Evaluating Example-based Pose Estimation: Experiments on the HumanEva Sets

We present an example-based approach to pose recovery, using histograms of oriented gradients as image descriptors. Tests on the HumanEva-I and HumanEva-II data sets provide us insight into the strengths and limitations of an example-based approach. We report mean relative 3D errors of approximately 65 mm per joint on HumanEva-I, and 175 mm on HumanEva-II. We discuss our results using single and multiple views. Also, we perform experiments to assess the algorithm’s generalization to unseen subjects, actions and viewpoints. We plan to incorporate the temporal aspect of human motion analysis to reduce orientation ambiguities, and increase the pose recovery accuracy

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

Recognition-Based Motion Capture and the HumanEva II Test Data

Quantitative comparison of algorithms for human motion capture have been hindered by the lack of standard benchmarks. The development of the HumanEva I & II test sets provides an opportunity to assess the state of the art by evaluating existing methods on the new standardized test videos. This paper presents a comprehensive evaluation of a monocular recognition-based pose recovery algorithm on the HumanEva II clips. The results show that the method achieves a mean relative error of around 10-12 cm per joint

Coupled Action Recognition and Pose Estimation from Multiple Views

Action recognition and pose estimation are two closely related topics in understanding human body movements; information from one task can be leveraged to assist the other, yet the two are often treated separately. We present here a framework for coupled action recognition and pose estimation by formulating pose estimation as an optimization over a set of action-specific manifolds. The framework allows for integration of a 2D appearance-based action recognition system as a prior for 3D pose estimation and for refinement of the action labels using relational pose features based on the extracted 3D poses. Our experiments show that our pose estimation system is able to estimate body poses with high degrees of freedom using very few particles and can achieve state-of-the-art results on the HumanEva-II benchmark. We also thoroughly investigate the impact of pose estimation and action recognition accuracy on each other on the challenging TUM kitchen dataset. We demonstrate not only the feasibility of using extracted 3D poses for action recognition, but also improved performance in comparison to action recognition using low-level appearance feature

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