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. Codes will be released.

翻译：从单张RGB图像重建手持物体是计算机视觉中的一项挑战性任务。与以往采用确定性建模范式的工作不同，我们利用点云去噪扩散模型来应对该问题的概率性质。核心创新在于提出了用于单目手持物体重建的质心固定双流条件扩散方法（D-SCo），旨在解决两个主要挑战。首先，为避免物体质心偏移，我们采用了一种新颖的手部约束质心固定范式，增强了扩散与逆扩散过程的稳定性以及特征投影的精度。其次，我们引入了双流去噪器，通过新颖的统一手-物语义嵌入对手-物交互进行语义与几何建模，从而提升物体手部遮挡区域的重建性能。在合成数据集ObMan及三个真实世界数据集HO3D、MOW和DexYCB上的实验表明，我们的方法能够超越所有现有最先进技术。代码将开源。