GASPACHO: Gaussian Splatting for Controllable Humans and Objects
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909963521097728 |
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| author | Mir, Aymen Moreau, Arthur Dhamo, Helisa Zhang, Zhensong Pons-Moll, Gerard Pérez-Pellitero, Eduardo |
| author_facet | Mir, Aymen Moreau, Arthur Dhamo, Helisa Zhang, Zhensong Pons-Moll, Gerard Pérez-Pellitero, Eduardo |
| contents | We present GASPACHO, a method for generating photorealistic, controllable renderings of human-object interactions from multi-view RGB video. Unlike prior work that reconstructs only the human and treats objects as background, GASPACHO simultaneously recovers animatable templates for both the human and the interacting object as distinct sets of Gaussians, thereby allowing for controllable renderings of novel human object interactions in different poses from novel-camera viewpoints. We introduce a novel formulation that learns object Gaussians on an underlying 2D surface manifold rather than in 3D volume, yielding sharper, fine-grained object details for dynamic object reconstruction. We further propose a contact constraint in Gaussian space that regularizes human-object relations and enables natural, physically plausible animation. Across three benchmarks - BEHAVE, NeuralDome, and DNA-Rendering - GASPACHO achieves high-quality reconstructions under heavy occlusion and supports controllable synthesis of novel human-object interactions. We also demonstrate that our method allows for composition of humans and objects in 3D scenes and for the first time showcase that neural rendering can be used for the controllable generation of photoreal humans interacting with dynamic objects in diverse scenes. Our results are available at: https://miraymen.github.io/gaspacho/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09342 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GASPACHO: Gaussian Splatting for Controllable Humans and Objects Mir, Aymen Moreau, Arthur Dhamo, Helisa Zhang, Zhensong Pons-Moll, Gerard Pérez-Pellitero, Eduardo Computer Vision and Pattern Recognition We present GASPACHO, a method for generating photorealistic, controllable renderings of human-object interactions from multi-view RGB video. Unlike prior work that reconstructs only the human and treats objects as background, GASPACHO simultaneously recovers animatable templates for both the human and the interacting object as distinct sets of Gaussians, thereby allowing for controllable renderings of novel human object interactions in different poses from novel-camera viewpoints. We introduce a novel formulation that learns object Gaussians on an underlying 2D surface manifold rather than in 3D volume, yielding sharper, fine-grained object details for dynamic object reconstruction. We further propose a contact constraint in Gaussian space that regularizes human-object relations and enables natural, physically plausible animation. Across three benchmarks - BEHAVE, NeuralDome, and DNA-Rendering - GASPACHO achieves high-quality reconstructions under heavy occlusion and supports controllable synthesis of novel human-object interactions. We also demonstrate that our method allows for composition of humans and objects in 3D scenes and for the first time showcase that neural rendering can be used for the controllable generation of photoreal humans interacting with dynamic objects in diverse scenes. Our results are available at: https://miraymen.github.io/gaspacho/ |
| title | GASPACHO: Gaussian Splatting for Controllable Humans and Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.09342 |