MonoPerfCap: Human Performance Capture from Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201

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

3D Human Pose and Shape Estimation Based on Parametric Model and Deep Learning

3D human body reconstruction from monocular images has wide applications in our life, such as movie, animation, Virtual/Augmented Reality, medical research and so on. Due to the high freedom of human body in real scene and the ambiguity of inferring 3D objects from 2D images, it is a challenging task to accurately recover 3D human body models from images. In this thesis, we explore the methods for estimating 3D human body models from images based on parametric model and deep learning.In the first part, the coarse 3D human body models are estimated automatically from multi-view images based on a parametric human body model called SMPL model. Two routes are exploited for estimating the pose and shape parameters of the SMPL model to obtain the 3D models: (1) Optimization based methods; and (2) Deep learning based methods. For the optimization based methods, we propose the novel energy functions based on some prior information including the 2D joint points and silhouettes. Through minimizing the energy functions, the SMPL model is fitted to the prior information, and then, the coarse 3D human body is obtained. In addition to the traditional optimization based methods, a deep learning based method is also proposed in the following work to regress the pose and shape parameters of the SMPL model. A novel architecture is proposed to put the optimization into a training loop of convolutional neural network (CNN) to form a self-supervision structure based on the multi-view images. The proposed methods are evaluated on both synthetic and real datasets to demonstrate that they can obtain better estimation of the pose and shape of 3D human body than previous approaches.In the second part, the problem is shifted to the detailed 3D human body reconstruction from multi-view images. Instead of using the SMPL model, implicit function is utilized to represent 3D models because implicit representation can generate continuous surface and has better flexibility for arbitrary topology. Firstly, a multi-scale features based method is proposed to learn the implicit representation for 3D models through multi-stage hourglass networks from multi-view images. Furthermore, a coarse-to-fine method is proposed to refine the 3D models from multi-view images through learning the voxel super-resolution. In this method, the coarse 3D models are estimated firstly by the learned implicit function based on multi-scale features from multi-view images. Afterwards, by voxelizing the coarse 3D models to low resolution voxel grids, voxel super-resolution is learned through a multi-stage 3D CNN for feature extraction from low resolution voxel grids and fully connected neural network for predicting the implicit function. Voxel super-resolution is able to remove the false reconstruction and preserve the surface details. The proposed methods are evaluated on both real and synthetic datasets in which our method can estimate 3D model with higher accuracy and better surface quality than some previous methods

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

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

Extracting curve-skeletons from digital shapes using occluding contours

Curve-skeletons are compact and semantically relevant shape descriptors, able to summarize both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper we introduce a formally sound and intuitive definition of curve-skeleton, then we propose a novel method for skeleton extraction that rely on the visual appearance of the shapes. To achieve this result we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred by a small set of its planar projections. The proposed method is fast, insensitive to noise, capable of working with different shape representations, resolution insensitive and easy to implement

Global 3D non-rigid registration of deformable objects using a single RGB-D camera

We present a novel global non-rigid registration method for dynamic 3D objects. Our method allows objects to undergo large non-rigid deformations, and achieves high quality results even with substantial pose change or camera motion between views. In addition, our method does not require a template prior and uses less raw data than tracking based methods since only a sparse set of scans is needed. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure problem, and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed shapes, especially near open boundaries of scans. To cope with large-scale problems, we design a coarse-to-fine multi-resolution scheme, which also avoids the optimization being trapped into local minima. The proposed method is evaluated on public datasets and real datasets captured by an RGB-D sensor. Experimental results demonstrate that the proposed method obtains better results than several state-of-the-art methods