GHNeRF: Learning Generalizable Human Features with Efficient Neural
  Radiance Fields
Arnab Dey Di Yang Rohith Agaram Antitza Dantcheva Andrew I. Comport Srinath Sridhar Jean Martinet
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.