Hi4D: 4D Instance Segmentation of Close Human Interaction
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Abstract (arXiv)
We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subjects is a challenging task due to occlusions and complex shapes. Hence, existing multi-view systems typically fuse 3D surfaces of close subjects into a single, connected mesh. To address this issue we leverage i) individually fitted neural implicit avatars; ii) an alternating optimization scheme that refines pose and surface through periods of close proximity; and iii) thus segment the fused raw scans into individual instances. From these instances we compile Hi4D dataset of 4D textured scans of 20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D contains rich interaction-centric annotations in 2D and 3D alongside accurately registered parametric body models. We define varied human pose and shape estimation tasks on this dataset and provide results from state-of-the-art methods on these benchmarks.
Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). Builds on 67 of its 92 references that are in the map. First posted on arXiv 27 Mar 2023.
Builds on
67 of 92 references.
References · 67
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50 most cited of 67 All 67 (JSON)
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Citing papers per year: 2023: 6; 2024: 22; 2025: 34; 2026: 25.
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- 2025Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models22 Dec 2025
- 2024Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images27 Sep 2024
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- 2026Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis20 Aug 2026
- 2026STAR: Skeletal Token Alignment and Rearrangement for Interaction Recognition19 Jul 2026
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