VGGA: Geometry-Grounded Autoencoder for Video Generation

Chaofan Gan, Yian Zhao, Shijie Li, Tieyuan Chen, Zicheng Zhao +2

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 explicitly incorporates geometric constraints into the reconstruction process. Specifically, VGGA grounds the learned representations along three complementary geometric dimensions: \emph{Structural Depth}, \emph{Motion Correspondence}, and \emph{Projective Warping}. These objectives are unified within a Video Geometry Decoder, which employs a shared spatio-temporal upsampling trunk to jointly optimize geometric learning and RGB reconstruction. Through these geometry-grounded objectives, VGGA…