This work aims to reconstruct the 3D geometry of a rigid object manipulated by one or both hands using monocular RGB video. Previous methods rely on Structure-from-Motion or hand priors to estimate relative motion between the object and camera, which typically assume textured objects or single-hand interactions. To accurately recover object geometry in dynamic interactions, we incorporate priors from 3D generation model into object pose estimation and propose semantic consistency constraints to solve the challenge of shape and texture discrepancy between the generated priors and observations. The poses are initialized, followed by joint optimization of the object poses and implicit neural representation. During optimization, a novel pose outlier voting strategy with inter-view consistency is proposed to correct large pose errors. Experiments on three datasets demonstrate that our method significantly outperforms the state-of-the-art in reconstruction quality for both single- and two-hand scenarios. Our project page: https://east-j.github.io/dynhor/

Our work aims to reconstruct a 3D object that is held and rotated by a hand in front of a static RGB camera. Previous methods that use implicit neural representations to recover the geometry of a generic hand-held object from multi-view images achieved compelling results in the visible part of the object. However, these methods falter in accurately capturing the shape within the hand-object contact region due to occlusion. In this paper, we propose a novel method that deals with surface reconstruction under occlusion by incorporating priors of 2D occlusion elucidation and physical contact constraints. For the former, we introduce an object amodal completion network to infer the 2D complete mask of objects under occlusion. To ensure the accuracy and view consistency of the predicted 2D amodal masks, we devise a joint optimization method for both amodal mask refinement and 3D reconstruction. For the latter, we impose penetration and attraction constraints on the local geometry in contact regions. We evaluate our approach on HO3D and HOD datasets and demonstrate that it outperforms the state-of-the-art methods in terms of reconstruction surface quality, with an improvement of 52% on HO3D and 20% on HOD. Project webpage: https://east-j.github.io/ihor.

Grasping motion planning aims to find a feasible grasping trajectory in the configuration space given an input target grasp. While optimizing grasp motion with two or three-fingered grippers has been well studied, the study on natural grasp motion planning with a dexterous hand remains a very challenging problem due to the high dimensional working space. In this work, we propose a novel temporal-parametric grasp prior (TPGP) optimization method to simplify the difficulty of grasping trajectory optimization for the dexterous hand while maintaining smooth and natural properties of the grasping motion. Specifically, we formulate the discrete trajectory parameters into a temporal-based parameterization, where the prior constraint provided by a hand poser network, is introduced to ensure that hand pose is natural and reasonable throughout the trajectory. Finally, we present a joint target optimization strategy to enhance the target pose for more feasible trajectories. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp motion on various metrics.