Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text2Video: Text-driven Talking-head Video Synthesis with Personalized
  Phoneme-Pose Dictionary   With the advance of deep learning technology, automatic video generation from
audio or text has become an emerging and promising research topic. In this
paper, we present a novel approach to synthesize video from the text. The
method builds a phoneme-pose dictionary and trains a generative adversarial
network (GAN) to generate video from interpolated phoneme poses. Compared to
audio-driven video generation algorithms, our approach has a number of
advantages: 1) It only needs a fraction of the training data used by an
audio-driven approach; 2) It is more flexible and not subject to vulnerability
due to speaker variation; 3) It significantly reduces the preprocessing,
training and inference time. We perform extensive experiments to compare the
proposed method with state-of-the-art talking face generation methods on a
benchmark dataset and datasets of our own. The results demonstrate the
effectiveness and superiority of our approach.

8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat