Evaluating motion graphs for character animation

Reitsma, Paul S. A.; Pollard, Nancy S.

doi:10.1145/1289603.1289609

Cited by 58 publications

(38 citation statements)

References 39 publications

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“…Motion graphs have been introduced in computer animation to organize a database of motion as a set of nodes (indicating poses or small clips) and edges (indicating possible connection between nodes without discontinuity) [23]. It has been used extensively to automatically combine motion clips to produce and control character animations [18,31,32]. A large motion database usually results in a dense graph with a large number of nodes and edges.…”

Section: Motion Database Structurementioning

confidence: 99%

Filtered pose graph for efficient kinect pose reconstruction

Plantard

Shum

Multon

2016

Multimed Tools Appl

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Being marker-free and calibration free, Microsoft Kinect is nowadays widely used in many motion-based applications, such as user training for complex industrial tasks and ergonomics pose evaluation. The major problem of Kinect is the placement requirement to obtain accurate poses, as well as its weakness against occlusions. To improve the robustness of Kinect in interactive motion-based applications, real-time data-driven pose reconstruction has been proposed. The idea is to utilize a database of accurately captured human poses as a prior to optimize the Kinect recognized ones, in order to estimate the true poses performed by the user. The key research problem is to identify the most relevant poses in the database for accurate and efficient reconstruction. In this paper, we propose a new pose reconstruction method based on modelling the pose database with a structure called Filtered Pose Graph, which indicates the intrinsic correspondence between poses. Such a graph not only speeds up the database poses selection process, but also improves the relevance of the selected poses for higher quality reconstruction. We apply the proposed method in a challenging environment of industrial context that involves sub-optimal Kinect placement and a large amount of occlusion. Experimental results show that our real-time system reconstructs Kinect poses more accurately than existing methods.

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Section: Motion Database Structurementioning

confidence: 99%

Filtered pose graph for efficient kinect pose reconstruction

Plantard

Shum

Multon

2016

Multimed Tools Appl

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“…Our work leverages was inspired by SteerBench [SKN * 09] and [RP07]. The work in [RP07] presented a method of calculating coverage of motion graphs for a set of animation and navigation benchmarks.…”

Section: Comparison To Related Workmentioning

confidence: 99%

“…The work in [RP07] presented a method of calculating coverage of motion graphs for a set of animation and navigation benchmarks. SteerBench proposed an objective set of test cases and an ad hoc, automatic method of scoring the performance of steering algorithms.…”

Section: Comparison To Related Workmentioning

confidence: 99%

Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms

2016

Simulating Heterogeneous Crowd With Interactive Behaviors

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Navigation and steering in complex dynamically changing environments is a challenging research problem, and a fundamental aspect of immersive virtual worlds. While there exist a wide variety of approaches for navigation and steering, there is no definitive solution for evaluating and analyzing steering algorithms. Evaluating a steering algorithm involves two major challenges: (a) characterizing and generating the space of possible scenarios that the algorithm must solve, and (b) defining evaluation criteria (metrics) and applying them to the solution. In this paper, we address both of these challenges. First, we characterize and analyze the complete space of steering scenarios that an agent may encounter in dynamic situations. Then, we propose the representative scenario space and a sampling method that can generate subsets of the representative space with good statistical properties. We also propose a new set of metrics and a statistically robust approach to determining the coverage and the quality of a steering algorithm in this space. We demonstrate the effectiveness of our approach on three state of the art techniques. Our results show that these methods can only solve 60% of the scenarios in the representative scenario space. Disciplines Computer Sciences | Engineering | Graphics and Human Computer InterfacesThis conference paper is available at ScholarlyCommons: http://repository.upenn.edu/hms/170

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“…The Motion Graph approach [Arikan and Forsyth 2002;Lee et al 2002;Kovar et al 2002;Min and Chai 2012;Beaudoin et al 2008;Arikan et al 2003;Li et al 2002] is a method to interactively reproduce continuous motions based on a graph generated from captured motion data. Reitsma and Pollard [2007] compared different motion graph techniques comprehensively. Heck et al [2007] further parametrized the motion space to control how the motions are being generated by blending samples in the motion graph.…”

Section: Related Workmentioning

confidence: 99%

SkillVis

Shum

Wang

et al. 2016

Proceedings of the 9th International Conference on Motion in Games

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Figure 1: Our system visualizes high-level boxing skills such as the richness of actions and the transition of them with a graph structure. A graph representation is employed with the characters of different sizes representing nodes with different sizes and edges representing groups of actions. AbstractMotion analysis and visualization are crucial in sports science for sports training and performance evaluation. While primitive computational methods have been proposed for simple analysis such as postures and movements, few can evaluate the high-level quality of sports players such as their skill levels and strategies. We propose a visualization tool to help visualizing boxers' motions and assess their skill levels. Our system automatically builds a graph-based representation from motion capture data and reduces the dimension of the graph onto a 3D space so that it can be easily visualized and understood. In particular, our system allows easy understanding of the boxer's boxing behaviours, preferred actions, potential strength and weakness. We demonstrate the effectiveness of our system on different boxers' motions. Our system not only serves as a tool for visualization, it also provides intuitive motion analysis that can be further used beyond sports science.

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Evaluating motion graphs for character animation

Cited by 58 publications

References 39 publications

Filtered pose graph for efficient kinect pose reconstruction

Filtered pose graph for efficient kinect pose reconstruction

Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms

SkillVis

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