Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state- of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors.Comment: Paper presented at CVPR 1

Real-time 3D human body pose estimation from monocular RGB input

Human motion capture finds extensive application in movies, games, sports and biomechanical analysis. However, existing motion capture solutions require cumbersome external and/or on-body instrumentation, or use active sensors with limits on the possible capture volume dictated by power consumption. The ubiquity and ease of deployment of RGB cameras makes monocular RGB based human motion capture an extremely useful problem to solve, which would lower the barrier-to entry for content creators to employ motion capture tools, and enable newer applications of human motion capture. This thesis demonstrates the first real-time monocular RGB based motion-capture solutions that work in general scene settings. They are based on developing neural network based approaches to address the ill-posed problem of estimating 3D human pose from a single RGB image, in combination with model based fitting. In particular, the contributions of this work make advances towards three key aspects of real-time monocular RGB based motion capture, namely speed, accuracy, and the ability to work for general scenes. New training datasets are proposed, for single-person and multi-person scenarios, which, together with the proposed transfer learning based training pipeline, allow learning based approaches to be appearance invariant. The training datasets are accompanied by evaluation benchmarks with multiple avenues of fine-grained evaluation. The evaluation benchmarks differ visually from the training datasets, so as to promote efforts towards solutions that generalize to in-the-wild scenes. The proposed task formulations for the single-person and multi-person case allow higher accuracy, and incorporate additional qualities such as occlusion robustness, that are helpful in the context of a full motion capture solution. The multi-person formulations are designed to have a nearly constant inference time regardless of the number of subjects in the scene, and combined with contributions towards fast neural network inference, enable real-time 3D pose estimation for multiple subjects. Combining the proposed learning-based approaches with a model-based kinematic skeleton fitting step provides temporally stable joint angle estimates, which can be readily employed for driving virtual characters.Menschlicher Motion Capture findet umfangreiche Anwendung in Filmen, Spielen, Sport und biomechanischen Analysen. Bestehende Motion-Capture-Lösungen erfordern jedoch umständliche externe Instrumentierung und / oder Instrumentierung am Körper, oder verwenden aktive Sensoren deren begrenztes Erfassungsvolumen durch den Stromverbrauch begrenzt wird. Die Allgegenwart und einfache Bereitstellung von RGB-Kameras macht die monokulare RGB-basierte Motion Capture zu einem äußerst nützlichen Problem. Dies würde die Eintrittsbarriere für Inhaltsersteller für die Verwendung der Motion Capture verringern und neuere Anwendungen dieser Tools zur Analyse menschlicher Bewegungen ermöglichen. Diese Arbeit zeigt die ersten monokularen RGB-basierten Motion-Capture-Lösungen in Echtzeit, die in allgemeinen Szeneneinstellungen funktionieren. Sie basieren auf der Entwicklung neuronaler netzwerkbasierter Ansätze, um das schlecht gestellte Problem der Schätzung der menschlichen 3D-Pose aus einem einzelnen RGB-Bild in Kombination mit einer modellbasierten Anpassung anzugehen. Insbesondere machen die Beiträge dieser Arbeit Fortschritte in Richtung drei Schlüsselaspekte der monokularen RGB-basierten Echtzeit-Bewegungserfassung, nämlich Geschwindigkeit, Genauigkeit und die Fähigkeit, für allgemeine Szenen zu arbeiten. Es werden neue Trainingsdatensätze für Einzel- und Mehrpersonen-Szenarien vorgeschlagen, die zusammen mit der vorgeschlagenen Trainingspipeline, die auf Transferlernen basiert, ermöglichen, dass lernbasierte Ansätze nicht von Unterschieden im Erscheinungsbild des Bildes beeinflusst werden. Die Trainingsdatensätze werden von Bewertungsbenchmarks mit mehreren Möglichkeiten einer feinkörnigen Bewertung begleitet. Die angegebenen Benchmarks unterscheiden sich visuell von den Trainingsaufzeichnungen, um die Entwicklung von Lösungen zu fördern, die sich auf verschiedene Szenen verallgemeinern lassen. Die vorgeschlagenen Aufgabenformulierungen für den Einzel- und Mehrpersonenfall ermöglichen eine höhere Genauigkeit und enthalten zusätzliche Eigenschaften wie die Robustheit der Okklusion, die im Kontext einer vollständigen Bewegungserfassungslösung hilfreich sind. Die Mehrpersonenformulierungen sind so konzipiert, dass sie unabhängig von der Anzahl der Subjekte in der Szene eine nahezu konstante Inferenzzeit haben. In Kombination mit Beiträgen zur schnellen Inferenz neuronaler Netze ermöglichen sie eine 3D-Posenschätzung in Echtzeit für mehrere Subjekte. Die Kombination der vorgeschlagenen lernbasierten Ansätze mit einem modellbasierten kinematischen Skelettanpassungsschritt liefert zeitlich stabile Gelenkwinkelschätzungen, die leicht zum Ansteuern virtueller Charaktere verwendet werden können

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Bridging the gap between reconstruction and synthesis

Aplicat embargament des de la data de defensa fins el 15 de gener de 20223D reconstruction and image synthesis are two of the main pillars in computer vision. Early works focused on simple tasks such as multi-view reconstruction and texture synthesis. With the spur of Deep Learning, the field has rapidly progressed, making it possible to achieve more complex and high level tasks. For example, the 3D reconstruction results of traditional multi-view approaches are currently obtained with single view methods. Similarly, early pattern based texture synthesis works have resulted in techniques that allow generating novel high-resolution images. In this thesis we have developed a hierarchy of tools that cover all these range of problems, lying at the intersection of computer vision, graphics and machine learning. We tackle the problem of 3D reconstruction and synthesis in the wild. Importantly, we advocate for a paradigm in which not everything should be learned. Instead of applying Deep Learning naively we propose novel representations, layers and architectures that directly embed prior 3D geometric knowledge for the task of 3D reconstruction and synthesis. We apply these techniques to problems including scene/person reconstruction and photo-realistic rendering. We first address methods to reconstruct a scene and the clothed people in it while estimating the camera position. Then, we tackle image and video synthesis for clothed people in the wild. Finally, we bridge the gap between reconstruction and synthesis under the umbrella of a unique novel formulation. Extensive experiments conducted along this thesis show that the proposed techniques improve the performance of Deep Learning models in terms of the quality of the reconstructed 3D shapes / synthesised images, while reducing the amount of supervision and training data required to train them. In summary, we provide a variety of low, mid and high level algorithms that can be used to incorporate prior knowledge into different stages of the Deep Learning pipeline and improve performance in tasks of 3D reconstruction and image synthesis.La reconstrucció 3D i la síntesi d'imatges són dos dels pilars fonamentals en visió per computador. Els estudis previs es centren en tasques senzilles com la reconstrucció amb informació multi-càmera i la síntesi de textures. Amb l'aparició del "Deep Learning", aquest camp ha progressat ràpidament, fent possible assolir tasques molt més complexes. Per exemple, per obtenir una reconstrucció 3D, tradicionalment s'utilitzaven mètodes multi-càmera, en canvi ara, es poden obtenir a partir d'una sola imatge. De la mateixa manera, els primers treballs de síntesi de textures basats en patrons han donat lloc a tècniques que permeten generar noves imatges completes en alta resolució. En aquesta tesi, hem desenvolupat una sèrie d'eines que cobreixen tot aquest ventall de problemes, situats en la intersecció entre la visió per computador, els gràfics i l'aprenentatge automàtic. Abordem el problema de la reconstrucció i la síntesi 3D en el món real. És important destacar que defensem un paradigma on no tot s'ha d'aprendre. Enlloc d'aplicar el "Deep Learning" de forma naïve, proposem representacions novedoses i arquitectures que incorporen directament els coneixements geomètrics ja existents per a aconseguir la reconstrucció 3D i la síntesi d'imatges. Nosaltres apliquem aquestes tècniques a problemes com ara la reconstrucció d'escenes/persones i a la renderització d'imatges fotorealistes. Primer abordem els mètodes per reconstruir una escena, les persones vestides que hi ha i la posició de la càmera. A continuació, abordem la síntesi d'imatges i vídeos de persones vestides en situacions quotidianes. I finalment, aconseguim, a través d'una nova formulació única, connectar la reconstrucció amb la síntesi. Els experiments realitzats al llarg d'aquesta tesi demostren que les tècniques proposades milloren el rendiment dels models de "Deepp Learning" pel que fa a la qualitat de les reconstruccions i les imatges sintetitzades alhora que redueixen la quantitat de dades necessàries per entrenar-los. En resum, proporcionem una varietat d'algoritmes de baix, mitjà i alt nivell que es poden utilitzar per incorporar els coneixements previs a les diferents etapes del "Deep Learning" i millorar el rendiment en tasques de reconstrucció 3D i síntesi d'imatges.Postprint (published version

Human Pose Estimation with Implicit Shape Models

This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions

Learning Generative Models for Multi-Activity Body Pose Estimation

We present a method to simultaneously estimate 3D body pose and action categories from monocular video sequences. Our approach learns a generative model of the relationship of body pose and image appearance using a sparse kernel regressor. Body poses are modelled on a low-dimensional manifold obtained by Locally Linear Embedding dimensionality reduction. In addition, we learn a prior model of likely body poses and a dynamical model in this pose manifold. Sparse kernel regressors capture the nonlinearities of this mapping efficiently. Within a Recursive Bayesian Sampling framework, the potentially multimodal posterior probability distributions can then be inferred. An activity-switching mechanism based on learned transfer functions allows for inference of the performed activity class, along with the estimation of body pose and 2D image location of the subject. Using a rough foreground segmentation, we compare Binary PCA and distance transforms to encode the appearance. As a postprocessing step, the globally optimal trajectory through the entire sequence is estimated, yielding a single pose estimate per frame that is consistent throughout the sequence. We evaluate the algorithm on challenging sequences with subjects that are alternating between running and walking movements. Our experiments show how the dynamical model helps to track through poorly segmented low-resolution image sequences where tracking otherwise fails, while at the same time reliably classifying the activity typ

Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking

Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática