AvatarBooth：高质量且可定制的3D人体化身生成
AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
AI总结：
该研究提出AvatarBooth方法，通过人脸、人体双微调扩散模型及姿态约束、多分辨率渲染策略，实现高质量可定制的3D人体化身生成与编辑，性能优于现有文本转3D方法。
摘要：
我们提出了AvatarBooth，这是一种利用文本提示或特定图像生成高质量3D化身的新方法。与以往仅能基于简单文本描述合成化身的方法不同，我们的方法既支持基于文本的模型生成与编辑，又能从随手拍摄的面部或身体图像创建个性化化身。我们的核心贡献是通过分别针对人脸和人体的两个微调扩散模型，实现精准的化身生成控制。这让我们能够捕捉面部外观、服装和配饰的精细细节，生成高度逼真的化身。此外，我们在优化过程中引入了姿态一致性约束，以提升扩散模型合成的头部图像的多视角一致性，从而消除不受控的人体姿态带来的干扰。另外，我们提出了一种多分辨率渲染策略，可对3D化身生成进行从粗到细的监督，进而提升所提系统的性能。生成的化身模型可通过额外的文本描述进一步编辑，并可由运动序列驱动。实验表明，无论是基于文本提示还是特定图像，AvatarBooth在渲染质量和几何质量上均优于以往的文本转3D方法。请访问我们的项目网站：https://zeng-yifei.github.io/avatarbooth_page/。
英文摘要：
We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.
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。
