最新模型
V-RAE Uses Frozen Visual Representations as Video Latents, Converging Up to 6× Faster in K600 Generation Training
Instead of training a video VAE solely for pixel reconstruction, V-RAE adds temporal compression and a decoder on top of semantic features from models such as DINOv3, SigLIP2, and V-JEPA 2.1. The authors found that the new latents improve both generation and future-frame prediction, though they have so far demonstrated only low-resolution, short-clip, class-conditional generation.