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We propose an efficient self-shadowing illumination model for Morphable Models. Simulating self-shadowing with ray casting is computationally expensive which makes them impractical in Analysis-by-Synthesis methods for object reconstruction from single images. Therefore, we propose to learn self-shadowing for Morphable Model parameters directly with a linear model. Radiance transfer functions are a...
Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep...
Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNNs, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted...
In this paper, the face modeling problem, a random forest model on each feature point by pixel difference feature, by regression estimation of forest model shape training samples; to estimate the shape of training samples for linear least squares fitting and real shape, a global optimization model; and then use the model to test the sample feature point location regression estimation and shape optimization,...
Virtual characters play a central role in populating virtual worlds, whether they act as conduits for human expressions as avatars or are automatically controlled by a machine as agents. In modern game-related scenarios, it is economical to assemble virtual characters from varying sources of appearances and motions. However, doing so may have unintended consequences with respect to how people perceive...
Nowadays there is a big interest in applying natural gesture communication language to various systems. There are several techniques on how to detect willed hand motion or recognize gestures by using different kind of sensors. RGB cameras can be used to detect hand, but it has a limited application. For example, hand detection can be particularly hard in some lighting conditions or in different skin...
The changes in facial appearance resulting from the effect of plastic surgery may manifest in the form of textural variations, and/or geometric variations with respect to the facial structural features such as eyes, nose, mouth, jaw, nose, brow, etc. This study argues that despite these variations, there exist facial features that are insensitive to the effect of plastic surgery. This paper therefore...
Although point cloud data (PCD) are easily measured using a RGB-Depth sensor such as Kinect and Xtion, measured PCD have undesirable noise and fluctuation of values. In this paper, a curved surface fitting method using a raster-scanning window is proposed to smooth original organized PCD with noise. The method allows PCD to be fitted to numerous small quadratic curved surfaces and to be smoothed while...
In this paper, we address the problem of gender classification based on facial images. The Speeded Up Robust Feature (SURF) algorithm descriptors are used as features to built dictionaries and a multi-task Sparse Representation Classification (SRC) is used as classifier to determine the gender of an individual face. Our approach uses smaller and compact dictionaries by removing the redundant atoms...
It is not only interesting to predict how an individual of a relatively young age will look in the future but also to reconstruct the facial appearance in the past during childhood. It can be even more desirable when different circumstances, behavior and lifestyle and their impacts on the facial shape appearance as a consequence are taken into account. Such may be applicable for many practical reasons...
Facial expression recognition is an active research area in the field of signal social processing. The goal is to distinguish human emotion. The problem is similar emotion, variation of emotion, and independent object through face image. The existing research using various method for modeling human facial to entirely describe facial expression through face image. We consider to variation analysis...
In order to better learn the distributions of 2D and 3D faces and the mapping between them with limited training samples, a new 3D face reconstruction method based on progressive cascade regression is proposed. Firstly, it learns the mapping between 2D and 3D facial landmarks to estimate the initial 3D facial landmarks with a coupled space learning method. Secondly, a deformed space is constructed...
While hand geometry trait has been widely used to perform biometric recognition, majority of the methods employ images acquired against a uniform background. If segmentation of the hand is implemented, existing techniques can be used in cluttered backgrounds as well. This paper presents an approach for accurate segmentation of human hands for images following the aforementioned conditions using skin...
3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful...
Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB...
We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Unlike the previous depth-based discriminative or data-driven methods that require sophisticated training or manual intervention, we propose a generative framework that unifies pose tracking and face model adaptation on-the-fly. Particularly,...
We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild. After an initial estimation of shape and low-frequency albedo, we compute a high-frequency partial texture map, without the shading component, of the visible face area. To extract the fine appearance details from this incomplete input,...
In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the task of estimating facial surface normals in-the-wild. We train a fully convolutional...
In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for...
Pre-learnt subspace methods, e.g., 3DMMs, are significant exploration for the synthesis of 3D faces by assuming that faces are in a linear class. However, the human face is in a nonlinear manifold, and a new test are always not in the pre-learnt subspace accurately because of the disparity brought by ethnicity, age, gender, etc. In the paper, we propose a parametric T-spline morphable model (T-splineMM)...
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