--- license: apache-2.0 language: - en --- arxiv.org/abs/2503.15667

[CVPR'25]DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis

Yuming Gu1,2 · Phong Tran2 · Yujian Zheng2 · Hongyi Xu3 · Heyuan Li4 · Adilbek Karmanov2 · Hao Li2,5
1Unviersity of Southern California  2MBZUAI   3ByteDance Inc.  
4The Chinese University of Hong Kong, Shenzhen  5Pinscreen Inc.

Paper PDF Project Page

## 📜 Requirements * An NVIDIA GPU with CUDA support is required. * We have tested on a single A6000 GPU. * **Minimum**: The minimum GPU memory required is 30GB for generating a single NVS video (batch_size=1) of 32 frames each time. * **Recommended**: We recommend using a GPU with 40GB of memory. * Operating system: Linux ## 🧱 Download Pretrained Models ```bash Diffportrait360 |----... |----pretrained_weights |----back_head-230000.th # back head generator |----model_state-3400000.th # diffportrait360 main module |----easy-khair-180-gpc0.8-trans10-025000.th |----... ``` ## 🔗 BibTeX If you find [Diffportrait360](https://arxiv.org/abs/2503.15667) is useful for your research and applications, please cite Diffportrait360 using this BibTeX: ```BibTeX @article{gu2025diffportrait360, title={DiffPortrait360: Consistent Portrait Diffusion for 360 View Synthesis}, author={Gu, Yuming and Tran, Phong and Zheng, Yujian and Xu, Hongyi and Li, Heyuan and Karmanov, Adilbek and Li, Hao}, journal={arXiv preprint arXiv:2503.15667}, year={2025} } ``` ## License Our code is distributed under the Apache-2.0 license. ## Acknowledgements This work is supported by the Metaverse Center Grant from the MBZUAI Research Office. We appreciate the contributions from [Diffportrait3D](https://github.com/FreedomGu/DiffPortrait3D), [PanoHead](https://github.com/SizheAn/PanoHead), [SphereHead](https://lhyfst.github.io/spherehead/), [ControlNet](https://github.com/lllyasviel/ControlNet) for their open-sourced research. We thank [Egor Zakharov](https://egorzakharov.github.io/), [Zhenhui Lin](https://www.linkedin.com/in/zhenhui-lin-5b6510226/?originalSubdomain=ae), [Maksat Kengeskanov](https://www.linkedin.com/in/maksat-kengeskanov/%C2%A0/), and Yiming Chen for the early discussions, helpful suggestions, and feedback.