GST: Precise 3D Human Body from a Single Image with Gaussian Splatting
  Transformers
Authors: name: Lorenza Prospero, name: Abdullah Hamdi, name: Joao F. Henriques, name: Christian Rupprecht

Reconstructing realistic 3D human models from monocular images has
significant applications in creative industries, human-computer interfaces, and
healthcare. We base our work on 3D Gaussian Splatting (3DGS), a scene
representation composed of a mixture of Gaussians. Predicting such mixtures for
a human from a single input image is challenging, as it is a non-uniform
density (with a many-to-one relationship with input pixels) with strict
physical constraints. At the same time, it needs to be flexible to accommodate
a variety of clothes and poses. Our key observation is that the vertices of
standardized human meshes (such as SMPL) can provide an adequate density and
approximate initial position for Gaussians. We can then train a transformer
model to jointly predict comparatively small adjustments to these positions, as
well as the other Gaussians' attributes and the SMPL parameters. We show
empirically that this combination (using only multi-view supervision) can
achieve fast inference of 3D human models from a single image without test-time
optimization, expensive diffusion models, or 3D points supervision. We also
show that it can improve 3D pose estimation by better fitting human models that
account for clothes and other variations. The code is available on the project
website https://abdullahamdi.com/gst/ .