Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Surface-Aligned Neural Radiance Fields for Controllable 3D Human
  Synthesis   We propose a new method for reconstructing controllable implicit 3D human
models from sparse multi-view RGB videos. Our method defines the neural scene
representation on the mesh surface points and signed distances from the surface
of a human body mesh. We identify an indistinguishability issue that arises
when a point in 3D space is mapped to its nearest surface point on a mesh for
learning surface-aligned neural scene representation. To address this issue, we
propose projecting a point onto a mesh surface using a barycentric
interpolation with modified vertex normals. Experiments with the ZJU-MoCap and
Human3.6M datasets show that our approach achieves a higher quality in a
novel-view and novel-pose synthesis than existing methods. We also demonstrate
that our method easily supports the control of body shape and clothes. Project
page: https://pfnet-research.github.io/surface-aligned-nerf/.

4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th