  We introduce JointNet, a novel neural network architecture for modeling the
joint distribution of images and an additional dense modality (e.g., depth
maps). JointNet is extended from a pre-trained text-to-image diffusion model,
where a copy of the original network is created for the new dense modality
branch and is densely connected with the RGB branch. The RGB branch is locked
during network fine-tuning, which enables efficient learning of the new
modality distribution while maintaining the strong generalization ability of
the large-scale pre-trained diffusion model. We demonstrate the effectiveness
of JointNet by using RGBD diffusion as an example and through extensive
experiments, showcasing its applicability in a variety of applications,
including joint RGBD generation, dense depth prediction, depth-conditioned
image generation, and coherent tile-based 3D panorama generation.
