Text-Conditional Contextualized Avatars For Zero-Shot Personalization
This enables scalable personalization for millions of users in generative models, addressing an under-explored aspect in text-to-image generation.
The paper tackles the challenge of personalizing image generation with user avatars by proposing a zero-shot pipeline that renders avatars in poses faithful to text prompts, using a novel text-to-3D pose diffusion model trained on a curated large-scale dataset to improve SOTA text-to-motion models significantly.
Recent large-scale text-to-image generation models have made significant improvements in the quality, realism, and diversity of the synthesized images and enable users to control the created content through language. However, the personalization aspect of these generative models is still challenging and under-explored. In this work, we propose a pipeline that enables personalization of image generation with avatars capturing a user's identity in a 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 millions of users who can generate a scene with their avatar. To render the avatar in a pose faithful to the given text prompt, we propose a novel text-to-3D pose diffusion model trained on a curated large-scale dataset of in-the-wild human poses improving the performance of the SOTA text-to-motion models significantly. We show, for the first time, how to leverage large-scale image datasets to learn human 3D pose parameters and overcome the limitations of motion capture datasets.