6,303 research outputs found
Towards responsive Sensitive Artificial Listeners
This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness
Learning Speech-driven 3D Conversational Gestures from Video
We propose the first approach to automatically and jointly synthesize both
the synchronous 3D conversational body and hand gestures, as well as 3D face
and head animations, of a virtual character from speech input. Our algorithm
uses a CNN architecture that leverages the inherent correlation between facial
expression and hand gestures. Synthesis of conversational body gestures is a
multi-modal problem since many similar gestures can plausibly accompany the
same input speech. To synthesize plausible body gestures in this setting, we
train a Generative Adversarial Network (GAN) based model that measures the
plausibility of the generated sequences of 3D body motion when paired with the
input audio features. We also contribute a new way to create a large corpus of
more than 33 hours of annotated body, hand, and face data from in-the-wild
videos of talking people. To this end, we apply state-of-the-art monocular
approaches for 3D body and hand pose estimation as well as dense 3D face
performance capture to the video corpus. In this way, we can train on orders of
magnitude more data than previous algorithms that resort to complex in-studio
motion capture solutions, and thereby train more expressive synthesis
algorithms. Our experiments and user study show the state-of-the-art quality of
our speech-synthesized full 3D character animations
The Many Moods of Emotion
This paper presents a novel approach to the facial expression generation
problem. Building upon the assumption of the psychological community that
emotion is intrinsically continuous, we first design our own continuous emotion
representation with a 3-dimensional latent space issued from a neural network
trained on discrete emotion classification. The so-obtained representation can
be used to annotate large in the wild datasets and later used to trained a
Generative Adversarial Network. We first show that our model is able to map
back to discrete emotion classes with a objectively and subjectively better
quality of the images than usual discrete approaches. But also that we are able
to pave the larger space of possible facial expressions, generating the many
moods of emotion. Moreover, two axis in this space may be found to generate
similar expression changes as in traditional continuous representations such as
arousal-valence. Finally we show from visual interpretation, that the third
remaining dimension is highly related to the well-known dominance dimension
from psychology
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