Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 6D Rotation Representation For Unconstrained Head Pose Estimation   In this paper, we present a method for unconstrained end-to-end head pose
estimation. We address the problem of ambiguous rotation labels by introducing
the rotation matrix formalism for our ground truth data and propose a
continuous 6D rotation matrix representation for efficient and robust direct
regression. This way, our method can learn the full rotation appearance which
is contrary to previous approaches that restrict the pose prediction to a
narrow-angle for satisfactory results. In addition, we propose a geodesic
distance-based loss to penalize our network with respect to the SO(3) manifold
geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that
our proposed method significantly outperforms other state-of-the-art methods by
up to 20\%. We open-source our training and testing code along with our
pre-trained models: https://github.com/thohemp/6DRepNet.

23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat