Chronofy: Pose-Aware Generative 4D Reconstruction from Videos

Recent generative reconstruction methods effectively reconstruct static 3D content but often exhibit pronounced inter-frame jitter when applied to dynamic content. We present Chronofy, a pose-aware 4D generative reconstruction framework that jointly predicts complete dynamic objects and unified camera-space poses from single-view videos. Built upon SAM3D, Chronofy transfers its powerful generative reconstruction priors to the 4D domain through three key designs: (i) a pose-aware 4D representation that encodes frame-dependent motion in spatiotemporal structured latents and uses a unified pose for sequence-level placement; (ii) a generative reconstruction model that utilizes a spatiotemporal mixture of transformers for temporal modeling and frame-wise correspondence between shape and pose; and (iii) a dynamic Gaussian decoder that combines temporal latent interactions with time-varying Gaussian attributes to reduce inter-frame flickering. To preserve occlusion robustness with limited 4D training data, we use temporal masking augmentation, while layout test-time optimization refines multi-object spatial alignment. Experiments on Motion-80, Kubric-4D, and our simulated real-world Chrono-64 datasets demonstrate that Chronofy substantially improves spatiotemporal consistency in both 4D object generation and 4D scene reconstruction.