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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.08390 |
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| _version_ | 1866917327316975616 |
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| author | Wang, Zhi Liu, Liu Liu, Ruonan Guo, Dan Wang, Meng |
| author_facet | Wang, Zhi Liu, Liu Liu, Ruonan Guo, Dan Wang, Meng |
| contents | Recent progress in 3D hand--object interaction (HOI) generation has primarily focused on single--hand grasp synthesis, while bimanual manipulation remains significantly more challenging. Long--horizon planning instability, fine--grained joint articulation, and complex cross--hand coordination make coherent bimanual generation difficult, especially under multimodal conditions. Existing approaches often struggle to simultaneously ensure temporal consistency, physical plausibility, and semantic alignment over extended sequences. We propose StructBiHOI, a Structured articulation modeling framework for long-horizon Bimanual HOI generation. Our key insight is to structurally disentangle temporal joint planning from frame--level manipulation refinement. Specifically, a jointVAE models long-term joint evolution conditioned on object geometry and task semantics, while a maniVAE refines fine-grained hand poses at the single--frame level. To enable stable and efficient long--sequence generation, we incorporate a state--space--inspired diffusion denoiser based on Mamba, which models long--range dependencies with linear complexity. This hierarchical design facilitates coherent dual-hand coordination and articulated object interaction. Extensive experiments on bimanual manipulation and single-hand grasping benchmarks demonstrate that our method achieves superior long--horizon stability, motion realism, and computational efficiency compared to strong baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08390 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StructBiHOI: Structured Articulation Modeling for Long--Horizon Bimanual Hand--Object Interaction Generation Wang, Zhi Liu, Liu Liu, Ruonan Guo, Dan Wang, Meng Robotics Computer Vision and Pattern Recognition Recent progress in 3D hand--object interaction (HOI) generation has primarily focused on single--hand grasp synthesis, while bimanual manipulation remains significantly more challenging. Long--horizon planning instability, fine--grained joint articulation, and complex cross--hand coordination make coherent bimanual generation difficult, especially under multimodal conditions. Existing approaches often struggle to simultaneously ensure temporal consistency, physical plausibility, and semantic alignment over extended sequences. We propose StructBiHOI, a Structured articulation modeling framework for long-horizon Bimanual HOI generation. Our key insight is to structurally disentangle temporal joint planning from frame--level manipulation refinement. Specifically, a jointVAE models long-term joint evolution conditioned on object geometry and task semantics, while a maniVAE refines fine-grained hand poses at the single--frame level. To enable stable and efficient long--sequence generation, we incorporate a state--space--inspired diffusion denoiser based on Mamba, which models long--range dependencies with linear complexity. This hierarchical design facilitates coherent dual-hand coordination and articulated object interaction. Extensive experiments on bimanual manipulation and single-hand grasping benchmarks demonstrate that our method achieves superior long--horizon stability, motion realism, and computational efficiency compared to strong baselines. |
| title | StructBiHOI: Structured Articulation Modeling for Long--Horizon Bimanual Hand--Object Interaction Generation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.08390 |