MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space
This work addresses the need for fast and customizable avatar generation for applications in virtual reality and gaming, representing a novel method for a known bottleneck.
The paper tackles the problem of generating high-quality 3D human avatars from text prompts by introducing a framework that combines conditional NeRF, geometric priors from diffusion models, and VSD optimization, resulting in avatars with superior visual quality and better text adherence.
We introduce a novel framework for 3D human avatar generation and personalization, leveraging text prompts to enhance user engagement and customization. Central to our approach are key innovations aimed at overcoming the challenges in photo-realistic avatar synthesis. Firstly, we utilize a conditional Neural Radiance Fields (NeRF) model, trained on a large-scale unannotated multi-view dataset, to create a versatile initial solution space that accelerates and diversifies avatar generation. Secondly, we develop a geometric prior, leveraging the capabilities of Text-to-Image Diffusion Models, to ensure superior view invariance and enable direct optimization of avatar geometry. These foundational ideas are complemented by our optimization pipeline built on Variational Score Distillation (VSD), which mitigates texture loss and over-saturation issues. As supported by our extensive experiments, these strategies collectively enable the creation of custom avatars with unparalleled visual quality and better adherence to input text prompts. You can find more results and videos in our website: https://syntec-research.github.io/MagicMirror