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Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis

Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis. SIGGRAPH ‘02 Speaker: Alvin Date: 3 August 2004. Outline. Introduction Framework Result Conclusion Evaluation Form. Introduction. Motion Texture – A two-level statistical model Texton Local dynamics

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Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis

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  1. Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis SIGGRAPH ‘02 Speaker: Alvin Date: 3 August 2004

  2. Outline • Introduction • Framework • Result • Conclusion • Evaluation Form Alivn/GAME Lab./CSIE/NDHU

  3. Introduction • Motion Texture – A two-level statistical model • Texton • Local dynamics • Represented by a linear dynamic system (LDS). • Distribution • Global dynamics • Modeled by a transition matrix • Counting how many times a texton is switched to another. Alivn/GAME Lab./CSIE/NDHU

  4. Two-level Statistical Model Alivn/GAME Lab./CSIE/NDHU

  5. Motion Texton • State-Space model (LDS): Xt – Hidden State Variable Yt – The Observation Vt, Wt – Independent Gaussian noises at time t. Alivn/GAME Lab./CSIE/NDHU

  6. Distribution Commonly used in HMMs Alivn/GAME Lab./CSIE/NDHU

  7. Introduction (cont.) • Statistically similar to the original motion. • Motion textures display a 1-D temporal distribution. • User can synthesis and edit at both the texton level and the distribution level. Alivn/GAME Lab./CSIE/NDHU

  8. Framework • Learning • E-step • M-step • Synthesis • Texton Path Planning • Texton Synthesis • By Sampling Noise • With Constrained LDS Alivn/GAME Lab./CSIE/NDHU

  9. E-step Segment Labels as L = {l1,l2,…, lNs} Segmentation points as H = {H1,H2,…,HNs} Alivn/GAME Lab./CSIE/NDHU

  10. M-step Alivn/GAME Lab./CSIE/NDHU

  11. Learning • Initialization - A greedy approach: Until the entire sequence is processed: • Use Tmin to fit LDSi • Label the subsequent frames to segment i until the fitting error is above a threshold. • Test all existing LDS’ to choose the best-fit LDS. • If no LDS fits well, introduce a new LDS. Alivn/GAME Lab./CSIE/NDHU

  12. Learning (cont.) • The bigger the threshold, the longer the segments, and the fewer the number of textons. • Model selection methods: • BIC • MDL • Tmin must be long enough to capture the local dynamics. • Approximately one second. Alivn/GAME Lab./CSIE/NDHU

  13. Texton Path Planning • find a single best path, , which starts at and ends at . • Two approaches: • Finding the Lowest Cost Path • Dijkstra’s algorithm • Specifying the Path Length • Dynamic Programming Alivn/GAME Lab./CSIE/NDHU

  14. Texton Synthesis • By sampling noise • Inevitably depart from the original motion as time progresses. • LDS learns only locally consistent motion patterns. • The synthesis errors will accumulate as xt propagates. • With constrained LDS • Setting the end constraints. • The in-between frames can be synthesized by solving a block-banded system of linear equations. Alivn/GAME Lab./CSIE/NDHU

  15. Result • Environment • Intel P4 1.4G • 1G Memory • Input • Capture 20 minutes of dance motion. (49800 frames) • Result • It took about 4 hours to learn. • 246 textons are found. • The length of the texton ranges from 60 to 172 frames. • Synthesizing a texton only 25ms to 35ms. (Real-time) Alivn/GAME Lab./CSIE/NDHU

  16. Result (cont.) Alivn/GAME Lab./CSIE/NDHU

  17. Result (cont.) Alivn/GAME Lab./CSIE/NDHU

  18. Result (cont.) Alivn/GAME Lab./CSIE/NDHU

  19. Result (cont.) Alivn/GAME Lab./CSIE/NDHU

  20. Conclusion • Best suited for repeated motions. • Lack global variation when the data is limited. • Did not incorporate any physical model into the synthesis algorithm. • Capture the essential properties of the original motion. Alivn/GAME Lab./CSIE/NDHU

  21. Conclusion (cont.) • The edited pose can not deviate from the original one too much. • The additional constraint may contaminate the synthesized texton. • Does not consider the interaction with environment. • Initialization can be improved. Alivn/GAME Lab./CSIE/NDHU

  22. 論文簡報部份 完整性介紹(3) 系統性介紹(4) 表達能力 (3) 投影片製作(3) 論文審閱部分 瞭解論文內容(3) 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： Evaluation Form When we meet a problem that its input is highly repeating, we can use the statistical method to find the basic element. Alivn/GAME Lab./CSIE/NDHU

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