Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Generative Modeling for Scene Synthesis via Hybrid Representations   We present a deep generative scene modeling technique for indoor
environments. Our goal is to train a generative model using a feed-forward
neural network that maps a prior distribution (e.g., a normal distribution) to
the distribution of primary objects in indoor scenes. We introduce a 3D object
arrangement representation that models the locations and orientations of
objects, based on their size and shape attributes. Moreover, our scene
representation is applicable for 3D objects with different multiplicities
(repetition counts), selected from a database. We show a principled way to
train this model by combining discriminator losses for both a 3D object
arrangement representation and a 2D image-based representation. We demonstrate
the effectiveness of our scene representation and the deep learning method on
benchmark datasets. We also show the applications of this generative model in
scene interpolation and scene completion.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int