5,660 research outputs found
Computer facial animation: a review
Computer facial animation is not a new endeavour as it had been introduced since 1970s. However, animating human face
still presents interesting challenges because of its
familiarity as the face is the part used to recognize individuals.Facial modelling and facial animation are important in developing realistic computer facial animation. Both modelling and animation is dependent to drive the animation.This paper reviews several geometric
-based modelling (shape interpolation,parameterization and muscle-based animation)and data-driven animation (image-based techniques speech-driven techniques and performance-driven animation) techniques used in computer graphics and vision for facial animation. The main concept
s and problems for each technique are highlighted in the
paper
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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
Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers
We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality
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
ICface: Interpretable and Controllable Face Reenactment Using GANs
This paper presents a generic face animator that is able to control the pose
and expressions of a given face image. The animation is driven by human
interpretable control signals consisting of head pose angles and the Action
Unit (AU) values. The control information can be obtained from multiple sources
including external driving videos and manual controls. Due to the interpretable
nature of the driving signal, one can easily mix the information between
multiple sources (e.g. pose from one image and expression from another) and
apply selective post-production editing. The proposed face animator is
implemented as a two-stage neural network model that is learned in a
self-supervised manner using a large video collection. The proposed
Interpretable and Controllable face reenactment network (ICface) is compared to
the state-of-the-art neural network-based face animation techniques in multiple
tasks. The results indicate that ICface produces better visual quality while
being more versatile than most of the comparison methods. The introduced model
could provide a lightweight and easy to use tool for a multitude of advanced
image and video editing tasks.Comment: Accepted in WACV-202
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
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
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