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Elliptical weighted average (EWA) surface splatting is a technique for high quality rendering of point-sampled 3D objects. EWA surface splatting renders water-tight surfaces of complex point models with high quality, anisotropic texture filtering. In this paper we introduce a new multi-pass approach to perform EWA surface splatting on modern PC graphics(More)
This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then(More)
We present a vision-based performance interface for controlling animated human characters. The system interactively combines information about the user's motion contained in silhouettes from three viewpoints with domain knowledge contained in a motion capture database to produce an animation of high quality. Such an interactive system might be useful for(More)
In this paper, we investigate whether it is possible to develop a measure that quantifies the naturalness of human motion (as defined by a large database). Such a measure might prove useful in verifying that a motion editing operation had not destroyed the naturalness of a motion capture clip or that a synthetic motion transition was within the space of(More)
We present a hardware-accelerated adaptive EWA (elliptical weighted average) volume splatting algorithm. EWA splatting combines a Gaussian reconstruction kernel with a low-pass image filter for high image quality without aliasing artifacts or excessive blurring. We introduce a novel adaptive filtering scheme to reduce the computational cost of EWA(More)
Dynamic occlusion handling is critical for correct depth perception in Augmented Reality (AR) applications. Consequently it is a key component to ensure realistic and immersive AR experiences. Existing solutions to tackle this challenge typically suffer from various limitations, e.g. assumption of a static scene or high computational complexity. In this(More)
Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNN, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g.,(More)
For most iris capturing scenarios, captured iris images could easily blur when the user is out of the depth of field (DOF) of the camera, or when he or she is moving. The common solution is to let the user try the capturing process again as the quality of these blurred iris images is not good enough for recognition. In this paper, we propose a novel iris(More)
In this paper we propose a novel method called σ -DVO for dense visual odometry using a probabilistic sensor noise model. In contrast to sparse visual odometry, where camera poses are estimated based on matched visual features, we apply dense visual odometry which makes full use of all pixel information from an RGB-D camera. Previously, t-distribution was(More)