Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo
  Collection   Non-parametric face modeling aims to reconstruct 3D face only from images
without shape assumptions. While plausible facial details are predicted, the
models tend to over-depend on local color appearance and suffer from ambiguous
noise. To address such problem, this paper presents a novel Learning to
Aggregate and Personalize (LAP) framework for unsupervised robust 3D face
modeling. Instead of using controlled environment, the proposed method
implicitly disentangles ID-consistent and scene-specific face from
unconstrained photo set. Specifically, to learn ID-consistent face, LAP
adaptively aggregates intrinsic face factors of an identity based on a novel
curriculum learning approach with relaxed consistency loss. To adapt the face
for a personalized scene, we propose a novel attribute-refining network to
modify ID-consistent face with target attribute and details. Based on the
proposed method, we make unsupervised 3D face modeling benefit from meaningful
image facial structure and possibly higher resolutions. Extensive experiments
on benchmarks show LAP recovers superior or competitive face shape and texture,
compared with state-of-the-art (SOTA) methods with or without prior and
supervision.

22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con