FDLS: A Deep Learning Approach to Production Quality, Controllable, and Retargetable Facial Performances

Visual effects commonly requires both the creation of realistic synthetic humans as well as retargeting actors' performances to humanoid characters such as aliens and monsters. Achieving the expressive performances demanded in entertainment requires manipulating complex models with hundreds of parameters. Full creative control requires the freedom to make edits at any stage of the production, which prohibits the use of a fully automatic ``black box'' solution with uninterpretable parameters. On the other hand, producing realistic animation with these sophisticated models is difficult and laborious. This paper describes FDLS (Facial Deep Learning Solver), which is Weta Digital's solution to these challenges. FDLS adopts a coarse-to-fine and human-in-the-loop strategy, allowing a solved performance to be verified and edited at several stages in the solving process. To train FDLS, we first transform the raw motion-captured data into robust graph features. Secondly, based on the observation that the artists typically finalize the jaw pass animation before proceeding to finer detail, we solve for the jaw motion first and predict fine expressions with region-based networks conditioned on the jaw position. Finally, artists can optionally invoke a non-linear finetuning process on top of the FDLS solution to follow the motion-captured virtual markers as closely as possible. FDLS supports editing if needed to improve the results of the deep learning solution and it can handle small daily changes in the actor's face shape. FDLS permits reliable and production-quality performance solving with minimal training and little or no manual effort in many cases, while also allowing the solve to be guided and edited in unusual and difficult cases. The system has been under development for several years and has been used in major movies.Comment: DigiPro '22: The Digital Production Symposiu

An Implicit Parametric Morphable Dental Model

3D Morphable models of the human body capture variations among subjects and are useful in reconstruction and editing applications. Current dental models use an explicit mesh scene representation and model only the teeth, ignoring the gum. In this work, we present the first parametric 3D morphable dental model for both teeth and gum. Our model uses an implicit scene representation and is learned from rigidly aligned scans. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. It also learns a template shape thus enabling several applications such as segmentation, interpolation, and tooth replacement. Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications. Project page: https://vcai.mpi-inf.mpg.de/projects/DMM

SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator

Recent years have seen growing interest in 3D human faces modelling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner skeletal structures and appearance. In this paper, we present SCULPTOR, 3D face creations with Skeleton Consistency Using a Learned Parametric facial generaTOR, aiming to facilitate easy creation of both anatomically correct and visually convincing face models via a hybrid parametric-physical representation. At the core of SCULPTOR is LUCY, the first large-scale shape-skeleton face dataset in collaboration with plastic surgeons. Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, critical for evaluating surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41 female) where each subject has two CT scans taken pre- and post-orthognathic operations. Based on our LUCY dataset, we learn a novel skeleton consistent parametric facial generator, SCULPTOR, which can create the unique and nuanced facial features that help define a character and at the same time maintain physiological soundness. Our SCULPTOR jointly models the skull, face geometry and face appearance under a unified data-driven framework, by separating the depiction of a 3D face into shape blend shape, pose blend shape and facial expression blend shape. SCULPTOR preserves both anatomic correctness and visual realism in facial generation tasks compared with existing methods. Finally, we showcase the robustness and effectiveness of SCULPTOR in various fancy applications unseen before.Comment: 16 page, 13 fig

{3D} Morphable Face Models -- Past, Present and Future

In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications

Realtime Face Tracking and Animation

Capturing and processing human geometry, appearance, and motion is at the core of computer graphics, computer vision, and human-computer interaction. The high complexity of human geometry and motion dynamics, and the high sensitivity of the human visual system to variations and subtleties in faces and bodies make the 3D acquisition and reconstruction of humans in motion a challenging task. Digital humans are often created through a combination of 3D scanning, appearance acquisition, and motion capture, leading to stunning results in recent feature films. However, these methods typically require complex acquisition systems and substantial manual post-processing. As a result, creating and animating high-quality digital avatars entails long turn-around times and substantial production costs. Recent technological advances in RGB-D devices, such as Microsoft Kinect, brought new hopes for realtime, portable, and affordable systems allowing to capture facial expressions as well as hand and body motions. RGB-D devices typically capture an image and a depth map. This permits to formulate the motion tracking problem as a 2D/3D non-rigid registration of a deformable model to the input data. We introduce a novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization. This led to unprecedented face tracking quality on a low cost consumer level device. The main drawback of this approach in the context of consumer applications is the need for an offline user-specific training. Robust and efficient tracking is achieved by building an accurate 3D expression model of the user's face who is scanned in a predefined set of facial expressions. We extended this approach removing the need of a user-specific training or calibration, or any other form of manual assistance, by modeling online a 3D user-specific dynamic face model. In complement of a realtime face tracking and modeling algorithm, we developed a novel system for animation retargeting that allows learning a high-quality mapping between motion capture data and arbitrary target characters. We addressed one of the main challenges of existing example-based retargeting methods, the need for a large number of accurate training examples to define the correspondence between source and target expression spaces. We showed that this number can be significantly reduced by leveraging the information contained in unlabeled data, i.e. facial expressions in the source or target space without corresponding poses. Finally, we present a novel realtime physics-based animation technique allowing to simulate a large range of deformable materials such as fat, flesh, hair, or muscles. This approach could be used to produce more lifelike animations by enhancing the animated avatars with secondary effects. We believe that the realtime face tracking and animation pipeline presented in this thesis has the potential to inspire numerous future research in the area of computer-generated animation. Already, several ideas presented in thesis have been successfully used in industry and this work gave birth to the startup company faceshift AG

Physics-based Reconstruction and Animation of Humans

Creating digital representations of humans is of utmost importance for applications ranging from entertainment (video games, movies) to human-computer interaction and even psychiatrical treatments. What makes building credible digital doubles difficult is the fact that the human vision system is very sensitive to perceiving the complex expressivity and potential anomalies in body structures and motion. This thesis will present several projects that tackle these problems from two different perspectives: lightweight acquisition and physics-based simulation. It starts by describing a complete pipeline that allows users to reconstruct fully rigged 3D facial avatars using video data coming from a handheld device (e.g., smartphone). The avatars use a novel two-scale representation composed of blendshapes and dynamic detail maps. They are constructed through an optimization that integrates feature tracking, optical flow, and shape from shading. Continuing along the lines of accessible acquisition systems, we discuss a framework for simultaneous tracking and modeling of articulated human bodies from RGB-D data. We show how semantic information can be extracted from the scanned body shapes. In the second half of the thesis, we will deviate from using standard linear reconstruction and animation models, and rather focus on exploiting physics-based techniques that are able to incorporate complex phenomena such as dynamics, collision response and incompressibility of the materials. The first approach we propose assumes that each 3D scan of an actor records his body in a physical steady state and uses a process called inverse physics to extract a volumetric physics-ready anatomical model of him. By using biologically-inspired growth models for the bones, muscles and fat, our method can obtain realistic anatomical reconstructions that can be later on animated using external tracking data such as the one resulting from tracking motion capture markers. This is then extended to a novel physics-based approach for facial reconstruction and animation. We propose a facial animation model which simulates biomechanical muscle contractions in a volumetric head model in order to create the facial expressions seen in the input scans. We then show how this approach allows for new avenues of dynamic artistic control, simulation of corrective facial surgery, and interaction with external forces and objects

High-quality face capture, animation and editing from monocular video

Digitization of virtual faces in movies requires complex capture setups and extensive manual work to produce superb animations and video-realistic editing. This thesis pushes the boundaries of the digitization pipeline by proposing automatic algorithms for high-quality 3D face capture and animation, as well as photo-realistic face editing. These algorithms reconstruct and modify faces in 2D videos recorded in uncontrolled scenarios and illumination. In particular, advances in three main areas offer solutions for the lack of depth and overall uncertainty in video recordings. First, contributions in capture include model-based reconstruction of detailed, dynamic 3D geometry that exploits optical and shading cues, multilayer parametric reconstruction of accurate 3D models in unconstrained setups based on inverse rendering, and regression-based 3D lip shape enhancement from high-quality data. Second, advances in animation are video-based face reenactment based on robust appearance metrics and temporal clustering, performance-driven retargeting of detailed facial models in sync with audio, and the automatic creation of personalized controllable 3D rigs. Finally, advances in plausible photo-realistic editing are dense face albedo capture and mouth interior synthesis using image warping and 3D teeth proxies. High-quality results attained on challenging application scenarios confirm the contributions and show great potential for the automatic creation of photo-realistic 3D faces.Die Digitalisierung von Gesichtern zum Einsatz in der Filmindustrie erfordert komplizierte Aufnahmevorrichtungen und die manuelle Nachbearbeitung von Rekonstruktionen, um perfekte Animationen und realistische Videobearbeitung zu erzielen. Diese Dissertation erweitert vorhandene Digitalisierungsverfahren durch die Erforschung von automatischen Verfahren zur qualitativ hochwertigen 3D Rekonstruktion, Animation und Modifikation von Gesichtern. Diese Algorithmen erlauben es, Gesichter in 2D Videos, die unter allgemeinen Bedingungen und unbekannten Beleuchtungsverhältnissen aufgenommen wurden, zu rekonstruieren und zu modifizieren. Vor allem Fortschritte in den folgenden drei Hauptbereichen tragen zur Kompensation von fehlender Tiefeninformation und der allgemeinen Mehrdeutigkeit von 2D Videoaufnahmen bei. Erstens, Beiträge zur modellbasierten Rekonstruktion von detaillierter und dynamischer 3D Geometrie durch optische Merkmale und die Shading-Eigenschaften des Gesichts, mehrschichtige parametrische Rekonstruktion von exakten 3D Modellen mittels inversen Renderings in allgemeinen Szenen und regressionsbasierter 3D Lippenformverfeinerung mittels qualitativ hochwertigen Daten. Zweitens, Fortschritte im Bereich der Computeranimation durch videobasierte Gesichtsausdrucksübertragung und temporaler Clusterbildung, Übertragung von detaillierten Gesichtsmodellen, deren Mundbewegung mit Ton synchronisiert ist, und die automatische Erstellung von personalisierten "3D Face Rigs". Schließlich werden Fortschritte im Bereich der realistischen Videobearbeitung vorgestellt, welche auf der dichten Rekonstruktion von Hautreflektionseigenschaften und der Mundinnenraumsynthese mittels bildbasierten und geometriebasierten Verfahren aufbauen. Qualitativ hochwertige Ergebnisse in anspruchsvollen Anwendungen untermauern die Wichtigkeit der geleisteten Beiträgen und zeigen das große Potential der automatischen Erstellung von realistischen digitalen 3D Gesichtern auf