We present DreamAvatar, a text-and-shape guided framework for generating
high-quality 3D human avatars with controllable poses. While encouraging
results have been produced by recent methods on text-guided 3D common object
generation, generating high-quality human avatars remains an open challenge due
to the complexity of the human body's shape, pose, and appearance. We propose
DreamAvatar to tackle this challenge, which utilizes a trainable NeRF for
predicting density and color features for 3D points and a pre-trained
text-to-image diffusion model for providing 2D self-supervision. Specifically,
we leverage SMPL models to provide rough pose and shape guidance for the
generation. We introduce a dual space design that comprises a canonical space
and an observation space, which are related by a learnable deformation field
through the NeRF, allowing for the transfer of well-optimized texture and
geometry from the canonical space to the target posed avatar. Additionally, we
exploit a normal-consistency regularization to allow for more vivid generation
with detailed geometry and texture. Through extensive evaluations, we
demonstrate that DreamAvatar significantly outperforms existing methods,
establishing a new state-of-the-art for text-and-shape guided 3D human
generation.

DreamAvatar 是一个生成高质量、可控制姿态的 3D 人类头像的框架，它使用了可训练的 NeRF 生成 3D 点密度和颜色特征以及预先训练的文本到图像扩散模型提供 2D 自监督。通过 SMPL 模型提供粗略的姿势和形状指导生成，并引入了包含规范空间和观察空间的双重空间设计，它们之间由 NeRF 学习的变形场联系，从规范空间将优化的纹理和几何转移到目标位置头像，添加正常一致性正则化以获得更生动的几何和纹理。经过广泛的评估，证明 DreamAvatar 明显优于现有方法，为文本和形状引导的 3D 人体生成设立了新的技术水准。