Image-guide generation based on DALLĀ·EĀ·2 generated images. Argus3D is capable of generating a wide range of shapes from unseen images. These shapes can be further enhanced with textures created by a texture model, which utilizes text prompts from DALLĀ·EĀ·2. Additionally, the use of Generated various text image prompts enables the generation of new and unique textures.
Novel shape generation vs nearest neighbor retrieval (Followed HyperDiffusion). For generated shapes (left) from our method, we look up the top-5 nearest neighbors (right) from the training set based on the Chamfer distance. As shown, our method does not simply memorize train samples and can generalize to novel shapes.
@inproceedings{inproceedings,
author = {Luo, Simian and Qian, Xuelin and Fu, Yanwei and Zhang, Yinda and Tai, Ying and Zhang, Zhenyu and Wang, Chengjie and Xue, Xiangyang},
year = {2023},
month = {10},
pages = {14093-14103},
title = {Learning Versatile 3D Shape Generation with Improved Auto-regressive Models},
doi = {10.1109/ICCV51070.2023.01300}
}
@misc{qian2024pushing,
title={Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability},
author={Xuelin Qian and Yu Wang and Simian Luo and Yinda Zhang and Ying Tai and Zhenyu Zhang and Chengjie Wang and Xiangyang Xue and Bo Zhao and Tiejun Huang and Yunsheng Wu and Yanwei Fu},
year={2024},
eprint={2402.12225},
archivePrefix={arXiv},
primaryClass={cs.CV}
}