Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.Comment: To appear in ECCV 201

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fittingComment: 3DV 201

Indirect deep structured learning for 3D human body shape and pose prediction

In this paper we present a novel method for 3D human body shape and pose prediction. Our work is motivated by the need to reduce our reliance on costly-to-obtain ground truth labels. To achieve this, we propose training an encoder-decoder network using a two step procedure as follows. During the first step, a decoder is trained to predict a body silhouette using SMPL (a statistical body shape model) parameters as an input. During the second step, the whole network is trained on real image and corresponding silhouette pairs while the decoder is kept fixed. Such a procedure allows for an indirect learning of body shape and pose parameters from real images without requiring any ground truth parameter data. Our key contributions include: (a) a novel encoder-decoder architecture for 3D body shape and pose prediction, (b) corresponding training procedure as well as (c) quantitative and qualitative analysis of the proposed method on artificial and real image datasets

PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation

We consider the problem of reconstructing a 3D mesh of the human body from a single 2D image as a model-in-the-loop optimization problem. Existing approaches often regress the shape, pose, and translation parameters of a parametric statistical model assuming a weak-perspective camera. In contrast, we first estimate 2D pixel-aligned vertices in image space and propose PLIKS (Pseudo-Linear Inverse Kinematic Solver) to regress the model parameters by minimizing a linear least squares problem. PLIKS is a linearized formulation of the parametric SMPL model, which provides an optimal pose and shape solution from an adequate initialization. Our method is based on analytically calculating an initial pose estimate from the network predicted 3D mesh followed by PLIKS to obtain an optimal solution for the given constraints. As our framework makes use of 2D pixel-aligned maps, it is inherently robust to partial occlusion. To demonstrate the performance of the proposed approach, we present quantitative evaluations which confirm that PLIKS achieves more accurate reconstruction with greater than 10% improvement compared to other state-of-the-art methods with respect to the standard 3D human pose and shape benchmarks while also obtaining a reconstruction error improvement of 12.9 mm on the newer AGORA dataset

Human Shape Estimation using Statistical Body Models

Human body estimation methods transform real-world observations into predictions about human body state. These estimation methods benefit a variety of health, entertainment, clothing, and ergonomics applications. State may include pose, overall body shape, and appearance. Body state estimation is underconstrained by observations; ambiguity presents itself both in the form of missing data within observations, and also in the form of unknown correspondences between observations. We address this challenge with the use of a statistical body model: a data-driven virtual human. This helps resolve ambiguity in two ways. First, it fills in missing data, meaning that incomplete observations still result in complete shape estimates. Second, the model provides a statistically-motivated penalty for unlikely states, which enables more plausible body shape estimates. Body state inference requires more than a body model; we therefore build obser- vation models whose output is compared with real observations. In this thesis, body state is estimated from three types of observations: 3D motion capture markers, depth and color images, and high-resolution 3D scans. In each case, a forward process is proposed which simulates observations. By comparing observations to the results of the forward process, state can be adjusted to minimize the difference between simulated and observed data. We use gradient-based methods because they are critical to the precise estimation of state with a large number of parameters. The contributions of this work include three parts. First, we propose a method for the estimation of body shape, nonrigid deformation, and pose from 3D markers. Second, we present a concise approach to differentiating through the rendering process, with application to body shape estimation. And finally, we present a statistical body model trained from human body scans, with state-of-the-art fidelity, good runtime performance, and compatibility with existing animation packages