FAVOR: Full-Body AR-Driven Virtual Object Rearrangement Guided by Instruction Text
Abstract
Rearrangement operations form the crux of interactions between humans and their environment. The ability to generate natural, fluid sequences of this operation is of essential value in AR/VR and CG. Bridging a gap in the field, our study introduces FAVOR: a novel dataset for Full-body AR-driven Virtual Object Rearrangement that uniquely employs motion capture systems and AR eyeglasses. Comprising 3k diverse motion rearrangement sequences and 7.17 million interaction data frames, this dataset breaks new ground in research data. We also present a pipeline FAVORITE for producing digital human rearrangement motion sequences guided by instructions. Experimental results, both qualitative and quantitative, suggest that this dataset and pipeline deliver high-quality motion sequences. Our dataset, code, and appendix are available at https://kailinli.github.io/FAVOR.
Cite
Text
Li et al. "FAVOR: Full-Body AR-Driven Virtual Object Rearrangement Guided by Instruction Text." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I4.28097Markdown
[Li et al. "FAVOR: Full-Body AR-Driven Virtual Object Rearrangement Guided by Instruction Text." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/li2024aaai-favor/) doi:10.1609/AAAI.V38I4.28097BibTeX
@inproceedings{li2024aaai-favor,
title = {{FAVOR: Full-Body AR-Driven Virtual Object Rearrangement Guided by Instruction Text}},
author = {Li, Kailin and Yang, Lixin and Lin, Zenan and Xu, Jian and Zhan, Xinyu and Zhao, Yifei and Zhu, Pengxiang and Kang, Wenxiong and Wu, Kejian and Lu, Cewu},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {3136-3144},
doi = {10.1609/AAAI.V38I4.28097},
url = {https://mlanthology.org/aaai/2024/li2024aaai-favor/}
}