RHOBIN Challenge: Reconstruction of Human Object Interaction
Xianghui Xie, Xi Wang, Nikos Athanasiou, Bharat Lal Bhatnagar, and Chun-Hao P. Huang, Kaichun Mo, Hao Chen, Xia Jia, Zerui Zhang, and Liangxian Cui, Xiao Lin, Bingqiao Qian, Jie Xiao, Wenfei Yang, and Hyeongjin Nam, Daniel Sungho Jung, Kihoon Kim, Kyoung Mu Lee and

TL;DR
The RHOBIN challenge aims to advance 3D reconstruction of human-object interactions from monocular images, fostering collaboration and innovation in handling occlusion and complex dynamics.
Contribution
This paper introduces the first RHOBIN challenge, uniting research on human and object reconstruction and interaction modeling, with detailed analysis of top methods.
Findings
Human reconstruction is mature even under heavy occlusion.
Object pose estimation remains challenging.
Interaction modeling requires further research.
Abstract
Modeling the interaction between humans and objects has been an emerging research direction in recent years. Capturing human-object interaction is however a very challenging task due to heavy occlusion and complex dynamics, which requires understanding not only 3D human pose, and object pose but also the interaction between them. Reconstruction of 3D humans and objects has been two separate research fields in computer vision for a long time. We hence proposed the first RHOBIN challenge: reconstruction of human-object interactions in conjunction with the RHOBIN workshop. It was aimed at bringing the research communities of human and object reconstruction as well as interaction modeling together to discuss techniques and exchange ideas. Our challenge consists of three tracks of 3D reconstruction from monocular RGB images with a focus on dealing with challenging interaction scenarios. Our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHuman Pose and Action Recognition · Advanced Neural Network Applications · Multimodal Machine Learning Applications
MethodsFocus
