Baseline method1
方法： Check height of the foot
優點：easy
缺點： easily fooled，if a character skids to a stop
Baseline mehtod 2
方法： Check speed of the foot
優點：easy
缺點： unreliable the markers have some speed even during foot plants. marker data is noisy
Bindiganavale 98 Proceeding of the International Workshop on Modeling and Motion Capture Techniques for Virtual Environments
方法： detect zero-crossing in acceration space of the end effectors
優點： work well for non-noisy data
缺點： unreliable on motion capture data not reliabel when working with noisy signals require manualy tagged-objects to avoid checking for collison with all the objects in the scene
Liu and Popovic 2002 Siggraph
方法：detect frames in which the feet are stationary
優點：work well for non-noisy data
缺點： unreliable on motion capture data, not automatic .This method is dedicated to keyframed animation and is not intended to be applied to motion capture as it does not consider noise in the data.
Kovar 2002 Symposium on Computer Animation
題目：Footskate cleanup for motion capture editing
方法：use specific thresholds on the position and velocity of the feet to detect them.
優點：
缺點：not reliable for motion capture animation as derivatives tend to amplify nosie in signals
Lee 2002 Siggraph
題目：Interactive control of AVstars animated with human motion data
方法： consider body segments and objects in the environment relative velocity and position to decide whether a body segment is in contact with an object in the scene or not
優點：
缺點：not reliable for motion capture animation as derivatives tend to amplify nosie in signals
S.Menareais 2004 Symposium on Computer Animation
題目： Synchronization for Dynamic blending of motions
方法：use specific thresholds on the position and velocity of the feet to detect them
優點：
缺點 not reliable for motion capture animation as derivatives tend to amplify nosie in signals
Ikemoto 06 Symposium on Interactive 3D Graphics
方法：use a classifier to detect when foot plants should occur.By labeling a small set of frames, a user trains a classifier to detect when the foot should be planted.The classifier then automatically labels the remainder of the frames.
優點： semi-automatic(訓練部分需要手動參與),
缺點： This method is dedicated to footplants detection and would be difficult to generilized to any kind of effectors and /or constraints .Indeed ,detecting another type of constraints would require to build a new kind of teature vectors and to train the calssifier once more.
想法：這個方法沒看懂。。。說實話。。（一下午都在搞這個。。出了配了個Emacs。。。）
1） 首先怎么把三維mark點的軌跡映射到二維上，而且都是對齊的？ 從root點來搞？（貌似root點的確可以搞）
2） 下面就剩一些細節的東西。。21幀的問題。。。
貌似的確是SKELETON相關的。。所以不適合我們的問題。。。summer說的的確是不錯的。。
Le 06 Symposium on Computer Animation
題目：Robust kinematic constraint detection for motion data
方法：
優點：
缺點：
這個Roubust Kinematic 看得我真是頭大的很啊。。。SVD分解，線性代數。。。映射空間。。。高斯噪聲。。。噪聲模板。。。我勒個去。。先補基礎。。