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

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

18 Apr 2023arXiv:2304.08870archive 2025-07-28

Soon Yau Cheong, Armin Mustafa, Andrew Gilbert

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.

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