  This paper introduces Camera-free Diffusion (CamFreeDiff) model for
360-degree 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.
Instead, our model incorporates a mechanism for predicting homography directly
within the multi-view diffusion framework. The core of our approach is to
formulate camera estimation by predicting the homography transformation from
the input view to a predefined canonical view. The homography provides
point-level correspondences between the input image and targeting panoramic
images, allowing connections enforced by correspondence-aware attention in a
fully differentiable manner. Qualitative and quantitative experimental results
demonstrate our model's strong robustness and generalization ability for
360-degree image outpainting in the challenging context of camera-free inputs.
