Ins-HOI: Instance Aware Human-Object Interactions Recovery

• URL: http://arxiv.org/abs/2312.09641v2
• Date: Thu, 21 Mar 2024 15:57:28 GMT
• Title: Ins-HOI: Instance Aware Human-Object Interactions Recovery
• Authors: Jiajun Zhang, Yuxiang Zhang, Hongwen Zhang, Xiao Zhou, Boyao Zhou, Ruizhi Shao, Zonghai Hu, Yebin Liu,
• Abstract summary: We propose an end-to-end Instance-aware Human-Object Interactions recovery (Ins-HOI) framework. Ins-HOI supports instance-level reconstruction and provides reasonable and realistic invisible contact surfaces. We collect a large-scale, high-fidelity 3D scan dataset, including 5.2k high-quality scans with real-world human-chair and hand-object interactions.
• Score: 44.02128629239429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately modeling detailed interactions between human/hand and object is an appealing yet challenging task. Current multi-view capture systems are only capable of reconstructing multiple subjects into a single, unified mesh, which fails to model the states of each instance individually during interactions. To address this, previous methods use template-based representations to track human/hand and object. However, the quality of the reconstructions is limited by the descriptive capabilities of the templates so that these methods are inherently struggle with geometry details, pressing deformations and invisible contact surfaces. In this work, we propose an end-to-end Instance-aware Human-Object Interactions recovery (Ins-HOI) framework by introducing an instance-level occupancy field representation. However, the real-captured data is presented as a holistic mesh, unable to provide instance-level supervision. To address this, we further propose a complementary training strategy that leverages synthetic data to introduce instance-level shape priors, enabling the disentanglement of occupancy fields for different instances. Specifically, synthetic data, created by randomly combining individual scans of humans/hands and objects, guides the network to learn a coarse prior of instances. Meanwhile, real-captured data helps in learning the overall geometry and restricting interpenetration in contact areas. As demonstrated in experiments, our method Ins-HOI supports instance-level reconstruction and provides reasonable and realistic invisible contact surfaces even in cases of extremely close interaction. To facilitate the research of this task, we collect a large-scale, high-fidelity 3D scan dataset, including 5.2k high-quality scans with real-world human-chair and hand-object interactions. The code and data will be public for research purposes.

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