MonoPerfCap: Human Performance Capture from Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201

Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body is a time consuming and labour intensive postprocessing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework

Towards a Smart Drone Cinematographer for Filming Human Motion

Affordable consumer drones have made capturing aerial footage more convenient and accessible. However, shooting cinematic motion videos using a drone is challenging because it requires users to analyze dynamic scenarios while operating the controller. In this thesis, our task is to develop an autonomous drone cinematography system to capture cinematic videos of human motion. We understand the system's filming performance to be influenced by three key components: 1) video quality metric, which measures the aesthetic quality -- the angle, the distance, the image composition -- of the captured video, 2) visual feature, which encapsulates the visual elements that influence the filming style, and 3) camera planning, which is a decision-making model that predicts the next best movement. By analyzing these three components, we designed two autonomous drone cinematography systems using both heuristic-based methods and learning-based methods.For the first system, we designed an Autonomous CinemaTography system -- "ACT" by proposing a viewpoint quality metric focusing on the visibility of the 3D human skeleton of the subject. We expanded the application of human motion analysis and simplified manual control by assisting viewpoint selection using a through-the-lens method. For the second system, we designed an imitation-based system that learns the artistic intention of the cameramen through watching professional aerial videos. We designed a camera planner that analyzes the video contents and previous camera motion to predict future camera motion. Furthermore, we propose a planning framework, which can imitate a filming style by ``seeing" only one single demonstration video of such style. We named it ``one-shot imitation filming." To the best of our knowledge, this is the first work that extends imitation learning to autonomous filming. Experimental results in both simulation and field test exhibit significant improvements over existing techniques and our approach managed to help inexperienced pilots capture cinematic videos

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.Comment: Accepted to SIGGRAPH 201

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables 3D human pose estimation using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.Comment: 12 pages, Accepted at Eurographics 201

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012