MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second
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.
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Chenguo Lin
claimed_verified
2025-07-15T19:08:25.498Z
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Chenguo Lin
user
chenguolin
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Yuchen Lin
claimed_verified
2025-07-15T19:08:21.548Z
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Yuchen Lin
user
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Panwang Pan
claimed_verified
2025-07-15T19:08:23.426Z
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Panwang Pan
user
paulpanwang
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Yifan Yu
6875ed50257d4f04353705f5
Honglei Yan
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Katerina Fragkiadaki
6875ed50257d4f04353705f7
Yadong Mu
MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Second
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.
MoVieS synthesizes 4D dynamic novel views from monocular videos using pixel-aligned Gaussian primitives, enabling unified appearance, geometry, and motion modeling within a single framework.
feed-forward model Gaussian primitives view synthesis dynamic geometry reconstruction scene flow estimation moving object segmentation