10,039 research outputs found
Speech-driven Animation with Meaningful Behaviors
Conversational agents (CAs) play an important role in human computer
interaction. Creating believable movements for CAs is challenging, since the
movements have to be meaningful and natural, reflecting the coupling between
gestures and speech. Studies in the past have mainly relied on rule-based or
data-driven approaches. Rule-based methods focus on creating meaningful
behaviors conveying the underlying message, but the gestures cannot be easily
synchronized with speech. Data-driven approaches, especially speech-driven
models, can capture the relationship between speech and gestures. However, they
create behaviors disregarding the meaning of the message. This study proposes
to bridge the gap between these two approaches overcoming their limitations.
The approach builds a dynamic Bayesian network (DBN), where a discrete variable
is added to constrain the behaviors on the underlying constraint. The study
implements and evaluates the approach with two constraints: discourse functions
and prototypical behaviors. By constraining on the discourse functions (e.g.,
questions), the model learns the characteristic behaviors associated with a
given discourse class learning the rules from the data. By constraining on
prototypical behaviors (e.g., head nods), the approach can be embedded in a
rule-based system as a behavior realizer creating trajectories that are timely
synchronized with speech. The study proposes a DBN structure and a training
approach that (1) models the cause-effect relationship between the constraint
and the gestures, (2) initializes the state configuration models increasing the
range of the generated behaviors, and (3) captures the differences in the
behaviors across constraints by enforcing sparse transitions between shared and
exclusive states per constraint. Objective and subjective evaluations
demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
Gestures that accompany speech are an essential part of natural and efficient
embodied human communication. The automatic generation of such co-speech
gestures is a long-standing problem in computer animation and is considered an
enabling technology in film, games, virtual social spaces, and for interaction
with social robots. The problem is made challenging by the idiosyncratic and
non-periodic nature of human co-speech gesture motion, and by the great
diversity of communicative functions that gestures encompass. Gesture
generation has seen surging interest recently, owing to the emergence of more
and larger datasets of human gesture motion, combined with strides in
deep-learning-based generative models, that benefit from the growing
availability of data. This review article summarizes co-speech gesture
generation research, with a particular focus on deep generative models. First,
we articulate the theory describing human gesticulation and how it complements
speech. Next, we briefly discuss rule-based and classical statistical gesture
synthesis, before delving into deep learning approaches. We employ the choice
of input modalities as an organizing principle, examining systems that generate
gestures from audio, text, and non-linguistic input. We also chronicle the
evolution of the related training data sets in terms of size, diversity, motion
quality, and collection method. Finally, we identify key research challenges in
gesture generation, including data availability and quality; producing
human-like motion; grounding the gesture in the co-occurring speech in
interaction with other speakers, and in the environment; performing gesture
evaluation; and integration of gesture synthesis into applications. We
highlight recent approaches to tackling the various key challenges, as well as
the limitations of these approaches, and point toward areas of future
development.Comment: Accepted for EUROGRAPHICS 202
Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding
Modeling virtual agents with behavior style is one factor for personalizing
human agent interaction. We propose an efficient yet effective machine learning
approach to synthesize gestures driven by prosodic features and text in the
style of different speakers including those unseen during training. Our model
performs zero shot multimodal style transfer driven by multimodal data from the
PATS database containing videos of various speakers. We view style as being
pervasive while speaking, it colors the communicative behaviors expressivity
while speech content is carried by multimodal signals and text. This
disentanglement scheme of content and style allows us to directly infer the
style embedding even of speaker whose data are not part of the training phase,
without requiring any further training or fine tuning. The first goal of our
model is to generate the gestures of a source speaker based on the content of
two audio and text modalities. The second goal is to condition the source
speaker predicted gestures on the multimodal behavior style embedding of a
target speaker. The third goal is to allow zero shot style transfer of speakers
unseen during training without retraining the model. Our system consists of:
(1) a speaker style encoder network that learns to generate a fixed dimensional
speaker embedding style from a target speaker multimodal data and (2) a
sequence to sequence synthesis network that synthesizes gestures based on the
content of the input modalities of a source speaker and conditioned on the
speaker style embedding. We evaluate that our model can synthesize gestures of
a source speaker and transfer the knowledge of target speaker style variability
to the gesture generation task in a zero shot setup. We convert the 2D gestures
to 3D poses and produce 3D animations. We conduct objective and subjective
evaluations to validate our approach and compare it with a baseline
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
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An evaluation of constructivism for learners with ADHD: Development of a constructivist pedagogy for special needs
We examine whether constructivist eLearning tools can be used to help learners cope with special educational needs, such as difficulties with attention and concentration. Preliminary work is reported here, in which we seek to determine the reasons why a constructivist approach is difficult for learners with ADHD. This work is intended to lead to recommendations of how learners with ADHD could benefit from constructivist eLearning systems, e.g. through the managed use of multimedia technology. A preliminary model has been developed that illustrates the areas in which constructivist pedagogies need to address the limitations of ADHD learners. Further work will expand this model and eventually test it in a real environment (e.g. in a school with ADHD learners). The outcome will encourage a reconsideration of existing multimedia theories as they relate to learners with special needs, and provide new directions in order to support learners with ADHD
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