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Synthesizing Physically Realistic Human Motion in Low-Dimensional, Behavior- Specific Spaces SIGGRAPH ’ 04 Speaker: Alvin Date: 13 July 2004.

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Presentation on theme: "Synthesizing Physically Realistic Human Motion in Low-Dimensional, Behavior- Specific Spaces SIGGRAPH ’ 04 Speaker: Alvin Date: 13 July 2004."— Presentation transcript:

1 Synthesizing Physically Realistic Human Motion in Low-Dimensional, Behavior- Specific Spaces SIGGRAPH ’ 04 Speaker: Alvin Date: 13 July 2004

2 Outline IntroductionIntroduction ObservationsObservations FrameworkFramework ResultsResults ConclusionsConclusions Evaluation FormEvaluation Form

3 Introduction A optimization method.A optimization method. Reduce the dimension of the search space using PCA technique.Reduce the dimension of the search space using PCA technique. Use a motion capture database.Use a motion capture database. Use a sparse sketch as an initial guess.Use a sparse sketch as an initial guess.

4 Observations Many dynamic human motion can be represented with only 5 to 10 DOF.Many dynamic human motion can be represented with only 5 to 10 DOF. Motions with similar behavior can be used to construct a low-dimensional space that can represent well other sample of the same behavior.Motions with similar behavior can be used to construct a low-dimensional space that can represent well other sample of the same behavior.

5 Error

6 Framework Specifies a rough sketch of the desired motion and the constraints.Specifies a rough sketch of the desired motion and the constraints. Select the motions used to define the low- dimensional space for the desired motion.Select the motions used to define the low- dimensional space for the desired motion. Constrained optimization.Constrained optimization.

7 Low-dimensional Problem Representation B 1 … B d are the unit length orthogonal vectors. Q(t) is a point in an n-dimensional space. Q ’ (t) = Q m + B 1 A 1 (t)+B 2 A 2 (t)+ … +B d A d (t) A 1 … A d are the scalar coefficients.

8 Objective Function

9 Results

10 Conclusions Optimization of human motion is more effectively in a lower-dimensional space.Optimization of human motion is more effectively in a lower-dimensional space. Effective for different human motions, both highly and less dynamic.Effective for different human motions, both highly and less dynamic. Provides good control for the animator.Provides good control for the animator.

11 Evaluation Form 論文簡報部份 論文簡報部份 完整性介紹 (3) 完整性介紹 (3) 系統性介紹 (3) 系統性介紹 (3) 表達能力 (3) 表達能力 (3) 投影片製作 (3) 投影片製作 (3) 論文審閱部分 瞭解論文內容 (4) 結果正確性與完整性 (4) 原創性與重要性 (4) 讀後啟發與應用: 減少動畫資料的維度,可以增加找出最佳解的速度,定義出相似的招式所構 築出來的較低維度空間,更可以幫助我們定義招式之間相生的關係。


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