Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects   Articulated objects exist widely in the real world. However, previous 3D
generative methods for unsupervised part decomposition are unsuitable for such
objects, because they assume a spatially fixed part location, resulting in
inconsistent part parsing. In this paper, we propose PPD (unsupervised
Pose-aware Part Decomposition) to address a novel setting that explicitly
targets man-made articulated objects with mechanical joints, considering the
part poses. We show that category-common prior learning for both part shapes
and poses facilitates the unsupervised learning of (1) part decomposition with
non-primitive-based implicit representation, and (2) part pose as joint
parameters under single-frame shape supervision. We evaluate our method on
synthetic and real datasets, and we show that it outperforms previous works in
consistent part parsing of the articulated objects based on comparable part
pose estimation performance to the supervised baseline.

35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat