IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spaces for image generation. However, their reconstruction quality often remains suboptimal, largely because deep VFM representations do not preserve sufficient fine-grained visual detail. This limitation becomes even more severe after discretization, where missing low-level information is difficult to recover. In fact, we observe that shallow VFM features retain considerably richer local appearance and structural detail, which complements the high-level semantics carried by deep features used in existing RAEs. Moti...
| Authors | Yitong Chen, Zijie Diao, Junke Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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