Tracking 3D human pose with large root node uncertainty

Representing articulated objects as a graphical model has gained much popularity in recent years, often the root node of the graph describes the global position and ori-entation of the object. In this work a method is presented to robustly track 3D human pose by permitting greater un-certainty to be modeled over the root node than existing techniques allow. Significantly, this is achieved without in-creasing the uncertainty of remaining parts of the model. The benefit is that a greater volume of the posterior can be supported making the approach less vulnerable to tracking failure. Given a hypothesis of the root node state a novel method is presented to estimate the posterior over the re-maining parts of the body conditioned on this value. All probability distributions are approximated using a single Gaussian allowing inference to be carried out in closed form. A set of deterministically selected sample points are used that allow the posterior to be updated for each part requiring just seven image likelihood evaluations making it extremely efficient. Multiple root node states are supported and propagated using standard sampling techniques. We believe this to be the first work devoted to efficient track-ing of human pose whilst modeling large uncertainty in the root node and demonstrate the presented method to be more robust to tracking failures than existing approaches. 1

Robust Estimation of 3D Human Poses from a Single Image

Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error between the projection of the 3D pose and the corresponding 2D detection. The $L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets

Single image 3D human pose estimation from noisy observations

Markerless 3D human pose detection from a single image is a severely underconstrained problem because different 3D poses can have similar image projections. In order to handle this ambiguity, current approaches rely on prior shape models that can only be correctly adjusted if 2D image features are accurately detected. Unfortunately, although current 2D part detector algorithms have shown promising results, they are not yet accurate enough to guarantee a complete disambiguation of the 3D inferred shape. In this paper, we introduce a novel approach for estimating 3D human pose even when observations are noisy. We propose a stochastic sampling strategy to propagate the noise from the image plane to the shape space. This provides a set of ambiguous 3D shapes, which are virtually undistinguishable from their image projections. Disambiguation is then achieved by imposing kinematic constraints that guarantee the resulting pose resembles a 3D human shape. We validate the method on a variety of situations in which state-of-the-art 2D detectors yield either inaccurate estimations or partly miss some of the body parts.Preprin

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growing demand driven by the applications, predicting 3D pose from a 2D image is a challenging and ill-posed problem due to the loss of depth information during projection from 3D to 2D. Although there have been years of research on 3D pose estimation problem, it still remains unsolved. In this thesis, we propose a variety of ways to tackle the 3D pose estimation problem both for articulated human bodies and rigid object bodies by learning robust features and latent representations. First, we present a novel video-based approach that exploits spatiotemporal features for 3D human pose estimation in a discriminative regression scheme. While early approaches typically account for motion information by temporally regularizing noisy pose estimates in individual frames, we demonstrate that taking into account motion information very early in the modeling process with spatiotemporal features yields significant performance improvements. We further propose a CNN-based motion compensation approach that stabilizes and centralizes the human body in the bounding boxes of consecutive frames to increase the reliability of spatiotemporal features. This then allows us to effectively overcome ambiguities and improve pose estimation accuracy. Second, we develop a novel Deep Learning framework for structured prediction of 3D human pose. Our approach relies on an auto-encoder to learn a high-dimensional latent pose representation that accounts for joint dependencies. We combine traditional CNNs for supervised learning with auto-encoders for structured learning and demonstrate that our approach outperforms the existing ones both in terms of structure preservation and prediction accuracy. Third, we propose a 3D human pose estimation approach that relies on a two-stream neural network architecture to simultaneously exploit 2D joint location heatmaps and image features. We show that 2D pose of a person, predicted in terms of heatmaps by a fully convolutional network, provides valuable cues to disambiguate challenging poses and results in increased pose estimation accuracy. We further introduce a novel and generic trainable fusion scheme, which automatically learns where and how to fuse the features extracted from two different input modalities that a two-stream neural network operates on. Our trainable fusion framework selects the optimal network architecture on-the-fly and improves upon standard hard-coded network architectures. Fourth, we propose an efficient approach to estimate 3D pose of objects from a single RGB image. Existing methods typically detect 2D bounding boxes and then predict the object pose using a pipelined approach. The redundancy in different parts of the architecture makes such methods computationally expensive. Moreover, the final pose estimation accuracy depends on the accuracy of the intermediate 2D object detection step. In our method, the object is classified and its pose is regressed in a single shot from the full image using a single, compact fully convolutional neural network. Our approach achieves the state-of-the-art accuracy without requiring any costly pose refinement step and runs in real-time at 50 fps on a modern GPU, which is at least 5X faster than the state of the art

Understanding human-centric images : from geometry to fashion

Understanding humans from photographs has always been a fundamental goal of computer vision. Early works focused on simple tasks such as detecting the location of individuals by means of bounding boxes. As the field progressed, harder and more higher level tasks have been undertaken. For example, from human detection came the 2D and 3D human pose estimation in which the task consisted of identifying the location in the image or space of all different body parts, e.g., head, torso, knees, arms, etc. Human attributes also became a great source of interest as they allow recognizing individuals and other properties such as gender or age. Later, the attention turned to the recognition of the action being performed. This, in general, relies on the previous works on pose estimation and attribute classification. Currently, even higher level tasks are being conducted such as predicting the motivations of human behavior or identifying the fashionability of an individual from a photograph. In this thesis we have developed a hierarchy of tools that cover all these range of problems, from low level feature point descriptors to high level fashion-aware conditional random fields models, all with the objective of understanding humans from monocular, RGB images. In order to build these high level models it is paramount to have a battery of robust and reliable low and mid level cues. Along these lines, we have proposed two low-level keypoint descriptors: one based on the theory of the heat diffusion on images, and the other that uses a convolutional neural network to learn discriminative image patch representations. We also introduce distinct low-level generative models for representing human pose: in particular we present a discrete model based on a directed acyclic graph and a continuous model that consists of poses clustered on a Riemannian manifold. As mid level cues we propose two 3D human pose estimation algorithms: one that estimates the 3D pose given a noisy 2D estimation, and an approach that simultaneously estimates both the 2D and 3D pose. Finally, we formulate higher level models built upon low and mid level cues for human understanding. Concretely, we focus on two different tasks in the context of fashion: semantic segmentation of clothing, and predicting the fashionability from images with metadata to ultimately provide fashion advice to the user. In summary, to robustly extract knowledge from images with the presence of humans it is necessary to build high level models that integrate low and mid level cues. In general, using and understanding strong features is critical for obtaining reliable performance. The main contribution of this thesis is in proposing a variety of low, mid and high level algorithms for human-centric images that can be integrated into higher level models for comprehending humans from photographs, as well as tackling novel fashion-oriented problems.Siempre ha sido una meta fundamental de la visión por computador la comprensión de los seres humanos. Los primeros trabajos se fijaron en objetivos sencillos tales como la detección en imágenes de la posición de los individuos. A medida que la investigación progresó se emprendieron tareas mucho más complejas. Por ejemplo, a partir de la detección de los humanos se pasó a la estimación en dos y tres dimensiones de su postura por lo que la tarea consistía en identificar la localización en la imagen o el espacio de las diferentes partes del cuerpo, por ejemplo cabeza, torso, rodillas, brazos, etc...También los atributos humanos se convirtieron en una gran fuente de interés ya que permiten el reconocimiento de los individuos y de sus propiedades como el género o la edad. Más tarde, la atención se centró en el reconocimiento de la acción realizada. Todos estos trabajos reposan en las investigaciones previas sobre la estimación de las posturas y la clasificación de los atributos. En la actualidad, se llevan a cabo investigaciones de un nivel aún superior sobre cuestiones tales como la predicción de las motivaciones del comportamiento humano o la identificación del tallaje de un individuo a partir de una fotografía. En esta tesis desarrollamos una jerarquía de herramientas que cubre toda esta gama de problemas, desde descriptores de rasgos de bajo nivel a modelos probabilísticos de campos condicionales de alto nivel reconocedores de la moda, todos ellos con el objetivo de mejorar la comprensión de los humanos a partir de imágenes RGB monoculares. Para construir estos modelos de alto nivel es decisivo disponer de una batería de datos robustos y fiables de nivel bajo y medio. En este sentido, proponemos dos descriptores novedosos de bajo nivel: uno se basa en la teoría de la difusión de calor en las imágenes y otro utiliza una red neural convolucional para aprender representaciones discriminativas de trozos de imagen. También introducimos diferentes modelos de bajo nivel generativos para representar la postura humana: en particular presentamos un modelo discreto basado en un gráfico acíclico dirigido y un modelo continuo que consiste en agrupaciones de posturas en una variedad de Riemann. Como señales de nivel medio proponemos dos algoritmos estimadores de la postura humana: uno que estima la postura en tres dimensiones a partir de una estimación imprecisa en el plano de la imagen y otro que estima simultáneamente la postura en dos y tres dimensiones. Finalmente construimos modelos de alto nivel a partir de señales de nivel bajo y medio para la comprensión de la persona a partir de imágenes. En concreto, nos centramos en dos diferentes tareas en el ámbito de la moda: la segmentación semántica del vestido y la predicción del buen ajuste de la prenda a partir de imágenes con meta-datos con la finalidad de aconsejar al usuario sobre moda. En resumen, para extraer conocimiento a partir de imágenes con presencia de seres humanos es preciso construir modelos de alto nivel que integren señales de nivel medio y bajo. En general, el punto crítico para obtener resultados fiables es el empleo y la comprensión de rasgos fuertes. La aportación fundamental de esta tesis es la propuesta de una variedad de algoritmos de nivel bajo, medio y alto para el tratamiento de imágenes centradas en seres humanos que pueden integrarse en modelos de alto nivel, para mejor comprensión de los seres humanos a partir de fotografías, así como abordar problemas planteados por el buen ajuste de las prendas