V-RAE: Rethinking Video Latent Spaces for Generation
Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization. A reconstruction-optimal latent space, however, need not be well suited to generative modeling. We propose V-RAE, a video representation autoencoder that builds compact generative latents on top of frozen vision foundation model representations. A lightweight temporal pooling module removes temporal redundancy while preserving semantic structure, and a video decoder reconstructs co...
| Authors | Minghui Guo, Shengqiong Wu, Hao Fei |
|---|---|
| Primary category | cs.CV |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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