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In this paper, we aim to model 3D scene dynamics from multi-view videos.Unlike the majority of existing works which usually focus on the common task ofnovel view synthesis within the training time period, we propose tosimultaneously learn the geometry, appearance, and physical velocity of 3Dscenes only from video frames, such that multiple desirable applications can besupported, including future frame extrapolation, unsupervised 3D semantic scenedecomposition, and dynamic motion transfer. Our method consists of three majorcomponents, 1) the keyframe dynamic radiance field, 2) the interframe velocityfield, and 3) a joint keyframe and interframe optimization module which is thecore of our framework to effectively train both networks. To validate ourmethod, we further introduce two dynamic 3D datasets: 1) Dynamic Objectdataset, and 2) Dynamic Indoor Scene dataset. We conduct extensive experimentson multiple datasets, demonstrating the superior performance of our method overall baselines, particularly in the critical tasks of future frame extrapolationand unsupervised 3D semantic scene decomposition.

NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos