Wang, Hai;
Xiang, Xiaoyu;
Fan, Yuchen;
Xue, Jinghao;
(2024)
Customizing 360-Degree Panoramas Through Text-to-Image Diffusion Models.
In:
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
(pp. pp. 4933-4943).
IEEE: Waikoloa, HI, USA.
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Abstract
Personalized text-to-image (T2I) synthesis based on diffusion models has attracted significant attention in recent research. However, existing methods primarily concentrate on customizing subjects or styles, neglecting the exploration of global geometry. In this study, we propose an approach that focuses on the customization of 360-degree panoramas, which inherently possess global geometric properties, using a T2I diffusion model. To achieve this, we curate a paired image-text dataset specifically designed for the task and subsequently employ it to fine-tune a pre-trained T2I diffusion model with LoRA. Nevertheless, the fine-tuned model alone does not ensure the continuity between the leftmost and rightmost sides of the synthesized images, a crucial characteristic of 360-degree panoramas. To address this issue, we propose a method called StitchDiffusion. Specifically, we perform pre-denoising operations twice at each time step of the denoising process on the stitch block consisting of the leftmost and rightmost image regions. Furthermore, a global cropping is adopted to synthesize seamless 360-degree panoramas. Experimental results demonstrate the effectiveness of our customized model combined with the proposed StitchDiffusion in generating high-quality 360-degree panoramic images. Moreover, our customized model exhibits exceptional generalization ability in producing scenes unseen in the fine-tuning dataset. Code is available at https://github.com/littlewhitesea/StitchDiffusion.
| Type: | Proceedings paper |
|---|---|
| Title: | Customizing 360-Degree Panoramas Through Text-to-Image Diffusion Models |
| Event: | IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) |
| Dates: | 4th-8th January 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/WACV57701.2024.00486 |
| Publisher version: | https://doi.org/10.1109/WACV57701.2024.00486 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10184511 |
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