  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.
