4,989 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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
An Actor-Centric Approach to Facial Animation Control by Neural Networks For Non-Player Characters in Video Games
Game developers increasingly consider the degree to which character animation emulates facial expressions found in cinema. Employing animators and actors to produce cinematic facial animation by mixing motion capture and hand-crafted animation is labor intensive and therefore expensive. Emotion corpora and neural network controllers have shown promise toward developing autonomous animation that does not rely on motion capture. Previous research and practice in disciplines of Computer Science, Psychology and the Performing Arts have provided frameworks on which to build a workflow toward creating an emotion AI system that can animate the facial mesh of a 3d non-player character deploying a combination of related theories and methods. However, past investigations and their resulting production methods largely ignore the emotion generation systems that have evolved in the performing arts for more than a century. We find very little research that embraces the intellectual process of trained actors as complex collaborators from which to understand and model the training of a neural network for character animation. This investigation demonstrates a workflow design that integrates knowledge from the performing arts and the affective branches of the social and biological sciences. Our workflow begins at the stage of developing and annotating a fictional scenario with actors, to producing a video emotion corpus, to designing training and validating a neural network, to analyzing the emotion data annotation of the corpus and neural network, and finally to determining resemblant behavior of its autonomous animation control of a 3d character facial mesh. The resulting workflow includes a method for the development of a neural network architecture whose initial efficacy as a facial emotion expression simulator has been tested and validated as substantially resemblant to the character behavior developed by a human actor
A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation
Body language (BL) refers to the non-verbal communication expressed through
physical movements, gestures, facial expressions, and postures. It is a form of
communication that conveys information, emotions, attitudes, and intentions
without the use of spoken or written words. It plays a crucial role in
interpersonal interactions and can complement or even override verbal
communication. Deep multi-modal learning techniques have shown promise in
understanding and analyzing these diverse aspects of BL. The survey emphasizes
their applications to BL generation and recognition. Several common BLs are
considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and
Talking Head (TH), and we have conducted an analysis and established the
connections among these four BL for the first time. Their generation and
recognition often involve multi-modal approaches. Benchmark datasets for BL
research are well collected and organized, along with the evaluation of SOTA
methods on these datasets. The survey highlights challenges such as limited
labeled data, multi-modal learning, and the need for domain adaptation to
generalize models to unseen speakers or languages. Future research directions
are presented, including exploring self-supervised learning techniques,
integrating contextual information from other modalities, and exploiting
large-scale pre-trained multi-modal models. In summary, this survey paper
provides a comprehensive understanding of deep multi-modal learning for various
BL generations and recognitions for the first time. By analyzing advancements,
challenges, and future directions, it serves as a valuable resource for
researchers and practitioners in advancing this field. n addition, we maintain
a continuously updated paper list for deep multi-modal learning for BL
recognition and generation: https://github.com/wentaoL86/awesome-body-language
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