MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE

We introduce MotionCrafter, the first video diffusion-based framework that jointly reconstructs 4D geometry and estimates dense motion from a monocular video. To represent them effectively in latent space, we propose a 4D VAE that encodes point maps and scene flows as a unified latent compatible with pretrained video generators. Unlike prior work that forces the 3D value and latents to align strictly with RGB VAE latents—despite their fundamentally different distributions—we show that such alignment is unnecessary and leads to suboptimal performance. Instead, we introduce a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments across multiple datasets demonstrate that MotionCrafter achieves state-of-the-art performance in joint 4D geometry reconstruction and dense scene flow estimation, delivering 38.64\% and 25.0\% improvements in geometry and motion reconstruction, respectively, all without any post-optimization.