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Summary & Homework

Summary & Homework. Jinxiang Chai. Outline. Motion data process paper summary Presentation tips Homework Paper assignment. Skeleton Model Extraction. Estimate the skeleton model from motion capture data. Motion Data Processing I. Operations on single motion sequence

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Summary & Homework

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  1. Summary & Homework Jinxiang Chai

  2. Outline • Motion data process paper summary • Presentation tips • Homework • Paper assignment

  3. Skeleton Model Extraction • Estimate the skeleton model from motion capture data

  4. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints)

  5. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints) • Motion retargeting (can satisfy user constraints but slow)

  6. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints) • Motion retargeting (can satisfy user constraints but slow) • Motion edit (can satisfy user constraints and fast)

  7. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints) • Motion retargeting (can satisfy user constraints but slow) • Motion edit (can satisfy user constraints and fast) However: does not work for many user-defined constraints does not utilize spatial-temporal correlation in human motion

  8. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints) • Motion retargeting (can satisfy user constraints but slow) • Motion edit (can satisfy user constraints and fast) • Expression cloning (facial data) (cannot satisfy user constraints)

  9. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints) • Motion retargeting (can satisfy user constraints but slow) • Motion edit (can satisfy user constraints and fast) • Expression cloning (facial data) (cannot satisfy user constraints) • Motion synopsis

  10. Motion Data Processing I • Operations on single motion sequence • Motion warping (cannot satisfy user constraints) • Motion retargeting (can satisfy user constraints but slow) • Motion edit (can satisfy user constraints and fast) • Expression cloning (facial data) (cannot satisfy user constraints) • Motion synopsis • What else?

  11. Motion Data Processing I • Operations on single motion sequence • Motion segmentation? • Motion recognition? • Motion filtering? • More operations in facial data

  12. Motion Data Processing II • Operations between two motion sequence • Motion style translation • What else?

  13. Motion Data Processing II • Operations between two motion sequence • Motion style translation • What else?

  14. Motion Data Processing II • Operations between two motion sequence • Motion style translation • Motion interpolation?

  15. Motion Data Processing III • Operations on multiple motion sequences/database • Motion data compression

  16. Motion Data Processing III • Operations on multiple motion sequences/database • Motion data compression • What else?

  17. Motion Data Processing III • Operations on multiple motion sequences/database • Motion data compression • Motion Retrieval? • Motion synthesis?

  18. Presentation Tips • Choose right background • Use blue or black or dark colors • Focus on important things • Always talk about what, why, and how • Detailize the most important technical content • Always talk about the limitations of the paper • How to deal with equations • Avoid using too many equations • Talk about the intuition of the equations • Explain each term of the equations

  19. Presentation Tips (Cont.) • Choose the right background • Use dark colors (blue or black) • Be consistent • Font type, size, color, bullets, figure captions etc. • Use short & concise sentences • Do not copy & paste from the paper • Use images, diagrams, figures, videos to demonstrate ideas

  20. Presentation Tips (Cont.) • Play videos • Explain each video • Move the mouse away from the window

  21. Example Slides

  22. Interfaces for Controlling Human Motion Goal: everyone can generate and control human motion easily and quickly Online animation control

  23. Applications: Online Animation Performance-based facial animation for home use Friday Night 3D Bowling Mike Tyson Heavyweight Boxing Tiger Woods PGA Tour 2005 Xbox Outlaw Tennis Multi-user Virtual Worlds Virtual Presenter Virtual Poker Room Teleconferencing (from BT)

  24. Animation from Mocap Data Reordering motion clip [Lee et al. SIG02, Kovar et al. SIG02, Pullen & Bregler SIG02, Arikan et al. SIG03] Learning model from human motion[Brand & Hertzmann SIG00, Li et al. SIG02] Interpolating motions[Rose et al. CG&A98, Kovar et al. SIG04]

  25. Overview Preprocessed motion capture data Preprocessed motion capture data Online motion synthesis Online local modeling Video Analysis Online local models Low-dimensional control signals

  26. Online Motion Optimization … ? … Optimizing a nonlinear function in about 7 dimensional space wt : Control term Smoothness term Pose prior term

  27. Outline • Motion data paper summary • Presentation tips • Homework • Paper assignment

  28. Linear Dimensionality Reduction Dimensionality reduction using PCA Eigen-poses Mean pose Linear model: Dim(wt)? Current pose Mean pose Eigen-poses Low-dimensional space

  29. Linear Dimensionality Reduction 30 PCA (38D for error<1o) r 25 o r ) r e e l g n n o 20 i a t c t n u i r o t s j 15 n r e o p c e e r e r e 10 g g e a d r ( e v A 5 0 0 5 15 10 20 25 30 35 40 45 50 Number of dimensions A large heterogeneous database (1 hour of data)

  30. Reconstruction Error Curve r o r ) r e e l g n n o i a t c t n u i r o t s j n r e o p c e e r e r e g g e a d r ( e v A 2.5 2 1.5 1 0.5 0 0 10 20 30 40 50 60 Number of dimensions of Wt A large database

  31. Minimal Dimensionality r o r ) r e e l g n n o i a t c t n u i r o t s j n r e o p c e e r e r e g g e a d r ( e v A 2.5 56 dim 7 dim 2 1.5 1 0.8 0.5 0 7 0 10 20 30 40 50 60 Number of dimensions of Wt A large database

  32. Videos • Two Animation videos rendered by Maya • Original mocap sequence • Reconstructed mocap sequence

  33. Startup Codes & Data • Motion capture data (.amc files and .asf file) • A skinned character model • Visualize amc/asf file • Read asf/amc file • Visualize mocap data • Forward kinematics • Matlab codes • Read and write .amc file

  34. Softwares • Maya • To be installed in Rm 220, HRBB • Render each frame based on mocap data and character model • Instruction on how to render animation • Adobe Premier • Installed in Rm 220, HRBB • Making video from image sequences

  35. Outline • Motion data paper summary • Presentation tips • Homework • Paper assignment

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