MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space
<|reference_start|>MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space: 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<|reference_end|>
@article{comas-massagué2024magicmirror:,
    title={MagicMirror: Fast and High-Quality Avatar Generation with a Constrained
  Search Space},
    author={Armand Comas-Massagu'e, Di Qiu, Menglei Chai, Marcel B"uhler, Amit
  Raj, Ruiqi Gao, Qiangeng Xu, Mark Matthews, Paulo Gotardo, Octavia Camps,
  Sergio Orts-Escolano, Thabo Beeler},
    journal={arXiv preprint arXiv:2404.01296},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01296},
    primaryClass={cs.CV}
}