Decoupled Generative Modeling for Human-Object Interaction Synthesis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908956347072512 |
|---|---|
| author | Jung, Hwanhee Lee, Seunggwan Yoon, Jeongyoon Kim, SeungHyeon Nam, Giljoo Huang, Qixing Kim, Sangpil |
| author_facet | Jung, Hwanhee Lee, Seunggwan Yoon, Jeongyoon Kim, SeungHyeon Nam, Giljoo Huang, Qixing Kim, Sangpil |
| contents | Synthesizing realistic human-object interaction (HOI) is essential for 3D computer vision and robotics, underpinning animation and embodied control. Existing approaches often require manually specified intermediate waypoints and place all optimization objectives on a single network, which increases complexity, reduces flexibility, and leads to errors such as unsynchronized human and object motion or penetration. To address these issues, we propose Decoupled Generative Modeling for Human-Object Interaction Synthesis (DecHOI), which separates path planning and action synthesis. A trajectory generator first produces human and object trajectories without prescribed waypoints, and an action generator conditions on these paths to synthesize detailed motions. To further improve contact realism, we employ adversarial training with a discriminator that focuses on the dynamics of distal joints. The framework also models a moving counterpart and supports responsive, long-sequence planning in dynamic scenes, while preserving plan consistency. Across two benchmarks, FullBodyManipulation and 3D-FUTURE, DecHOI surpasses prior methods on most quantitative metrics and qualitative evaluations, and perceptual studies likewise prefer our results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoupled Generative Modeling for Human-Object Interaction Synthesis Jung, Hwanhee Lee, Seunggwan Yoon, Jeongyoon Kim, SeungHyeon Nam, Giljoo Huang, Qixing Kim, Sangpil Computer Vision and Pattern Recognition Synthesizing realistic human-object interaction (HOI) is essential for 3D computer vision and robotics, underpinning animation and embodied control. Existing approaches often require manually specified intermediate waypoints and place all optimization objectives on a single network, which increases complexity, reduces flexibility, and leads to errors such as unsynchronized human and object motion or penetration. To address these issues, we propose Decoupled Generative Modeling for Human-Object Interaction Synthesis (DecHOI), which separates path planning and action synthesis. A trajectory generator first produces human and object trajectories without prescribed waypoints, and an action generator conditions on these paths to synthesize detailed motions. To further improve contact realism, we employ adversarial training with a discriminator that focuses on the dynamics of distal joints. The framework also models a moving counterpart and supports responsive, long-sequence planning in dynamic scenes, while preserving plan consistency. Across two benchmarks, FullBodyManipulation and 3D-FUTURE, DecHOI surpasses prior methods on most quantitative metrics and qualitative evaluations, and perceptual studies likewise prefer our results. |
| title | Decoupled Generative Modeling for Human-Object Interaction Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.19049 |