Harvesting Multiple Views for Marker-less 3D Human Pose Annotations

Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.Comment: CVPR 2017 Camera Read

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

Human poses and motions are important cues for analysis of videos with people and there is strong evidence that representations based on body pose are highly effective for a variety of tasks such as activity recognition, content retrieval and social signal processing. In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis. The benchmark encompasses three competition tracks focusing on i) single-frame multi-person pose estimation, ii) multi-person pose estimation in videos, and iii) multi-person articulated tracking. To facilitate the benchmark and challenge we collect, annotate and release a new %large-scale benchmark dataset that features videos with multiple people labeled with person tracks and articulated pose. A centralized evaluation server is provided to allow participants to evaluate on a held-out test set. We envision that the proposed benchmark will stimulate productive research both by providing a large and representative training dataset as well as providing a platform to objectively evaluate and compare the proposed methods. The benchmark is freely accessible at https://posetrack.net.Comment: www.posetrack.ne

Personalization framework for adaptive robotic feeding assistance

The final publication is available at link.springer.comThe deployment of robots at home must involve robots with pre-defined skills and the capability of personalizing their behavior by non-expert users. A framework to tackle this personalization is presented and applied to an automatic feeding task. The personalization involves the caregiver providing several examples of feeding using Learning-by- Demostration, and a ProMP formalism to compute an overall trajectory and the variance along the path. Experiments show the validity of the approach in generating different feeding motions to adapt to user’s preferences, automatically extracting the relevant task parameters. The importance of the nature of the demonstrations is also assessed, and two training strategies are compared. © Springer International Publishing AG 2016.Peer ReviewedPostprint (author's final draft

Real-time factored ConvNets: Extracting the x factor in human parsing

© 2017. The copyright of this document resides with its authors. We propose a real-time and lightweight multi-task style ConvNet (termed a Factored ConvNet) for human body parsing in images or video. Factored ConvNets have isolated areas which perform known sub-tasks, such as object localization or edge detection. We call this area and sub-task pair an X factor. Unlike multi-task ConvNets which have independent tasks, the Factored ConvNet’s sub-task has direct effect on the main task outcome. In this paper we show how to isolate the X factor of foreground/background (f/b) subtraction from the main task of segmenting human body images into 31 different body part types. Knowledge of this X factor leads to a number of benefits for the Factored ConvNet: 1) Ease of network transfer to other image domains, 2) ability to personalize to humans in video and 3) easy model performance boosts. All achieved by either efficient network update or replacement of the X factor whilst avoiding catastrophic forgetting of previously learnt body part dependencies and structure. We show these benefits on a large dataset of images and also on YouTube videos.SeeQuesto

Fully Automatic Multi-Object Articulated Motion Tracking

Fully automatic tracking of articulated motion in real-time with a monocular RGB camera is a challenging problem which is essential for many virtual reality (VR) and human-computer interaction applications. In this paper, we present an algorithm for multiple articulated objects tracking based on monocular RGB image sequence. Our algorithm can be directly employed in practical applications as it is fully automatic, real-time, and temporally stable. It consists of the following stages: dynamic objects counting, objects specific 3D skeletons generation, initial 3D poses estimation, and 3D skeleton fitting which fits each 3D skeleton to the corresponding 2D body-parts locations. In the skeleton fitting stage, the 3D pose of every object is estimated by maximizing an objective function that combines a skeleton fitting term with motion and pose priors. To illustrate the importance of our algorithm for practical applications, we present competitive results for real-time tracking of multiple humans. Our algorithm detects objects that enter or leave the scene, and dynamically generates or deletes their 3D skeletons. This makes our monocular RGB method optimal for real-time applications. We show that our algorithm is applicable for tracking multiple objects in outdoor scenes, community videos, and low-quality videos captured with mobile-phone cameras. Keywords: Multi-object motion tracking, Articulated motion capture, Deep learning, Anthropometric data, 3D pose estimation. DOI: 10.7176/CEIS/12-1-01 Publication date: March 31st 202