TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes
Alakh Aggarwal, Ningna Wang, Xiaohu Guo
IEEE Transactions on Visualization and Computer Graphics (CVM 2025), 2025. 🏆 Best Paper Award
Reconstructs the shape and true surface color of an unknown object from a single video of a hand handling it. It accounts for the shadows and reflected light the hand casts on the object.
Results#


Abstract#
Reconstructing 3D models of dynamic, real-world objects with high-fidelity textures from monocular frame sequences has been a challenging problem in recent years. This difficulty stems from factors such as shadows, indirect illumination, and inaccurate object-pose estimations due to occluding hand-object interactions. To address these challenges, we propose a novel approach that predicts the hand’s impact on environmental visibility and indirect illumination on the object’s surface albedo. Our method first learns the geometry and low-fidelity texture of the object, hand, and background through composite rendering of radiance fields. Simultaneously, we optimize the hand and object poses to achieve accurate object-pose estimations. We then refine physics-based rendering parameters—including roughness, specularity, albedo, hand visibility, skin color reflections, and environmental illumination—to produce precise albedo, and accurate hand illumination and shadow regions. Our approach surpasses state-of-the-art methods in texture reconstruction and, to the best of our knowledge, is the first to account for hand-object interactions in object texture reconstruction.
Citation#
@article{aggarwal2025texhoi,
title = {TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes},
author = {Aggarwal, Alakh and Wang, Ningna and Guo, Xiaohu},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2025},
doi = {10.1109/TVCG.2025.3567276}
}