Algebraic Point Set Surfaces (APSS) define a smooth surface from a set of points using local moving least-squares (MLS) fitting of algebraic spheres. In this paper we first revisit the spherical fitting problem and provide a new, more generic solution that includes intuitive parameters for curvature control of the fitted spheres. As a second contribution we present a novel real-time rendering system of such surfaces using a dynamic up-sampling strategy combined with a conventional splatting algorithm for high quality rendering. Our approach also includes a new view dependent geometric error tailored to efficient and adaptive up-sampling of the surface. One of the key features of our system is its high degree of flexibility that enables us to achieve high performance even for highly dynamic data or complex models by exploiting temporal coherence at the primitive level. We also address the issue of efficient spatial search data structures with respect to construction, access and GPU friendliness. Finally, we present an efficient parallel GPU implementation of the algorithms and search structures.

We present a novel representation and rendering method for free-viewpoint video of human characters based on multiple input video streams. The basic idea is to approximate the articulated 3D shape of the human body using a subdivision into textured billboards along the skeleton structure. Billboards are clustered to fans such that each skeleton bone contains one billboard per source camera. We call this representation articulated billboards. In the paper we describe a semi-automatic, data-driven algorithm to construct and render this representation, which robustly handles even challenging acquisition scenarios characterized by sparse camera positioning, inaccurate camera calibration, low video resolution, or occlusions in the scene. First, for each input view, a 2D pose estimation based on image silhouettes, motion capture data, and temporal video coherence is used to create a segmentation mask for each body part. Then, from the 2D poses and the segmentation, the actual articulated billboard model is constructed by a 3D joint optimization and compensation for camera calibration errors. The rendering method includes a novel way of blending the textural contributions of each billboard and features an adaptive seam correction to eliminate visible discontinuities between adjacent billboards textures. Our articulated billboards do not only minimize ghosting artifacts known from conventional billboard rendering, but also alleviate restrictions to the setup and sensitivities to errors of more complex 3D representations and multiview reconstruction techniques. Our results demonstrate the flexibility and the robustness of our approach with high quality free-viewpoint video generated from broadcast footage of challenging, uncontrolled environments.

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We propose a data-driven, multi-view body pose estimation algorithm for video. It can operate in uncontrolled environments with loosely calibrated and low resolution cameras and without restricting assumptions on the family of possible poses or motions.Our algorithm first estimates a rough pose estimation using a spatial and temporal silhouette based search in a database of known poses. The estimated pose is improved in a novel pose consistency step acting locally on single frames and globally over the entire sequence. Finally, the resulting pose estimation is refined in a spatial and temporal pose optimization consisting of novel constraints to obtain an accurate pose. Our method proved to perform well on low resolution video footage from real broadcast of soccer games.