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This is a technical report on the 360-degree panoramic image generation task based on diffusion models.
Recognizing scene viewpoint using panoramic place representation
Jianxiong Xiao, Krista A. Ehinger, Aude Oliva, and Antonio Torralba · 2012
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Earlier work this paper cites.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2022
Cited alongside, same era.
Mvdiffusion: Enabling holistic multi-view image generation with correspondence-aware diffusion
Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa · 2023
Cited alongside, same era.
Customizing 360-degree panoramas through text-to-image diffusion models, 2023a
Hai Wang, Xiaoyu Xiang, Yuchen Fan, and Jing-Hao Xue
Cited in the paper.
360-degree panorama generation from few unregistered nfov images
Jionghao Wang, Ziyu Chen, Jun Ling, Rong Xie, and Li Song
Cited in the paper.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan
Cited in the paper.
Adding conditional control to text-to-image diffusion models, 2023
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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