Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape,
  Appearance, and Pose   While deep learning reshaped the classical motion capture pipeline with
feed-forward networks, generative models are required to recover fine alignment
via iterative refinement. Unfortunately, the existing models are usually
hand-crafted or learned in controlled conditions, only applicable to limited
domains. We propose a method to learn a generative neural body model from
unlabelled monocular videos by extending Neural Radiance Fields (NeRFs). We
equip them with a skeleton to apply to time-varying and articulated motion. A
key insight is that implicit models require the inverse of the forward
kinematics used in explicit surface models. Our reparameterization defines
spatial latent variables relative to the pose of body parts and thereby
overcomes ill-posed inverse operations with an overparameterization. This
enables learning volumetric body shape and appearance from scratch while
jointly refining the articulated pose; all without ground truth labels for
appearance, pose, or 3D shape on the input videos. When used for
novel-view-synthesis and motion capture, our neural model improves accuracy on
diverse datasets. Project website: https://lemonatsu.github.io/anerf/ .

20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci