Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LISA: Learning Implicit Shape and Appearance of Hands   This paper proposes a do-it-all neural model of human hands, named LISA. The
model can capture accurate hand shape and appearance, generalize to arbitrary
hand subjects, provide dense surface correspondences, be reconstructed from
images in the wild and easily animated. We train LISA by minimizing the shape
and appearance losses on a large set of multi-view RGB image sequences
annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand
local coordinate, our model predicts the color and the signed distance with
respect to each hand bone independently, and then combines the per-bone
predictions using predicted skinning weights. The shape, color and pose
representations are disentangled by design, allowing to estimate or animate
only selected parameters. We experimentally demonstrate that LISA can
accurately reconstruct a dynamic hand from monocular or multi-view sequences,
achieving a noticeably higher quality of reconstructed hand shapes compared to
baseline approaches. Project page:
https://www.iri.upc.edu/people/ecorona/lisa/.

31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex