Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising
results in 3D scene representations, including 3D human representations.
However, these representations often lack crucial information on the underlying
human pose and structure, which is crucial for AR/VR applications and games. In
this paper, we introduce a novel approach, termed GHNeRF, designed to address
these limitations by learning 2D/3D joint locations of human subjects with NeRF
representation. GHNeRF uses a pre-trained 2D encoder streamlined to extract
essential human features from 2D images, which are then incorporated into the
NeRF framework in order to encode human biomechanic features. This allows our
network to simultaneously learn biomechanic features, such as joint locations,
along with human geometry and texture. To assess the effectiveness of our
method, we conduct a comprehensive comparison with state-of-the-art human NeRF
techniques and joint estimation algorithms. Our results show that GHNeRF can
achieve state-of-the-art results in near real-time.

GHNeRF 是一种新颖的方法，通过学习 2D/3D 人体关节位置与 NeRF 表示来解决 3D 人体表示中缺乏的关键信息，以实现对人体生物力学特征的编码，并得到接近实时的最先进结果。