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Recent large-scale text-to-image generation models have made significantimprovements in the quality, realism, and diversity of the synthesized imagesand enable users to control the created content through language. However, thepersonalization aspect of these generative models is still challenging andunder-explored. In this work, we propose a pipeline that enablespersonalization of image generation with avatars capturing a user's identity ina delightful way. Our pipeline is zero-shot, avatar texture and style agnostic,and does not require training on the avatar at all - it is scalable to millionsof users who can generate a scene with their avatar. To render the avatar in apose faithful to the given text prompt, we propose a novel text-to-3D posediffusion model trained on a curated large-scale dataset of in-the-wild humanposes improving the performance of the SOTA text-to-motion modelssignificantly. We show, for the first time, how to leverage large-scale imagedatasets to learn human 3D pose parameters and overcome the limitations ofmotion capture datasets.

Text-Conditional Contextualized Avatars For Zero-Shot Personalization