Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From Real to Synthetic and Back: Synthesizing Training Data for
  Multi-Person Scene Understanding   We present a method for synthesizing naturally looking images of multiple
people interacting in a specific scenario. These images benefit from the
advantages of synthetic data: being fully controllable and fully annotated with
any type of standard or custom-defined ground truth. To reduce the
synthetic-to-real domain gap, we introduce a pipeline consisting of the
following steps: 1) we render scenes in a context modeled after the real world,
2) we train a human parsing model on the synthetic images, 3) we use the model
to estimate segmentation maps for real images, 4) we train a conditional
generative adversarial network (cGAN) to learn the inverse mapping -- from a
segmentation map to a real image, and 5) given new synthetic segmentation maps,
we use the cGAN to generate realistic images. An illustration of our pipeline
is presented in Figure 2. We use the generated data to train a multi-task model
on the challenging tasks of UV mapping and dense depth estimation. We
demonstrate the value of the data generation and the trained model, both
quantitatively and qualitatively on the CMU Panoptic Dataset.

18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th