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Journal ArticleDOI

Synthesis and evaluation of linear motion transitions

TLDR
This article develops methods for determining visually appealing motion transitions using linear blending, and assess the importance of these techniques by determining the minimum sensitivity of viewers to transition durations, the just noticeable difference, for both center-aligned and start-end specifications.
Abstract
This article develops methods for determining visually appealing motion transitions using linear blending. Motion transitions are segues between two sequences of animation, and are important components for generating compelling animation streams in virtual environments and computer games. Methods involving linear blending are studied because of their efficiency, computational speed, and widespread use. Two methods of transition specification are detailed, center-aligned and start-end transitions. First, we compute a set of optimal weights for an underlying cost metric used to determine the transition points. We then evaluate the optimally weighted cost metric for generalizability, appeal, and robustness through a cross-validation and user study. Next, we develop methods for computing visually appealing blend lengths for two broad categories of motion. We empirically evaluate these results through user studies. Finally, we assess the importance of these techniques by determining the minimum sensitivity of viewers to transition durations, the just noticeable difference, for both center-aligned and start-end specifications.

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Citations
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Journal ArticleDOI

A concise and provably informative multi-scale signature based on heat diffusion

TL;DR: The Heat Kernel Signature, called the HKS, is obtained by restricting the well‐known heat kernel to the temporal domain and shows that under certain mild assumptions, HKS captures all of the information contained in the heat kernel, and characterizes the shape up to isometry.
Journal ArticleDOI

HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion

TL;DR: A baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering is described, and a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters are explored.
Journal ArticleDOI

Coupled Dictionary Training for Image Super-Resolution

TL;DR: This paper demonstrates that the coupled dictionary learning method can outperform the existing joint dictionary training method both quantitatively and qualitatively and speed up the algorithm approximately 10 times by learning a neural network model for fast sparse inference and selectively processing only those visually salient regions.
Journal ArticleDOI

PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation

TL;DR: This article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems.
Journal ArticleDOI

Advances in three-dimensional integral imaging: sensing, display, and applications [Invited]

TL;DR: This Commemorative Review presents an overview of literature on physical principles and applications of integral imaging, and applications including 3D underwater imaging, 3D imaging in photon-starved environments, 2D tracking of occluded objects,3D optical microscopy, and 3D polarimetric imaging are reviewed.
References
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Book

Signal detection theory and psychophysics

TL;DR: This book discusses statistical decision theory and sensory processes in signal detection theory and psychophysics and describes how these processes affect decision-making.
Book

Detection Theory: A User's Guide

TL;DR: This book discusses Detection and Discrimination of Compound Stimuli: Tools for Multidimensional Detection Theory and Multi-Interval Discrimination Designs and Adaptive Methods for Estimating Empirical Thresholds.
Journal ArticleDOI

Visual perception of biological motion and a model for its analysis

TL;DR: The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to biological motion patterns and the results turned out to be highly positive.