VGGA: Geometry-Grounded Autoencoder for Video Generation
Autoencoders are the core component of latent video generative models, compressing high-dimensional videos into compact latent spaces for downstream generation. However, existing video autoencoders are primarily optimized for pixel-level reconstruction, providing limited supervision for learning structured latent representations. We argue that videos should not be treated merely as signals to be reconstructed, but rather as 2D projections of dynamic 3D worlds that exhibit explicit and well-structured geometric properties. Building on this insight, we propose \textbf{V}ideo \textbf{G}eometry \textbf{G}rounded \textbf{A}utoencoder (\textbf{VGGA}), a geometry-aware video autoencoder that exp...
| Authors | Chaofan Gan, Yian Zhao, Shijie Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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