HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video

Zicong Fan, Maria Parelli, Maria Eleni Kadoglou, Xu Chen, Muhammed Kocabas, Michael J. Black, Otmar Hilliges; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 494-504

Since humans interact with diverse objects every day the holistic 3D capture of these interactions is important to understand and model human behaviour. However most existing methods for hand-object reconstruction from RGB either assume pre-scanned object templates or heavily rely on limited 3D hand-object data restricting their ability to scale and generalize to more unconstrained interaction settings. To address this we introduce HOLD -- the first category-agnostic method that reconstructs an articulated hand and an object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can reconstruct disentangled 3D hands and objects from 2D images. We also further incorporate hand-object constraints to improve hand-object poses and consequently the reconstruction quality. Our method does not rely on any 3D hand-object annotations while significantly outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings. Moreover we qualitatively show its robustness in reconstructing from in-the-wild videos. See https://github.com/zc-alexfan/hold for code data models and updates.