Improvements on a simple muscle-based 3D face for realistic facial expressions

Facial expressions play an important role in face-to-face communication. With the development of personal computers capable of rendering high quality graphics, computer facial animation has produced more and more realistic facial expressions to enrich human-computer communication. In this paper, we present a simple muscle-based 3D face model that can produce realistic facial expressions in real time. We extend Waters' (1987) muscle model to generate bulges and wrinkles and to improve the combination of multiple muscle actions. In addition, we present techniques to reduce the computation burden on the muscle mode

A Survey of Computer Graphics Facial Animation Methods: Comparing Traditional Approaches to Machine Learning Methods

Human communications rely on facial expression to denote mood, sentiment, and intent. Realistic facial animation of computer graphic models of human faces can be difficult to achieve as a result of the many details that must be approximated in generating believable facial expressions. Many theoretical approaches have been researched and implemented to create more and more accurate animations that can effectively portray human emotions. Even though many of these approaches are able to generate realistic looking expressions, they typically require a lot of artistic intervention to achieve a believable result. To reduce the intervention needed to create realistic facial animation, new approaches that utilize machine learning are being researched to reduce the amount of effort needed to generate believable facial animations. This survey paper summarizes over 20 research papers related to facial animation and compares the traditional animation approaches to newer machine learning methods as well as highlights the strengths, weaknesses, and use cases of each different approach

Learning to Dress {3D} People in Generative Clothing

Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is.tue.mpg.de.Comment: CVPR-2020 camera ready. Code and data are available at https://cape.is.tue.mpg.d

THREE DIMENSIONAL MODELING AND ANIMATION OF FACIAL EXPRESSIONS

Facial expression and animation are important aspects of the 3D environment featuring human characters. These animations are frequently used in many kinds of applications and there have been many efforts to increase the realism. Three aspects are still stimulating active research: the detailed subtle facial expressions, the process of rigging a face, and the transfer of an expression from one person to another. This dissertation focuses on the above three aspects. A system for freely designing and creating detailed, dynamic, and animated facial expressions is developed. The presented pattern functions produce detailed and animated facial expressions. The system produces realistic results with fast performance, and allows users to directly manipulate it and see immediate results. Two unique methods for generating real-time, vivid, and animated tears have been developed and implemented. One method is for generating a teardrop that continually changes its shape as the tear drips down the face. The other is for generating a shedding tear, which is a kind of tear that seamlessly connects with the skin as it flows along the surface of the face, but remains an individual object. The methods both broaden CG and increase the realism of facial expressions. A new method to automatically set the bones on facial/head models to speed up the rigging process of a human face is also developed. To accomplish this, vertices that describe the face/head as well as relationships between each part of the face/head are grouped. The average distance between pairs of vertices is used to place the head bones. To set the bones in the face with multi-density, the mean value of the vertices in a group is measured. The time saved with this method is significant. A novel method to produce realistic expressions and animations by transferring an existing expression to a new facial model is developed. The approach is to transform the source model into the target model, which then has the same topology as the source model. The displacement vectors are calculated. Each vertex in the source model is mapped to the target model. The spatial relationships of each mapped vertex are constrained

Physically-based forehead animation including wrinkles

Physically-based animation techniques enable more realistic and accurate animation to be created. We present a fully physically-based approach for efficiently producing realistic-looking animations of facial movement, including animation of expressive wrinkles. This involves simulation of detailed voxel-based models using a graphics processing unit-based total Lagrangian explicit dynamic finite element solver with an anatomical muscle contraction model, and advanced boundary conditions that can model the sliding of soft tissue over the skull. The flexibility of our approach enables detailed animations of gross and fine-scale soft-tissue movement to be easily produced with different muscle structures and material parameters, for example, to animate different aged skins. Although we focus on the forehead, our approach can be used to animate any multi-layered soft body

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structure-aware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation based on the distance field of wrinkle lines, jointly modeled with detail displacements to establish better correspondences and enable intuitive manipulation of wrinkle structure. Besides, SEMM introduces two transformation modules to translate expression blendshape weights and age values into changes in latent space, allowing effective semantic detail editing while maintaining identity. Extensive experiments demonstrate that the proposed model compactly represents facial details, outperforms previous methods in expression animation qualitatively and quantitatively, and achieves effective age editing and wrinkle line editing of facial details. Code and model are available at https://github.com/gerwang/facial-detail-manipulation.Comment: ECCV 202

Modeling of Facial Wrinkles for Applications in Computer Vision

International audienceAnalysis and modeling of aging human faces have been extensively studied in the past decade for applications in computer vision such as age estimation, age progression and face recognition across aging. Most of this research work is based on facial appearance and facial features such as face shape, geometry, location of landmarks and patch-based texture features. Despite the recent availability of higher resolution, high quality facial images, we do not find much work on the image analysis of local facial features such as wrinkles specifically. For the most part, modeling of facial skin texture, fine lines and wrinkles has been a focus in computer graphics research for photo-realistic rendering applications. In computer vision, very few aging related applications focus on such facial features. Where several survey papers can be found on facial aging analysis in computer vision, this chapter focuses specifically on the analysis of facial wrinkles in the context of several applications. Facial wrinkles can be categorized as subtle discontinuities or cracks in surrounding inhomogeneous skin texture and pose challenges to being detected/localized in images. First, we review commonly used image features to capture the intensity gradients caused by facial wrinkles and then present research in modeling and analysis of facial wrinkles as aging texture or curvilinear objects for different applications. The reviewed applications include localization or detection of wrinkles in facial images , incorporation of wrinkles for more realistic age progression, analysis for age estimation and inpainting/removal of wrinkles for facial retouching

Dynamic Facial Emotion Recognition Oriented to HCI Applications

Producción CientíficaAs part of a multimodal animated interface previously presented in [38], in this paper we describe a method for dynamic recognition of displayed facial emotions on low resolution streaming images. First, we address the detection of Action Units of the Facial Action Coding System upon Active Shape Models and Gabor filters. Normalized outputs of the Action Unit recognition step are then used as inputs for a neural network which is based on real cognitive systems architecture, and consists on a habituation network plus a competitive network. Both the competitive and the habituation layer use differential equations thus taking into account the dynamic information of facial expressions through time. Experimental results carried out on live video sequences and on the Cohn-Kanade face database show that the proposed method provides high recognition hit rates.Junta de Castilla y León (Programa de apoyo a proyectos de investigación-Ref. VA036U14)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA013A12-2)Ministerio de Economía, Industria y Competitividad (Grant DPI2014-56500-R