wang

Despite their fundamental role, it remains unclear what properties could make
visual tokenizers more effective for generative modeling. We observe that
modern generative models share a conceptually similar training objective --
reconstructing clean signals from corrupted inputs such as Gaussian noise or
masking -- a process we term denoising. Motivated by this insight, we propose
aligning tokenizer embeddings directly with the downstream denoising objective,
encouraging latent embeddings to be more easily reconstructed even when heavily
corrupted. To achieve this, we introduce the Latent Denoising Tokenizer
(l-DeTok), a simple yet effective tokenizer trained to reconstruct clean images
from latent embeddings corrupted by interpolative noise and random masking.
Extensive experiments on ImageNet 256x256 demonstrate that our tokenizer
consistently outperforms standard tokenizers across six representative
generative models. Our findings highlight denoising as a fundamental design
principle for tokenizer development, and we hope it could motivate new
perspectives for future tokenizer design.