UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer

作者：Soon Yau Cheong, A. Mustafa, Andrew Gilbert · 发表于：2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · 年份：2023 · DOI：10.1109/iccvw60793.2023.00451 · 被引用次数：25 · 研究领域：Computer Science

Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people. However, due to the random nature of the generation process, the person has a different appearance e.g. pose, face, and clothing, despite using the same text prompt. The appearance inconsistency makes T2I unsuitable for pose transfer. We address this by proposing a multimodal diffusion model that accepts text, pose, and visual prompting. Our model is the first unified method to perform all person image tasks-generation, pose transfer, and mask-less edit. We also pioneer using small dimensional 3D body model parameters directly to demonstrate new capability - simultaneous pose and camera view interpolation while maintaining the person’s appearance.