[Bf-committers] Interested in GSoC project about motion capture data

Roger Wickes rogerwickes at yahoo.com
Sat Mar 20 17:11:11 CET 2010


Cool! Some thoughts for variations/features:
- what makes repeating human motion "humanly" is the variation from one step to another. so, like the gimp plugin that can fill in cutoouts, intelligence in varying the cycle would be great.
- what makes human motion "humanly" is the irregularity of the motion. 
Eliminating keys that can not be interpolated, and which result in smooth flow, makes it robotic. Pehaps a LOD (Level of detail) selector would be cool.

i address some of the library issues you speak of in a tut i wrote 
at http://wiki.blender.org/index.php/Doc:Tutorials/Animation/Advanced/MoCap


--Roger


Check out my website at www.rogerwickes.com for a good deal on my book and 
training course, as well as information about my latest activities. Use coupon
Papasmurf for $15 off!




________________________________
From: Yumei Wang <yumeiw86 at gmail.com>
To: bf-committers at blender.org
Sent: Sat, March 20, 2010 3:13:36 AM
Subject: [Bf-committers] Interested in GSoC project about motion capture data

Hi all,

I am interested in the idea "Improve blender for dealing with motion
capture data". I'm a graduate student doing research on motion
synthesis based on motion capture data. I think this project is quite
related to my research. I once do a small project of motion blending
using BVH motion data.

Following is my understanding about the features mentioned in the idea:

Curve simplification for mo-cap data: we can use a smaller set of key
frames to represent the original mo-cap data. Users can edit the
motion with these key frames. So the problem here is how to sample
these key frames to fit the original motion data.

Turn repetitive motion into loops: a simple example is generate
walking motion of any length with one walking cycle. The problem here
is to find the repetitive pattern of  motion from the mo-cap data.

Not sure if I get it correctly or not? Any suggestions or information
is welcome.

Thanks

--
Yumei Wang
School of Computing,
National University of Singapore.
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