3D modeling has long been an important area in computer vision and computer
graphics. Recently, thanks to the breakthroughs in neural representations and
generative models, we witnessed a rapid development of 3D modeling. 3D human
modeling, lying at the core of many real-world applications, such as gaming and
animation, has attracted significant attention. Over the past few years, a
large body of work on creating 3D human avatars has been introduced, forming a
new and abundant knowledge base for 3D human modeling. The scale of the
literature makes it difficult for individuals to keep track of all the works.
This survey aims to provide a comprehensive overview of these emerging
techniques for 3D human avatar modeling, from both reconstruction and
generation perspectives. Firstly, we review representative methods for 3D human
reconstruction, including methods based on pixel-aligned implicit function,
neural radiance field, and 3D Gaussian Splatting, etc. We then summarize
representative methods for 3D human generation, especially those using large
language models like CLIP, diffusion models, and various 3D representations,
which demonstrate state-of-the-art performance. Finally, we discuss our
reflection on existing methods and open challenges for 3D human avatar
modeling, shedding light on future research.

3D 建模近年来得到了快速发展，特别是在三维人体建模方面，这篇综述从重建和生成的角度全面介绍了相关技术和方法，并对现有方法的反思和未来研究方向进行了讨论。

三维人物化身建模综述 -- 从重建到生成

A Survey on 3D Human Avatar Modeling -- From Reconstruction to  Generation

3D human modeling has been widely used for engaging interaction in gaming,
film, and animation. The customization of these characters is crucial for
creativity and scalability, which highlights the importance of controllability.
In this work, we introduce Text-guided 3D Human Generation (\texttt{T3H}),
where a model is to generate a 3D human, guided by the fashion description.
There are two goals: 1) the 3D human should render articulately, and 2) its
outfit is controlled by the given text. To address this \texttt{T3H} task, we
propose Compositional Cross-modal Human (CCH). CCH adopts cross-modal attention
to fuse compositional human rendering with the extracted fashion semantics.
Each human body part perceives relevant textual guidance as its visual
patterns. We incorporate the human prior and semantic discrimination to enhance
3D geometry transformation and fine-grained consistency, enabling it to learn
from 2D collections for data efficiency. We conduct evaluations on DeepFashion
and SHHQ with diverse fashion attributes covering the shape, fabric, and color
of upper and lower clothing. Extensive experiments demonstrate that CCH
achieves superior results for \texttt{T3H} with high efficiency.

该研究提出了一种基于文本描述的三维人体生成方法，采用跨模态注意力将构成人体渲染与提取的时尚语义融合，以控制服装的形状、面料和颜色，实现对三维人体的高效控制和生成。