Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Purely Unsupervised Disentanglement of Appearance and Shape for
  Person Images Generation   There have been a fairly of research interests in exploring the
disentanglement of appearance and shape from human images. Most existing
endeavours pursuit this goal by either using training images with annotations
or regulating the training process with external clues such as human skeleton,
body segmentation or cloth patches etc. In this paper, we aim to address this
challenge in a more unsupervised manner---we do not require any annotation nor
any external task-specific clues. To this end, we formulate an
encoder-decoder-like network to extract both the shape and appearance features
from input images at the same time, and train the parameters by three losses:
feature adversarial loss, color consistency loss and reconstruction loss. The
feature adversarial loss mainly impose little to none mutual information
between the extracted shape and appearance features, while the color
consistency loss is to encourage the invariance of person appearance
conditioned on different shapes. More importantly, our unsupervised
(Unsupervised learning has many interpretations in different tasks. To be
clear, in this paper, we refer unsupervised learning as learning without
task-specific human annotations, pairs or any form of weak supervision.)
framework utilizes learned shape features as masks which are applied to the
input itself in order to obtain clean appearance features. Without using fixed
input human skeleton, our network better preserves the conditional human
posture while requiring less supervision. Experimental results on DeepFashion
and Market1501 demonstrate that the proposed method achieves clean
disentanglement and is able to synthesis novel images of comparable quality
with state-of-the-art weakly-supervised or even supervised methods.

16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys