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[bibtex]@InProceedings{Zhang_2024_CVPR, author = {Zhang, Mengqi and Fu, Yang and Ding, Zheng and Liu, Sifei and Tu, Zhuowen and Wang, Xiaolong}, title = {HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {8521-8531} }
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
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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