GUSH3R: Everyone Everywhere All at Once as Gaussians

• URL: http://arxiv.org/abs/2607.05243v1
• Date: Mon, 06 Jul 2026 15:53:09 GMT
• Title: GUSH3R: Everyone Everywhere All at Once as Gaussians
• Abstract summary: We present GUSH3R (Gaussian-Unified Scene Human 3D Reconstruction), a feed-forward framework for online dynamic human-scene reconstruction.<n>From a monocular human-scene video, our method reconstructs dynamic humans (everyone) and static scenes (everywhere) in a single forward pass.<n>Experiments on monocular human-scene datasets demonstrate that our approach achieves competitive novel view synthesis quality.
• Score: 30.884035795245946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing dynamic human-scene environments from monocular videos is a challenging problem that requires jointly modeling scene geometry, camera motion, and non-rigid human dynamics while enabling photorealistic rendering. Recent feed-forward methods can efficiently predict geometry, but they are often limited to non-photorealistic representations such as point clouds and meshes, or they fail to handle non-rigid objects, particularly dynamic humans. To fill this gap, we present GUSH3R (Gaussian-Unified Scene Human 3D Reconstruction), a feed-forward framework for online dynamic human-scene reconstruction. From a monocular human-scene video, our method reconstructs dynamic humans (everyone) and static scenes (everywhere) in a single forward pass (all at once) as 3D Gaussian Splatting (3DGS) primitives (as gaussians), which are geometrically consistent and capable of novel view synthesis. Experiments on monocular human-scene datasets demonstrate that our approach achieves competitive novel view synthesis quality while significantly improving inference efficiency compared to optimization-based methods.

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