16,376 research outputs found
Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations
Audio-driven talking face generation, which aims to synthesize talking faces
with realistic facial animations (including accurate lip movements, vivid
facial expression details and natural head poses) corresponding to the audio,
has achieved rapid progress in recent years. However, most existing work
focuses on generating lip movements only without handling the closely
correlated facial expressions, which degrades the realism of the generated
faces greatly. This paper presents DIRFA, a novel method that can generate
talking faces with diverse yet realistic facial animations from the same
driving audio. To accommodate fair variation of plausible facial animations for
the same audio, we design a transformer-based probabilistic mapping network
that can model the variational facial animation distribution conditioned upon
the input audio and autoregressively convert the audio signals into a facial
animation sequence. In addition, we introduce a temporally-biased mask into the
mapping network, which allows to model the temporal dependency of facial
animations and produce temporally smooth facial animation sequence. With the
generated facial animation sequence and a source image, photo-realistic talking
faces can be synthesized with a generic generation network. Extensive
experiments show that DIRFA can generate talking faces with realistic facial
animations effectively
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
Facial expression analysis based on machine learning requires large number of
well-annotated data to reflect different changes in facial motion. Publicly
available datasets truly help to accelerate research in this area by providing
a benchmark resource, but all of these datasets, to the best of our knowledge,
are limited to rough annotations for action units, including only their
absence, presence, or a five-level intensity according to the Facial Action
Coding System. To meet the need for videos labeled in great detail, we present
a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D
Facial Animation. One hundred and twenty-two participants, including children,
young adults and elderly people, were recorded in real-world conditions. In
addition, 99,356 frames were manually labeled using Expression Quantitative
Tool developed by us to quantify 9 symmetrical FACS action units, 10
asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action
descriptors and 2 asymmetrical FACS action descriptors, and each action unit or
action descriptor is well-annotated with a floating point number between 0 and
1. To provide a baseline for use in future research, a benchmark for the
regression of action unit values based on Convolutional Neural Networks are
presented. We also demonstrate the potential of our FEAFA dataset for 3D facial
animation. Almost all state-of-the-art algorithms for facial animation are
achieved based on 3D face reconstruction. We hence propose a novel method that
drives virtual characters only based on action unit value regression of the 2D
video frames of source actors.Comment: 9 pages, 7 figure
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and factors of variation are highly controlled. However, labeled
examples of facial expressions, emotions and key-points for new individuals are
difficult and costly to obtain. In this paper we improve the ability of
techniques to generalize to new and unseen individuals by explicitly modeling
previously seen variations related to identity and expression. We use a
weakly-supervised approach in which identity labels are used to learn the
different factors of variation linked to identity separately from factors
related to expression. We show how probabilistic modeling of these sources of
variation allows one to learn identity-invariant representations for
expressions which can then be used to identity-normalize various procedures for
facial expression analysis and animation control. We also show how to extend
the widely used techniques of active appearance models and constrained local
models through replacing the underlying point distribution models which are
typically constructed using principal component analysis with
identity-expression factorized representations. We present a wide variety of
experiments in which we consistently improve performance on emotion
recognition, markerless performance-driven facial animation and facial
key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Face performance capture and reenactment techniques use multiple cameras and sensors, positioned at a distance from the face or mounted on heavy wearable devices. This limits their applications in mobile and outdoor environments. We present EgoFace, a radically new lightweight setup for face performance capture and front-view videorealistic reenactment using a single egocentric RGB camera. Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments. The input image is projected into a low dimensional latent space of the facial expression parameters. Through careful adversarial training of the parameter-space synthetic rendering, a videorealistic animation is produced. Our problem is challenging as the human visual system is sensitive to the smallest face irregularities that could occur in the final results. This sensitivity is even stronger for video results. Our solution is trained in a pre-processing stage, through a supervised manner without manual annotations. EgoFace captures a wide variety of facial expressions, including mouth movements and asymmetrical expressions. It works under varying illuminations, background, movements, handles people from different ethnicities and can operate in real time
Investigating facial animation production through artistic inquiry
Studies into dynamic facial expressions tend to make use of experimental methods based on objectively manipulated stimuli. New techniques for displaying increasingly realistic facial movement and methods of measuring observer responses are typical of computer animation and psychology facial expression research. However, few projects focus on the artistic nature of performance production. Instead, most concentrate on the naturalistic appearance of posed or acted expressions. In this paper, the authors discuss a method for exploring the creative process of emotional facial expression animation, and ask whether anything can be learned about authentic dynamic expressions through artistic inquiry
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Parametric Representations of Facial Expressions on PDE-Based Surfaces
NoParameterisation of facial expressions on PDE surface
representations of human faces are presented in this
work. Taking advantage of the boundary-value approach
inherent to Bloor-Wilson PDE method, facial expressions
are achieved by manipulating the original boundary curves.
Such curves are responsible for generating a surface representation
of a human face in its neutral configuration,
so that regions on these curves represent a given facial
expression in a fast and realistic manner. Additionally, the
parameterisation proposed here is carried out by applying
different mathematical transformations to the affected
curves according to the corresponding facial expression.
Full analytic expressions parameterising some of the most
common facial expressions such as smiling and eyebrow
raising are in this work. Some graphical examples of these
facial expressions are used to illustrate the results obtained
using Bloor-Wilson PDE method as the foundations of the
parameterisation scheme proposed here. Thus, it is shown
that an efficient, intuitive and realistic parameterisation of
facial expressions is attainable using Bloor-Wilson PDE
method in along with a suitable mathematical expression
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PDE-based Facial Animation: Making the Complex Simple
YesDirect parameterisation is among the most widely used facial animation techniques but requires complicated ways to animate face models which have complex topology. This paper develops a simple solution by introducing a PDE-based facial animation scheme. Using a PDE face model means we only need to animate a group of boundary curves without using any other conventional surface interpolation algorithms. We describe the basis of the method and show results from a practical implementation.EPSR
Considerations for believable emotional facial expression animation
Facial expressions can be used to communicate emotional states through the use of universal signifiers within key regions of the face. Psychology research has identified what these signifiers are and how different combinations and variations can be interpreted. Research into expressions has informed animation practice, but as yet very little is known about the movement within and between emotional expressions. A better understanding of sequence, timing, and duration could better inform the production of believable animation. This paper introduces the idea of expression choreography, and how tests of observer perception might enhance our understanding of moving emotional expressions
A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment
We describe the design and implementation of a vision based interactive
entertainment system that makes use of both involuntary and voluntary control
paradigms. Unintentional input to the system from a potential viewer is used to
drive attention-getting output and encourage the transition to voluntary
interactive behaviour. The iMime system consists of a character animation
engine based on the interaction metaphor of a mime performer that simulates
non-verbal communication strategies, without spoken dialogue, to capture and
hold the attention of a viewer. The system was developed in the context of a
project studying care of dementia sufferers. Care for a dementia sufferer can
place unreasonable demands on the time and attentional resources of their
caregivers or family members. Our study contributes to the eventual development
of a system aimed at providing relief to dementia caregivers, while at the same
time serving as a source of pleasant interactive entertainment for viewers. The
work reported here is also aimed at a more general study of the design of
interactive entertainment systems involving a mixture of voluntary and
involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho
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