-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Yuan_2025_CVPR, author = {Yuan, Xiaoding and Tang, Shitao and Li, Kejie and Wang, Peng}, title = {CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {16408-16417} }
CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model
Abstract
This paper introduces Camera-free Diffusion (CamFreeDiff) model for 360^\circ image outpainting from a single camera-free image and text description. This method distinguishes itself from existing strategies, such as MVDiffusion, by eliminating the requirement for predefined camera poses. CamFreeDiff seamlessly incorporates a mechanism for predicting homography within the multi-view diffusion framework. The key component of our approach is to formulate camera estimation by directly predicting the homography transformation from the input view to the predefined canonical view. In contrast to the direct two-stage approach of image transformation and outpainting, CamFreeDiff utilizes predicted homography to establish point-level correspondences between the input view and the target panoramic view. This enables consistency through correspondence-aware attention, which is learned in a fully differentiable manner. Qualitative and quantitative experimental results demonstrate the strong robustness and performance of CamFreeDiff for 360^\circ image outpainting in the challenging context of camera-free inputs.
Related Material