Primitive-Based 3D Human-Object Interaction Modelling and Programming
Siqi Liu, Yong-Lu Li, Zhou Fang, Xinpeng Liu, Yang You, Cewu Lu
摘要
Embedding Human and Articulated Object Interaction (HAOI) in 3D is an important direction for a deeper human activity understanding. Different from previous works that use parametric and CAD models to represent humans and objects, in this work, we propose a novel 3D geometric primitive-based language to encode both humans and objects. Given our new paradigm, humans and objects are all compositions of primitives instead of heterogeneous entities. Thus, mutual information learning may be achieved between the limited 3D data of humans and different object categories. Moreover, considering the simplicity of the expression and the richness of the information it contains, we choose the superquadric as the primitive representation. To explore an effective embedding of HAOI for the machine, we build a new benchmark on 3D HAOI consisting of primitives together with their images and propose a task requiring machines to recover 3D HAOI using primitives from images. Moreover, we propose a baseline of single-view 3D reconstruction on HAOI. We believe this primitive-based 3D HAOI representation would pave the way for 3D HAOI studies. Our code and data are available at https://mvig-rhos.com/p3haoi.
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引用它的顶会 Paper5
- SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction ScenariosLingwei Dang, Ruizhi Shao, Hongwen Zhang, Wei Min 等NeurIPS 2025 · 被引用 12 次
- TriDi: Trilateral Diffusion of 3D Humans, Objects, and InteractionsIlia A. Petrov, Riccardo Marin, Julian Chibane, Gerard Pons-MollICCV 2025 · 被引用 1 次
- Reconstructing In-the-Wild Open-Vocabulary Human-Object InteractionsBoran Wen, Dingbang Huang, Zichen Zhang, Jiahong Zhou 等CVPR 2025
- Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program SynthesisFeng Zhou, Ruiyang Liu, Chen Liu, Gaofeng He 等CVPR 2025
- CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangementYun Liu, Chengwen Zhang, Ruofan Xing, Bingda Tang 等CVPR 2025
它引用的顶会 Paper18
- PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback LoopHongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang 等ICCV 2021 · 被引用 376 次
- BEHAVE: Dataset and Method for Tracking Human Object InteractionsBharat Lal Bhatnagar, Xianghui Xie, Ilya A. Petrov, Cristian Sminchisescu 等CVPR 2022 · 被引用 144 次
- Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying KernelsYi Zhou, Chenglei Wu, Zimo Li, Chen Cao 等NeurIPS 2020 · 被引用 98 次
- LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part DiscoveryChun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein 等NeurIPS 2022 · 被引用 83 次
- AKB-48: A Real-World Articulated Object Knowledge BaseLiu Liu, Wenqiang Xu, Haoyuan Fu, Sucheng Qian 等CVPR 2022 · 被引用 64 次
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