D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction

• URL: http://arxiv.org/abs/2311.14189v4
• Date: Thu, 5 Sep 2024 05:19:56 GMT
• Title: D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction
• Authors: Bowen Fu, Gu Wang, Chenyangguang Zhang, Yan Di, Ziqin Huang, Zhiying Leng, Fabian Manhardt, Xiangyang Ji, Federico Tombari,
• Abstract summary: We introduce centroid-fixed dual-stream conditional diffusion for monocular hand-held object reconstruction. First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm. Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions.
• Score: 74.49121940466675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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