Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generation High resolution 3D model from natural language by Generative
  Adversarial Network   We present a method of generating high resolution 3D shapes from natural
language descriptions. To achieve this goal, we propose two steps that
generating low resolution shapes which roughly reflect texts and generating
high resolution shapes which reflect the detail of texts. In a previous paper,
the authors have shown a method of generating low resolution shapes. We improve
it to generate 3D shapes more faithful to natural language and test the
effectiveness of the method. To generate high resolution 3D shapes, we use the
framework of Conditional Wasserstein GAN. We propose two roles of Critic
separately, which calculate the Wasserstein distance between two probability
distribution, so that we achieve generating high quality shapes or acceleration
of learning speed of model. To evaluate our approach, we performed quantitive
evaluation with several numerical metrics for Critic models. Our method is
first to realize the generation of high quality model by propagating text
embedding information to high resolution task when generating 3D model.

23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol