43,936 research outputs found
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
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image preprocessing, model initialization and architecture choice. We present a
study investigating these different effects.
In detail, our work compares four popular neural network architectures,
studies the effect of pretraining, evaluates the robustness of the considered
alignment preprocessings via cross-method test set swapping and intuitively
visualizes the model's prediction strategies in given preprocessing conditions
using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our
evaluations on the challenging Adience benchmark show that suitable parameter
initialization leads to a holistic perception of the input, compensating
artefactual data representations. With a combination of simple preprocessing
steps, we reach state of the art performance in gender recognition.Comment: 8 pages, 5 figures, 5 tables. Presented at ICCV 2017 Workshop: 7th
IEEE International Workshop on Analysis and Modeling of Faces and Gesture
A motion system for social and animated robots
This paper presents an innovative motion system that is used to control the motions and animations of a social robot. The social robot Probo is used to study Human-Robot Interactions (HRI), with a special focus on Robot Assisted Therapy (RAT). When used for therapy it is important that a social robot is able to create an "illusion of life" so as to become a believable character that can communicate with humans. The design of the motion system in this paper is based on insights from the animation industry. It combines operator-controlled animations with low-level autonomous reactions such as attention and emotional state. The motion system has a Combination Engine, which combines motion commands that are triggered by a human operator with motions that originate from different units of the cognitive control architecture of the robot. This results in an interactive robot that seems alive and has a certain degree of "likeability". The Godspeed Questionnaire Series is used to evaluate the animacy and likeability of the robot in China, Romania and Belgium
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Smile asymmetries and reputation as reliable indicators of likelihood to cooperate: An evolutionary analysis
Cooperating with individuals whose altruism is not motivated by genuine prosocial emotions could have been costly in ancestral division of labour partnerships. How do humans âknowâ whether or not an individual has the prosocial emotions committing future cooperation? Frank (1988) has hypothesized two pathways for altruist-detection: (a) facial expressions of emotions signalling character; and (b) gossip regarding the target individualâs reputation. Detecting non-verbal cues signalling commitment to cooperate may be one way to avoid the costs of exploitation. Spontaneous smiles while cooperating may be reliable index cues because of the physiological constraints controlling the neural pathways mediating involuntary emotional expressions. Specifically, it is hypothesized that individuals whose help is mediated by a genuine sympathy will express involuntary smiles (which are observably different from posed smiles). To investigate this idea, 38 participants played dictator games (i.e. a unilateral resource allocation task) against cartoon faces with a benevolent emotional expression (i.e. concern furrows and smile). The faces were presented with information regarding reputation (e.g. descriptions of an altruistic character vs. a non-altruistic character). Half of the sample played against icons with symmetrical smiles (representing a spontaneous smile) while the other half played against asymmetrically smiling icons (representing a posed smile). Icons described as having altruistic motives received more resources than icons described as self-interested helpers. Faces with symmetrical smiles received more resources than faces with asymmetrical smiles. These results suggest that reputation and smile asymmetry influence the likelihood of cooperation and thus may be reliable cues to altruism. These cues may allow for altruists to garner more resources in division of labour situations
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Human preferences for sexually dimorphic faces may be evolutionarily novel
This article has been made available through the Brunel Open Access Publishing Fund.A large literature proposes that preferences for exaggerated sex typicality in human faces (masculinity/femininity) reflect a long evolutionary history of sexual and social selection. This proposal implies that dimorphism was important to judgments of attractiveness and personality in ancestral environments. It is difficult to evaluate, however, because most available data come from largescale, industrialized, urban populations. Here, we report the results for 12 populations with very diverse levels of economic development. Surprisingly, preferences for exaggerated sex-specific traits are only found in the novel, highly developed environments. Similarly, perceptions that masculine males look aggressive increase strongly with development, specifically, urbanization. These data challenge the hypothesis that facial dimorphism was an important ancestral signal of heritable mate value. One possibility is that highly developed environments provide novel opportunities to discern relationships between facial traits and behavior by exposing individuals to large numbers of unfamiliar faces, revealing patterns too subtle to detect with smaller samples
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