MagicMirror：在受限搜索空间中快速生成高质量虚拟形象
MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space
AI总结：
本文提出MagicMirror框架，通过条件NeRF、文本到图像扩散模型几何先验和变分分数蒸馏优化，在受限搜索空间中实现快速、高质量且符合文本提示的三维人体虚拟形象生成。
摘要：
我们提出了一种用于三维人体虚拟形象生成与个性化的新型框架，利用文本提示来增强用户参与度和定制化。我们方法的核心是旨在克服照片级真实虚拟形象合成挑战的关键创新。首先，我们利用一个在大规模无标注多视角数据集上训练的条件神经辐射场（NeRF）模型，创建一个多功能的初始解空间，从而加速并多样化虚拟形象生成。其次，我们开发了一种几何先验，利用文本到图像扩散模型的能力，以确保优越的视角不变性，并支持直接优化虚拟形象几何结构。这些基础思想由我们基于变分分数蒸馏（VSD）构建的优化流程加以补充，该流程缓解了纹理损失和过度饱和问题。正如我们大量实验所支持的那样，这些策略共同实现了具有无与伦比视觉质量和更好遵循输入文本提示的定制虚拟形象创建。您可以在我们的网站找到更多结果和视频：https://syntec-research.github.io/MagicMirror
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
