JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
Authors: Jingyang Zhang,Shiwei Li,Yuanxun Lu,Tian Fang,David Neil McKinnon,Yanghai Tsin,Long Quan,Yao Yao
Presentation: ICLR 2024,Poster

Abstract: 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 the RGB-D diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGB-D generation, dense depth prediction, depth-conditioned image generation, and high-resolution 3D panorama generation.