MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second

Chenguo Lin, Yuchen Lin, Panwang Pan, Yifan Yu, Tao Hu, Honglei Yan, Katerina Fragkiadaki, Yadong MU

Keywords: 4D Reconstruction, Dynamic Novel View Synthesis, 3D Motion Estimation

TL;DR: We present MoVieS, the first feed-forward framework that jointly models appearance, geometry and motion for 4D scene perception from monocular videos.

Abstract: We present MoVieS, a novel feed-forward model that synthesizes 4D dynamic novel views from monocular videos in one second. MoVieS represents dynamic 3D scenes with pixel-aligned Gaussian primitives and explicitly supervises their time-varying motions. This allows, for the first time, the unified modeling of appearance, geometry and motion from monocular videos, and enables reconstruction, view synthesis and 3D point tracking within a single learning-based framework. By bridging view synthesis with geometry reconstruction, MoVieS enables large-scale training on diverse datasets with minimal dependence on task-specific supervision. As a result, it also naturally supports a wide range of zero-shot applications, such as scene flow estimation and moving object segmentation. Extensive experiments validate the effectiveness and efficiency of MoVieS across multiple tasks, achieving competitive performance while offering several orders of magnitude speedups. The code and pretrained model will be publicly available.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 3392