Motion Guided Deep Dynamic 3D Garments

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative rollout prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.Comment: 11 page

The Stretch-Engine: A Method for Creating Exaggeration in Animation Through Squash and Stretch

Animators exaggerate character motion to emphasize personality and actions. Exaggeration is expressed by pushing a character’s pose, changing the action’s timing, or by changing a character’s form. This last method, referred to as squash and stretch, creates the most noticeable change in exaggeration. However, without practice, squash and stretch can adversely affect the animation. This work introduces a method to create exaggeration in motion by focusing solely on squash and stretch to control changes in a character’s form. It does this by displaying a limbs' path of motion and altering the shape of that path to create a change in the limb’s form. This paper provides information on tools that exist to create animation and exaggeration, then discusses the functionality and effectiveness of these tools and how they influenced the design of the Stretch-Engine. The Stretch-Engine is a prototype tool developed to demonstrate this approach and is designed to be integrated into an existing animation software, Maya. The Stretch-Engine contains a bipedal-humanoid rig with controls necessary for animation and the ability to squash and stretch. It can be accessed through a user interface that allows the animator to control squash and stretch by changing the shape of generated paths of motion. This method is then evaluated by comparing animations of realistic motion to versions created with the Stretch-Engine. These stretched versions displayed exaggerated results for their realistic counterparts, creating similar effects to Looney Tunes animation. This method fits within the animator’s workflow and helps new artists visualize and control squash and stretch to create exaggeration

LiveCap: Real-time Human Performance Capture from Monocular Video

We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video. We propose a novel two-stage analysis-by-synthesis optimization whose formulation and implementation are designed for high performance. In the first stage, a skinned template model is jointly fitted to background subtracted input video, 2D and 3D skeleton joint positions found using a deep neural network, and a set of sparse facial landmark detections. In the second stage, dense non-rigid 3D deformations of skin and even loose apparel are captured based on a novel real-time capable algorithm for non-rigid tracking using dense photometric and silhouette constraints. Our novel energy formulation leverages automatically identified material regions on the template to model the differing non-rigid deformation behavior of skin and apparel. The two resulting non-linear optimization problems per-frame are solved with specially-tailored data-parallel Gauss-Newton solvers. In order to achieve real-time performance of over 25Hz, we design a pipelined parallel architecture using the CPU and two commodity GPUs. Our method is the first real-time monocular approach for full-body performance capture. Our method yields comparable accuracy with off-line performance capture techniques, while being orders of magnitude faster

Doctor of Philosophy in Computing

dissertationPhysics-based animation has proven to be a powerful tool for creating compelling animations for film and games. Most techniques in graphics are based on methods developed for predictive simulation for engineering applications; however, the goals for graphics applications are dramatically different than the goals of engineering applications. As a result, most physics-based animation tools are difficult for artists to work with, providing little direct control over simulation results. In this thesis, we describe tools for physics-based animation designed with artist needs and expertise in mind. Most materials can be modeled as elastoplastic: they recover from small deformations, but large deformations permanently alter their rest shape. Unfortunately, large plastic deformations, common in graphical applications, cause simulation instabilities if not addressed. Most elastoplastic simulation techniques in graphics rely on a finite-element approach where objects are discretized into a tetrahedral mesh. Using these approaches, maintaining simulation stability during large plastic flows requires remeshing, a complex and computationally expensive process. We introduce a new point-based approach that does not rely on an explicit mesh and avoids the expense of remeshing. Our approach produces comparable results with much lower implementation complexity. Points are a ubiquitous primitive for many effects, so our approach also integrates well with existing artist pipelines. Next, we introduce a new technique for animating stylized images which we call Dynamic Sprites. Artists can use our tool to create digital assets that interact in a natural, but stylized, way in virtual environments. In order to support the types of nonphysical, exaggerated motions often desired by artists, our approach relies on a heavily modified deformable body simulator, equipped with a set of new intuitive controls and an example-based deformation model. Our approach allows artists to specify how the shape of the object should change as it moves and collides in interactive virtual environments. Finally, we introduce a new technique for animating destructive scenes. Our approach is built on the insight that the most important visual aspects of destruction are plastic deformation and fracture. Like with Dynamic Sprites, we use an example-based model of deformation for intuitive artist control. Our simulator treats objects as rigid when computing dynamics but allows them to deform plastically and fracture in between timesteps based on interactions with the other objects. We demonstrate that our approach can efficiently animate the types of destructive scenes common in film and games. These animation techniques are designed to exploit artist expertise to ease creation of complex animations. By using artist-friendly primitives and allowing artists to provide characteristic deformations as input, our techniques enable artists to create more compelling animations, more easily

Real-time human performance capture and synthesis

Most of the images one finds in the media, such as on the Internet or in textbooks and magazines, contain humans as the main point of attention. Thus, there is an inherent necessity for industry, society, and private persons to be able to thoroughly analyze and synthesize the human-related content in these images. One aspect of this analysis and subject of this thesis is to infer the 3D pose and surface deformation, using only visual information, which is also known as human performance capture. Human performance capture enables the tracking of virtual characters from real-world observations, and this is key for visual effects, games, VR, and AR, to name just a few application areas. However, traditional capture methods usually rely on expensive multi-view (marker-based) systems that are prohibitively expensive for the vast majority of people, or they use depth sensors, which are still not as common as single color cameras. Recently, some approaches have attempted to solve the task by assuming only a single RGB image is given. Nonetheless, they can either not track the dense deforming geometry of the human, such as the clothing layers, or they are far from real time, which is indispensable for many applications. To overcome these shortcomings, this thesis proposes two monocular human performance capture methods, which for the first time allow the real-time capture of the dense deforming geometry as well as an unseen 3D accuracy for pose and surface deformations. At the technical core, this work introduces novel GPU-based and data-parallel optimization strategies in conjunction with other algorithmic design choices that are all geared towards real-time performance at high accuracy. Moreover, this thesis presents a new weakly supervised multiview training strategy combined with a fully differentiable character representation that shows superior 3D accuracy. However, there is more to human-related Computer Vision than only the analysis of people in images. It is equally important to synthesize new images of humans in unseen poses and also from camera viewpoints that have not been observed in the real world. Such tools are essential for the movie industry because they, for example, allow the synthesis of photo-realistic virtual worlds with real-looking humans or of contents that are too dangerous for actors to perform on set. But also video conferencing and telepresence applications can benefit from photo-real 3D characters, as they can enhance the immersive experience of these applications. Here, the traditional Computer Graphics pipeline for rendering photo-realistic images involves many tedious and time-consuming steps that require expert knowledge and are far from real time. Traditional rendering involves character rigging and skinning, the modeling of the surface appearance properties, and physically based ray tracing. Recent learning-based methods attempt to simplify the traditional rendering pipeline and instead learn the rendering function from data resulting in methods that are easier accessible to non-experts. However, most of them model the synthesis task entirely in image space such that 3D consistency cannot be achieved, and/or they fail to model motion- and view-dependent appearance effects. To this end, this thesis presents a method and ongoing work on character synthesis, which allow the synthesis of controllable photoreal characters that achieve motion- and view-dependent appearance effects as well as 3D consistency and which run in real time. This is technically achieved by a novel coarse-to-fine geometric character representation for efficient synthesis, which can be solely supervised on multi-view imagery. Furthermore, this work shows how such a geometric representation can be combined with an implicit surface representation to boost synthesis and geometric quality.In den meisten Bildern in den heutigen Medien, wie dem Internet, Büchern und Magazinen, ist der Mensch das zentrale Objekt der Bildkomposition. Daher besteht eine inhärente Notwendigkeit für die Industrie, die Gesellschaft und auch für Privatpersonen, die auf den Mensch fokussierten Eigenschaften in den Bildern detailliert analysieren und auch synthetisieren zu können. Ein Teilaspekt der Anaylse von menschlichen Bilddaten und damit Bestandteil der Thesis ist das Rekonstruieren der 3D-Skelett-Pose und der Oberflächendeformation des Menschen anhand von visuellen Informationen, was fachsprachlich auch als Human Performance Capture bezeichnet wird. Solche Rekonstruktionsverfahren ermöglichen das Tracking von virtuellen Charakteren anhand von Beobachtungen in der echten Welt, was unabdingbar ist für Applikationen im Bereich der visuellen Effekte, Virtual und Augmented Reality, um nur einige Applikationsfelder zu nennen. Nichtsdestotrotz basieren traditionelle Tracking-Methoden auf teuren (markerbasierten) Multi-Kamera Systemen, welche für die Mehrheit der Bevölkerung nicht erschwinglich sind oder auf Tiefenkameras, die noch immer nicht so gebräuchlich sind wie herkömmliche Farbkameras. In den letzten Jahren gab es daher erste Methoden, die versuchen, das Tracking-Problem nur mit Hilfe einer Farbkamera zu lösen. Allerdings können diese entweder die Kleidung der Person im Bild nicht tracken oder die Methoden benötigen zu viel Rechenzeit, als dass sie in realen Applikationen genutzt werden könnten. Um diese Probleme zu lösen, stellt die Thesis zwei monokulare Human Performance Capture Methoden vor, die zum ersten Mal eine Echtzeit-Rechenleistung erreichen sowie im Vergleich zu vorherigen Arbeiten die Genauigkeit von Pose und Oberfläche in 3D weiter verbessern. Der Kern der Methoden beinhaltet eine neuartige GPU-basierte und datenparallelisierte Optimierungsstrategie, die im Zusammenspiel mit anderen algorithmischen Designentscheidungen akkurate Ergebnisse erzeugt und dabei eine Echtzeit-Laufzeit ermöglicht. Daneben wird eine neue, differenzierbare und schwach beaufsichtigte, Multi-Kamera basierte Trainingsstrategie in Kombination mit einem komplett differenzierbaren Charaktermodell vorgestellt, welches ungesehene 3D Präzision erreicht. Allerdings spielt nicht nur die Analyse von Menschen in Bildern in Computer Vision eine wichtige Rolle, sondern auch die Möglichkeit, neue Bilder von Personen in unterschiedlichen Posen und Kamera- Blickwinkeln synthetisch zu rendern, ohne dass solche Daten zuvor in der Realität aufgenommen wurden. Diese Methoden sind unabdingbar für die Filmindustrie, da sie es zum Beispiel ermöglichen, fotorealistische virtuelle Welten mit real aussehenden Menschen zu erzeugen, sowie die Möglichkeit bieten, Szenen, die für den Schauspieler zu gefährlich sind, virtuell zu produzieren, ohne dass eine reale Person diese Aktionen tatsächlich ausführen muss. Aber auch Videokonferenzen und Telepresence-Applikationen können von fotorealistischen 3D-Charakteren profitieren, da diese die immersive Erfahrung von solchen Applikationen verstärken. Traditionelle Verfahren zum Rendern von fotorealistischen Bildern involvieren viele mühsame und zeitintensive Schritte, welche Expertenwissen vorraussetzen und zudem auch Rechenzeiten erreichen, die jenseits von Echtzeit sind. Diese Schritte beinhalten das Rigging und Skinning von virtuellen Charakteren, das Modellieren von Reflektions- und Materialeigenschaften sowie physikalisch basiertes Ray Tracing. Vor Kurzem haben Deep Learning-basierte Methoden versucht, die Rendering-Funktion von Daten zu lernen, was in Verfahren resultierte, die eine Nutzung durch Nicht-Experten ermöglicht. Allerdings basieren die meisten Methoden auf Synthese-Verfahren im 2D-Bildbereich und können daher keine 3D-Konsistenz garantieren. Darüber hinaus gelingt es den meisten Methoden auch nicht, bewegungs- und blickwinkelabhängige Effekte zu erzeugen. Daher präsentiert diese Thesis eine neue Methode und eine laufende Forschungsarbeit zum Thema Charakter-Synthese, die es erlauben, fotorealistische und kontrollierbare 3D-Charakteren synthetisch zu rendern, die nicht nur 3D-konsistent sind, sondern auch bewegungs- und blickwinkelabhängige Effekte modellieren und Echtzeit-Rechenzeiten ermöglichen. Dazu wird eine neuartige Grobzu- Fein-Charakterrepräsentation für effiziente Bild-Synthese von Menschen vorgestellt, welche nur anhand von Multi-Kamera-Daten trainiert werden kann. Daneben wird gezeigt, wie diese explizite Geometrie- Repräsentation mit einer impliziten Oberflächendarstellung kombiniert werden kann, was eine bessere Synthese von geomtrischen Deformationen sowie Bildern ermöglicht.ERC Consolidator Grant 4DRepL

A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint

3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy term. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which is naturally data-driven. We mainly survey recent research works from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed

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