MoVieS synthesizes 4D dynamic novel views from monocular videos using Gaussian primitives, enabling unified modeling of appearance, geometry, and motion with minimal task-specific supervision.
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 using pixel-aligned grids of Gaussian primitives, explicitly supervising
their time-varying motion. This allows, for the first time, the unified
modeling of appearance, geometry and motion, and enables view synthesis,
reconstruction and 3D point tracking within a single learning-based framework.
By bridging novel view synthesis with dynamic 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.