Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learned Spatial Representations for Few-shot Talking-Head Synthesis   We propose a novel approach for few-shot talking-head synthesis. While recent
works in neural talking heads have produced promising results, they can still
produce images that do not preserve the identity of the subject in source
images. We posit this is a result of the entangled representation of each
subject in a single latent code that models 3D shape information, identity
cues, colors, lighting and even background details. In contrast, we propose to
factorize the representation of a subject into its spatial and style
components. Our method generates a target frame in two steps. First, it
predicts a dense spatial layout for the target image. Second, an image
generator utilizes the predicted layout for spatial denormalization and
synthesizes the target frame. We experimentally show that this disentangled
representation leads to a significant improvement over previous methods, both
quantitatively and qualitatively.

3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org