Speech Enabled Avatar from a Single Photograph

This paper presents a complete framework for creating speech-enabled 2D and 3D avatars from a single image of a person. Our approach uses a generic facial motion model which represents deformations of the prototype face during speech. We have developed an HMM-based facial animation algorithm which takes into account both lexical stress and coarticulation. This algorithm produces realistic animations of the prototype facial surface from either text or speech. The generic facial motion model is transformed to a novel face geometry using a set of corresponding points between the generic mesh and the novel face. In the case of a 2D avatar, a single photograph of the person is used as input. We manually select a small number of features on the photograph and these are used to deform the prototype surface. The deformed surface is then used to animate the photograph. In the case of a 3D avatar, we use a single stereo image of the person as input. The sparse geometry of the face is computed from this image and used to warp the prototype surface to obtain the complete 3D surface of the person's face. This surface is etched into a glass cube using sub-surface laser engraving (SSLE) technology. Synthesized facial animation videos are then projected onto the etched glass cube. Even though the etched surface is static, the projection of facial animation onto it results in a compelling experience for the viewer. We show several examples of 2D and 3D avatars that are driven by text and speech inputs

Highly automated method for facial expression synthesis

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The synthesis of realistic facial expressions has been an unexplored area for computer graphics scientists. Over the last three decades, several different construction methods have been formulated in order to obtain natural graphic results. Despite these advancements, though, current techniques still require costly resources, heavy user intervention and specific training and outcomes are still not completely realistic. This thesis, therefore, aims to achieve an automated synthesis that will produce realistic facial expressions at a low cost. This thesis, proposes a highly automated approach for achieving a realistic facial expression synthesis, which allows for enhanced performance in speed (3 minutes processing time maximum) and quality with a minimum of user intervention. It will also demonstrate a highly technical and automated method of facial feature detection, by allowing users to obtain their desired facial expression synthesis with minimal physical input. Moreover, it will describe a novel approach to the normalization of the illumination settings values between source and target images, thereby allowing the algorithm to work accurately, even in different lighting conditions. Finally, we will present the results obtained from the proposed techniques, together with our conclusions, at the end of the paper

Analysis and Construction of Engaging Facial Forms and Expressions: Interdisciplinary Approaches from Art, Anatomy, Engineering, Cultural Studies, and Psychology

The topic of this dissertation is the anatomical, psychological, and cultural examination of a human face in order to effectively construct an anatomy-driven 3D virtual face customization and action model. In order to gain a broad perspective of all aspects of a face, theories and methodology from the fields of art, engineering, anatomy, psychology, and cultural studies have been analyzed and implemented. The computer generated facial customization and action model were designed based on the collected data. Using this customization system, culturally-specific attractive face in Korean popular culture, “kot-mi-nam (flower-like beautiful guy),” was modeled and analyzed as a case study. The “kot-mi-nam” phenomenon is overviewed in textual, visual, and contextual aspects, which reveals the gender- and sexuality-fluidity of its masculinity. The analysis and the actual development of the model organically co-construct each other requiring an interwoven process. Chapter 1 introduces anatomical studies of a human face, psychological theories of face recognition and an attractive face, and state-of-the-art face construction projects in the various fields. Chapter 2 and 3 present the Bezier curve-based 3D facial customization (BCFC) and Multi-layered Facial Action Model (MFAF) based on the analysis of human anatomy, to achieve a cost-effective yet realistic quality of facial animation without using 3D scanned data. In the experiments, results for the facial customization for gender, race, fat, and age showed that BCFC achieved enhanced performance of 25.20% compared to existing program Facegen , and 44.12% compared to Facial Studio. The experimental results also proved the realistic quality and effectiveness of MFAM compared with blend shape technique by enhancing 2.87% and 0.03% of facial area for happiness and anger expressions per second, respectively. In Chapter 4, according to the analysis based on BCFC, the 3D face of an average kot-mi-nam is close to gender neutral (male: 50.38%, female: 49.62%), and Caucasian (66.42-66.40%). Culturally-specific images can be misinterpreted in different cultures, due to their different languages, histories, and contexts. This research demonstrates that facial images can be affected by the cultural tastes of the makers and can also be interpreted differently by viewers in different cultures

Adjudicating between face-coding models with individual-face fMRI responses.

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.This work was supported by the European Research Council (261352 awarded to NK), the UK Medical Research Council (MC_A060_5PR2 awarded to NK), and a British Academy Postdoctoral Fellowship (JDC)

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Synthesization and reconstruction of 3D faces by deep neural networks

The past few decades have witnessed substantial progress towards 3D facial modelling and reconstruction as it is high importance for many computer vision and graphics applications including Augmented/Virtual Reality (AR/VR), computer games, movie post-production, image/video editing, medical applications, etc. In the traditional approaches, facial texture and shape are represented as triangle mesh that can cover identity and expression variation with non-rigid deformation. A dataset of 3D face scans is then densely registered into a common topology in order to construct a linear statistical model. Such models are called 3D Morphable Models (3DMMs) and can be used for 3D face synthesization or reconstruction by a single or few 2D face images. The works presented in this thesis focus on the modernization of these traditional techniques in the light of recent advances of deep learning and thanks to the availability of large-scale datasets. Ever since the introduction of 3DMMs by over two decades, there has been a lot of progress on it and they are still considered as one of the best methodologies to model 3D faces. Nevertheless, there are still several aspects of it that need to be upgraded to the "deep era". Firstly, the conventional 3DMMs are built by linear statistical approaches such as Principal Component Analysis (PCA) which omits high-frequency information by its nature. While this does not curtail shape, which is often smooth in the original data, texture models are heavily afflicted by losing high-frequency details and photorealism. Secondly, the existing 3DMM fitting approaches rely on very primitive (i.e. RGB values, sparse landmarks) or hand-crafted features (i.e. HOG, SIFT) as supervision that are sensitive to "in-the-wild" images (i.e. lighting, pose, occlusion), or somewhat missing identity/expression resemblance with the target image. Finally, shape, texture, and expression modalities are separately modelled by ignoring the correlation among them, placing a fundamental limit to the synthesization of semantically meaningful 3D faces. Moreover, photorealistic 3D face synthesis has not been studied thoroughly in the literature. This thesis attempts to address the above-mentioned issues by harnessing the power of deep neural network and generative adversarial networks as explained below: Due to the linear texture models, many of the state-of-the-art methods are still not capable of reconstructing facial textures with high-frequency details. For this, we take a radically different approach and build a high-quality texture model by Generative Adversarial Networks (GANs) that preserves details. That is, we utilize GANs to train a very powerful generator of facial texture in the UV space. And then show that it is possible to employ this generator network as a statistical texture prior to 3DMM fitting. The resulting texture reconstructions are plausible and photorealistic as GANs are faithful to the real-data distribution in both low- and high- frequency domains. Then, we revisit the conventional 3DMM fitting approaches making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. We propose to optimize the parameters with the supervision of pretrained deep identity features through our end-to-end differentiable framework. In order to be robust towards initialization and expedite the fitting process, we also propose a novel self-supervised regression-based approach. We demonstrate excellent 3D face reconstructions that are photorealistic and identity preserving and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details. In order to extend the non-linear texture model for photo-realistic 3D face synthesis, we present a methodology that generates high-quality texture, shape, and normals jointly. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. Additionally, we study another approach for photo-realistic face synthesis by 3D guidance. This study proposes to generate 3D faces by linear 3DMM and then augment their 2D rendering by an image-to-image translation network to the photorealistic face domain. Both works demonstrate excellent photorealistic face synthesis and show that the generated faces are improving face recognition benchmarks as synthetic training data. Finally, we study expression reconstruction for personalized 3D face models where we improve generalization and robustness of expression encoding. First, we propose a 3D augmentation approach on 2D head-mounted camera images to increase robustness to perspective changes. And, we also propose to train generic expression encoder network by populating the number of identities with a novel multi-id personalized model training architecture in a self-supervised manner. Both approaches show promising results in both qualitative and quantitative experiments.Open Acces

Pictonaut: movie cartoonization using 3D human pose estimation and GANs

This article describes Pictonaut, a novel method to automatically synthetise animated shots from motion picture footage. Its results are editable (backgrounds, characters, lighting, etc.) with conventional 3D software, and they have the finish of professional 2D animation. Rather than addressing the challenge solely as an image translation problem, a hybrid approach combining multi-person 3D human pose estimation and GANs is taken. Sub-sampled video frames are processed with OpenPose and SMPLify-X to obtain the 3D parameters of the pose (body, hands and face expression) of all depicted characters. The captured parameters are retargeted into manually selected 3D models, cel shaded to mimic the style of a 2D cartoon. The results of sub-sampled frames are interpolated to generate a complete and smooth motion for all the characters. The background is cartoonized with a GAN. Qualitative evaluation shows that the approach is feasible, and a small dataset of synthetised shots obtained from real movie scenes is provided.This work is partially supported by the Spanish Ministry of Science and Innovation under contract PID2019-107255GB, and by the SGR programme 2017-SGR-1414 of the Catalan Government.Peer ReviewedPostprint (published version