1,755 research outputs found
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
To facilitate the analysis of human actions, interactions and emotions, we
compute a 3D model of human body pose, hand pose, and facial expression from a
single monocular image. To achieve this, we use thousands of 3D scans to train
a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with
fully articulated hands and an expressive face. Learning to regress the
parameters of SMPL-X directly from images is challenging without paired images
and 3D ground truth. Consequently, we follow the approach of SMPLify, which
estimates 2D features and then optimizes model parameters to fit the features.
We improve on SMPLify in several significant ways: (1) we detect 2D features
corresponding to the face, hands, and feet and fit the full SMPL-X model to
these; (2) we train a new neural network pose prior using a large MoCap
dataset; (3) we define a new interpenetration penalty that is both fast and
accurate; (4) we automatically detect gender and the appropriate body models
(male, female, or neutral); (5) our PyTorch implementation achieves a speedup
of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to
both controlled images and images in the wild. We evaluate 3D accuracy on a new
curated dataset comprising 100 images with pseudo ground-truth. This is a step
towards automatic expressive human capture from monocular RGB data. The models,
code, and data are available for research purposes at
https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201
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
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