4,289 research outputs found
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks such as
object pose estimation when the input image is of high quality. However, in
challenging imaging conditions such as on low-resolution images or when the
image is corrupted by imaging artifacts, current systems degrade considerably
in accuracy. While a loss in performance is unavoidable, we would like our
models to quantify their uncertainty in order to achieve robustness against
images of varying quality. Probabilistic deep learning models combine the
expressive power of deep learning with uncertainty quantification. In this
paper, we propose a novel probabilistic deep learning model for the task of
angular regression. Our model uses von Mises distributions to predict a
distribution over object pose angle. Whereas a single von Mises distribution is
making strong assumptions about the shape of the distribution, we extend the
basic model to predict a mixture of von Mises distributions. We show how to
learn a mixture model using a finite and infinite number of mixture components.
Our model allows for likelihood-based training and efficient inference at test
time. We demonstrate on a number of challenging pose estimation datasets that
our model produces calibrated probability predictions and competitive or
superior point estimates compared to the current state-of-the-art
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore their eyes are not always visible. Consequently both gaze
and VFOA estimation cannot be based on eye detection and tracking. We propose a
method that exploits the correlation between eye gaze and head movements. Both
VFOA and gaze are modeled as latent variables in a Bayesian switching
state-space model. The proposed formulation leads to a tractable learning
procedure and to an efficient algorithm that simultaneously tracks gaze and
visual focus. The method is tested and benchmarked using two publicly available
datasets that contain typical multi-party human-robot and human-human
interactions.Comment: 15 pages, 8 figures, 6 table
Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control
We present Free-HeadGAN, a person-generic neural talking head synthesis
system. We show that modeling faces with sparse 3D facial landmarks are
sufficient for achieving state-of-the-art generative performance, without
relying on strong statistical priors of the face, such as 3D Morphable Models.
Apart from 3D pose and facial expressions, our method is capable of fully
transferring the eye gaze, from a driving actor to a source identity. Our
complete pipeline consists of three components: a canonical 3D key-point
estimator that regresses 3D pose and expression-related deformations, a gaze
estimation network and a generator that is built upon the architecture of
HeadGAN. We further experiment with an extension of our generator to
accommodate few-shot learning using an attention mechanism, in case more than
one source images are available. Compared to the latest models for reenactment
and motion transfer, our system achieves higher photo-realism combined with
superior identity preservation, while offering explicit gaze control
Photo-realistic face synthesis and reenactment with deep generative models
The advent of Deep Learning has led to numerous breakthroughs in the field of Computer Vision. Over the last decade, a significant amount of research has been undertaken towards designing neural networks for visual data analysis. At the same time, rapid advancements have been made towards the direction of deep generative modeling, especially after the introduction of Generative Adversarial Networks (GANs), which have shown particularly promising results when it comes to synthesising visual data. Since then, considerable attention has been devoted to the problem of photo-realistic human face animation due to its wide range of applications, including image and video editing, virtual assistance, social media, teleconferencing, and augmented reality. The objective of this thesis is to make progress towards generating photo-realistic videos of human faces. To that end, we propose novel generative algorithms that provide explicit control over the facial expression and head pose of synthesised subjects. Despite the major advances in face reenactment and motion transfer, current methods struggle to generate video portraits that are indistinguishable from real data. In this work, we aim to overcome the limitations of existing approaches, by combining concepts from deep generative networks and video-to-video translation with 3D face modelling, and more specifically by capitalising on prior knowledge of faces that is enclosed within statistical models such as 3D Morphable Models (3DMMs). In the first part of this thesis, we introduce a person-specific system that performs full head reenactment using ideas from video-to-video translation. Subsequently, we propose a novel approach to controllable video portrait synthesis, inspired from Implicit Neural Representations (INR). In the second part of the thesis, we focus on person-agnostic methods and present a GAN-based framework that performs video portrait reconstruction, full head reenactment, expression editing, novel pose synthesis and face frontalisation.Open Acces
Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns
This paper presents few-shot personalized saliency prediction based on
inter-personnel gaze patterns. In contrast to a general saliency map, a
personalized saliecny map (PSM) has been great potential since its map
indicates the person-specific visual attention that is useful for obtaining
individual visual preferences from heterogeneity of gazed areas. The PSM
prediction is needed for acquiring the PSM for the unseen image, but its
prediction is still a challenging task due to the complexity of individual gaze
patterns. For modeling individual gaze patterns for various images, although
the eye-tracking data obtained from each person is necessary to construct PSMs,
it is difficult to acquire the massive amounts of such data. Here, one solution
for efficient PSM prediction from the limited amount of data can be the
effective use of eye-tracking data obtained from other persons. In this paper,
to effectively treat the PSMs of other persons, we focus on the effective
selection of images to acquire eye-tracking data and the preservation of
structural information of PSMs of other persons. In the experimental results,
we confirm that the above two focuses are effective for the PSM prediction with
the limited amount of eye-tracking data.Comment: 5pages, 3 figure
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