D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917768732868608 |
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| author | Fu, Bowen Wang, Gu Zhang, Chenyangguang Di, Yan Huang, Ziqin Leng, Zhiying Manhardt, Fabian Ji, Xiangyang Tombari, Federico |
| author_facet | Fu, Bowen Wang, Gu Zhang, Chenyangguang Di, Yan Huang, Ziqin Leng, Zhiying Manhardt, Fabian Ji, Xiangyang Tombari, Federico |
| contents | Reconstructing hand-held objects from a single RGB image is a challenging task in computer vision. In contrast to prior works that utilize deterministic modeling paradigms, we employ a point cloud denoising diffusion model to account for the probabilistic nature of this problem. In the core, we introduce centroid-fixed dual-stream conditional diffusion for monocular hand-held object reconstruction (D-SCo), tackling two predominant challenges. First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm, enhancing the stability of diffusion and reverse processes and the precision of feature projection. Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions with a novel unified hand-object semantic embedding, enhancing the reconstruction performance of the hand-occluded region of the object. Experiments on the synthetic ObMan dataset and three real-world datasets HO3D, MOW and DexYCB demonstrate that our approach can surpass all other state-of-the-art methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_14189 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction Fu, Bowen Wang, Gu Zhang, Chenyangguang Di, Yan Huang, Ziqin Leng, Zhiying Manhardt, Fabian Ji, Xiangyang Tombari, Federico Computer Vision and Pattern Recognition Reconstructing hand-held objects from a single RGB image is a challenging task in computer vision. In contrast to prior works that utilize deterministic modeling paradigms, we employ a point cloud denoising diffusion model to account for the probabilistic nature of this problem. In the core, we introduce centroid-fixed dual-stream conditional diffusion for monocular hand-held object reconstruction (D-SCo), tackling two predominant challenges. First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm, enhancing the stability of diffusion and reverse processes and the precision of feature projection. Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions with a novel unified hand-object semantic embedding, enhancing the reconstruction performance of the hand-occluded region of the object. Experiments on the synthetic ObMan dataset and three real-world datasets HO3D, MOW and DexYCB demonstrate that our approach can surpass all other state-of-the-art methods. |
| title | D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.14189 |