HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data

• URL: http://arxiv.org/abs/2403.12011v1
• Date: Mon, 18 Mar 2024 17:48:31 GMT
• Title: HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
• Authors: Mengqi Zhang, Yang Fu, Zheng Ding, Sifei Liu, Zhuowen Tu, Xiaolong Wang,
• Abstract summary: We propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. We adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems.
• Score: 42.49031063635004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. This offers a more controllable and realistic synthesis as we can specify the structure and style inputs in a disentangled manner. HOIDiffusion is trained by leveraging a diffusion model pre-trained on large-scale natural images and a few 3D human demonstrations. Beyond controllable image synthesis, we adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems. Project page: https://mq-zhang1.github.io/HOIDiffusion

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