1 / 22

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

hartwell-carl
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related