8,197 research outputs found
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
Deformable Shape Completion with Graph Convolutional Autoencoders
The availability of affordable and portable depth sensors has made scanning
objects and people simpler than ever. However, dealing with occlusions and
missing parts is still a significant challenge. The problem of reconstructing a
(possibly non-rigidly moving) 3D object from a single or multiple partial scans
has received increasing attention in recent years. In this work, we propose a
novel learning-based method for the completion of partial shapes. Unlike the
majority of existing approaches, our method focuses on objects that can undergo
non-rigid deformations. The core of our method is a variational autoencoder
with graph convolutional operations that learns a latent space for complete
realistic shapes. At inference, we optimize to find the representation in this
latent space that best fits the generated shape to the known partial input. The
completed shape exhibits a realistic appearance on the unknown part. We show
promising results towards the completion of synthetic and real scans of human
body and face meshes exhibiting different styles of articulation and
partiality.Comment: CVPR 201
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