In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. For high-dimensional visual data, it reduces redundancy and emphasizes key features for high-quality generation. Current visual tokenization methods rely on a traditional autoencoder framework, where the encoder compresses data into latent representations, and the decoder reconstructs the original input. In this work, we offer a new perspective by proposing denoising as decoding, shifting from single-step reconstruction to iterative refinement. Specifically, we replace the decoder with a diffusion process that iteratively refines noise to recover the original image, guided by the latents provided by the encoder. We evaluate our approach by assessing both reconstruction (rFID) and generation quality (FID), comparing it to state-of-the-art autoencoding approach. We hope this work offers new insights into integrating iterative generation and autoencoding for improved compression and generation.

本研究解决了现有视觉tokenization方法中重建过程的单一性问题，提出将去噪视为解码的新视角。通过用扩散过程替换传统解码器，该方法迭代性地精炼噪声以恢复原始图像，从而提高了图像生成和压缩的效果。研究表明，该方法在重建和生成质量上优于当前最先进的自编码方法。

ε-VAE：去噪作为视觉解码