Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data

We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image data with either 2D or 3D annotations, as well as stand-alone 3D animations without corresponding image data. It features a 3D hand joint detection module and an inverse kinematics module which regresses not only 3D joint positions but also maps them to joint rotations in a single feed-forward pass. This output makes the method more directly usable for applications in computer vision and graphics compared to only regressing 3D joint positions. We demonstrate that our architectural design leads to a significant quantitative and qualitative improvement over the state of the art on several challenging benchmarks. Our model is publicly available for future research

Shape basis interpretation for monocular deformable 3D reconstruction

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft

Non-Rigid Structure from Motion for Complex Motion

Recovering deformable 3D motion from temporal 2D point tracks in a monocular video is an open problem with many everyday applications throughout science and industry, or the new augmented reality. Recently, several techniques have been proposed to deal the problem called Non-Rigid Structure from Motion (NRSfM), however, they can exhibit poor reconstruction performance on complex motion. In this project, we will analyze these situations for primitive human actions such as walk, run, sit, jump, etc. on different scenarios, reviewing first the current techniques to finally present our novel method. This approach is able to model complex motion into a union of subspaces, rather than the summation occurring in standard low-rank shape methods, allowing better reconstruction accuracy. Experiments in a wide range of sequences and types of motion illustrate the benefits of this new approac

2D-to-3D facial expression transfer

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape --obtained from standard factorization approaches over the input video-- using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.Peer ReviewedPostprint (author's final draft

FML: Face Model Learning from Videos

Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existing methods rely on data-driven priors that are built from limited 3D face scans. In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces. Our face model is learned using only corpora of in-the-wild video clips collected from the Internet. This virtually endless source of training data enables learning of a highly general 3D face model. In order to achieve this, we propose a novel multi-frame consistency loss that ensures consistent shape and appearance across multiple frames of a subject's face, thus minimizing depth ambiguity. At test time we can use an arbitrary number of frames, so that we can perform both monocular as well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ, Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19

Learning-based 3D human motion capture and animation synthesis

Realistic virtual human avatar is a crucial element in a wide range of applications, from 3D animated movies to emerging AR/VR technologies. However, producing a believable 3D motion for such avatars is widely known to be a challenging task. A traditional 3D human motion generation pipeline consists of several stages, each requiring expensive equipment and skilled human labor to perform, limiting its usage beyond the entertainment industry despite its massive potential benefits. This thesis attempts to explore some alternative solutions to reduce the complexity of the traditional 3D animation pipeline. To this end, it presents several novel ways to perform 3D human motion capture, synthesis, and control. Specifically, it focuses on using learning-based methods to bypass the critical bottlenecks of the classical animation approach. First, a new 3D pose estimation method from in-the-wild monocular images is proposed, eliminating the need for a multi-camera setup in the traditional motion capture system. Second, it explores several data-driven designs to achieve a believable 3D human motion synthesis and control that can potentially reduce the need for manual animation. In particular, the problem of speech-driven 3D gesture synthesis is chosen as the case study due to its uniquely ambiguous nature. The improved motion generation quality is achieved by introducing a novel adversarial objective that rates the difference between real and synthetic data. A novel motion generation strategy is also introduced by combining a classical database search algorithm with a powerful deep learning method, resulting in a greater motion control variation than the purely predictive counterparts. Furthermore, this thesis also contributes a new way of collecting a large-scale 3D motion dataset through the use of learning-based monocular estimations methods. This result demonstrates the promising capability of learning-based monocular approaches and shows the prospect of combining these learning-based modules into an integrated 3D animation framework. The presented learning-based solutions open the possibility of democratizing the traditional 3D animation system that can be enabled using low-cost equipment, e.g., a single RGB camera. Finally, this thesis also discusses the potential further integration of these learning-based approaches to enhance 3D animation technology.Realistische virtuelle menschliche Avatare sind ein entscheidendes Element in einer Vielzahl von Anwendungen, von 3D-Animationsfilmen bis hin zu neuen AR/VR-Technologien. Die Erzeugung glaubwürdiger Bewegungen solcher Avatare in drei Dimensionen ist bekanntermaßen eine herausfordernde Aufgabe. Traditionelle Pipelines zur Erzeugung menschlicher 3D-Bewegungen bestehen aus mehreren Stufen, die jede für sich genommen teure Ausrüstung und den Einsatz von Expertenwissen erfordern und daher trotz ihrer enormen potenziellen Vorteile abseits der Unterhaltungsindustrie nur eingeschränkt verwendbar sind. Diese Arbeit untersucht verschiedene Alternativen um die Komplexität der traditionellen 3D-Animations-Pipeline zu reduzieren. Zu diesem Zweck stellt sie mehrere neuartige Möglichkeiten zur Erfassung, Synthese und Steuerung humanoider 3D-Bewegungen vor. Sie konzentriert sich auf die Verwendung lernbasierter Methoden, um kritische Teile des klassischen Animationsansatzes zu überbrücken: Zunächst wird eine neue 3D-Pose-Estimation-Methode für monokulare Bilder vorgeschlagen, um die Notwendigkeit mehrerer Kameras im traditionellen Motion-Capture-Ansatz zu beseitigen. Des Weiteren untersucht die Arbeit mehrere datengetriebene Ansätze zur Synthese und Steuerung glaubwürdiger humanoider 3D-Bewegungen, die möglicherweise den Bedarf an manueller Animation reduzieren können. Als Fallstudie wird, aufgrund seiner einzigartig mehrdeutigen Natur, das Problem der sprachgetriebenen 3D-Gesten-Synthese untersucht. Die Verbesserungen in der Qualität der erzeugten Bewegungen wird durch eine neuartige Kostenfunktion erreicht, die den Unterschied zwischen realen und synthetischen Daten bewertet. Außerdem wird eine neue Strategie zur Bewegungssynthese beschrieben, die eine klassische Datenbanksuche mit einer leistungsstarken Deep-Learning-Methode kombiniert, was zu einer größeren Variation der Bewegungssteuerung führt, als rein lernbasierte Verfahren sie bieten. Ein weiterer Beitrag dieser Dissertation besteht in einer neuen Methode zum Aufbau eines großen Datensatzes dreidimensionaler Bewegungen, auf Grundlage lernbasierter monokularer Pose-Estimation- Methoden. Dies demonstriert die vielversprechenden Möglichkeiten lernbasierter monokularer Methoden und lässt die Aussicht erkennen, diese lernbasierten Module zu einem integrierten 3D-Animations- Framework zu kombinieren. Die in dieser Arbeit vorgestellten lernbasierten Lösungen eröffnen die Möglichkeit, das traditionelle 3D-Animationssystem auch mit kostengünstiger Ausrüstung, wie z.B. einer einzelnen RGB-Kamera verwendbar zu machen. Abschließend diskutiert diese Arbeit auch die mögliche weitere Integration dieser lernbasierten Ansätze zur Verbesserung der 3D-Animationstechnologie