MagicMirror: Fast and High-Quality Avatar Generation with a Constrained
  Search Space
Armand Comas-Massagué Di Qiu Menglei Chai Marcel Bühler Amit Raj Ruiqi Gao Qiangeng Xu Mark Matthews Paulo Gotardo Octavia Camps Sergio Orts-Escolano Thabo Beeler
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