Enhanced dynamic reflectometry for relightable free-viewpoint video

Free-Viewpoint Video of Human Actors allows photo- realistic rendering of real-world people under novel viewing conditions. Dynamic Reflectometry extends the concept of free-view point video and allows rendering in addition under novel lighting conditions. In this work, we present an enhanced method for capturing human shape and motion as well as dynamic surface reflectance properties from a sparse set of input video streams. We augment our initial method for model-based relightable free-viewpoint video in several ways. Firstly, a single-skin mesh is introduced for the continuous appearance of the model. Moreover an algorithm to detect and compensate lateral shifting of textiles in order to improve temporal texture registration is presented. Finally, a structured resampling approach is introduced which enables reliable estimation of spatially varying surface reflectance despite a static recording setup. The new algorithm ingredients along with the Relightable 3D Video framework enables us to realistically reproduce the appearance of animated virtual actors under different lighting conditions, as well as to interchange surface attributes among different people, e.g. for virtual dressing. Our contribution can be used to create 3D renditions of real-world people under arbitrary novel lighting conditions on standard graphics hardware

A flexible and versatile studio for synchronized multi-view video recording

In recent years, the convergence of Computer Vision and Computer Graphics has put forth new research areas that work on scene reconstruction from and analysis of multi-view video footage. In free-viewpoint video, for example, new views of a scene are generated from an arbitrary viewpoint in real-time from a set of real multi-view input video streams. The analysis of real-world scenes from multi-view video to extract motion information or reflection models is another field of research that greatly benefits from high-quality input data. Building a recording setup for multi-view video involves a great effort on the hardware as well as the software side. The amount of image data to be processed is huge, a decent lighting and camera setup is essential for a naturalistic scene appearance and robust background subtraction, and the computing infrastructure has to enable real-time processing of the recorded material. This paper describes the recording setup for multi-view video acquisition that enables the synchronized recording of dynamic scenes from multiple camera positions under controlled conditions. The requirements to the room and their implementation in the separate components of the studio are described in detail. The efficiency and flexibility of the room is demonstrated on the basis of the results that we obtain with a real-time 3D scene reconstruction system, a system for non-intrusive optical motion capture and a model-based free-viewpoint video system for human actors.

Towards virtual communities on the Web: Actors and audience

We report about ongoing research in a virtual reality environment where visitors can interact with agents that help them to obtain information, to perform certain transactions and to collaborate with them in order to get some tasks done. Our environment models a theatre in our hometown. We discuss attempts to let this environment evolve into a theatre community where we do not only have goal-directed visitors, but also visitors that that are not sure whether they want to buy or just want information or visitors who just want to look around. It is shown that we need a multi-user and multiagent environment to realize our goals. Since our environment models a theatre it is also interesting to investigate the roles of performers and audience in this environment. For that reason we discuss capabilities and personalities of agents. Some notes on the historical development of networked communities are included

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

Fast human activity recognition based on structure and motion

This is the post-print version of the final paper published in Pattern Recognition Letters. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.We present a method for the recognition of human activities. The proposed approach is based on the construction of a set of templates for each activity as well as on the measurement of the motion in each activity. Templates are designed so that they capture the structural and motion information that is most discriminative among activities. The direct motion measurements capture the amount of translational motion in each activity. The two features are fused at the recognition stage. Recognition is achieved in two steps by calculating the similarity between the templates and motion features of the test and reference activities. The proposed methodology is experimentally assessed and is shown to yield excellent performance.European Commissio