KEYFRAME REDUCTION TECHNIQUES FOR MOTION CAPTURE DATA * Onur ¨ Onder 1 , U˘ gur G¨ ud¨ ukbay 1 , B¨ ulent ¨ Ozg¨ uc ¸ 1 , Tanju Erdem 2 ,C ¸i˘ gdem Erdem 2 and Mehmet ¨ Ozkan 2 1 Bilkent University, Department of Computer Engineering, Bilkent, Ankara, Turkey 2 Momentum DMT, TUBITAK MAM TEKSEB, Gebze, Kocaeli, Turkey ABSTRACT Two methods for keyframe reduction of motion capture data are presented. Keyframe reduction of motion capture data en- ables animators to easily edit motion data with smaller num- ber of keyframes. One of the approaches achieves keyframe reduction and noise removal simultaneously by fitting a curve to the motion information using dynamic programming. The other approach uses curve simplification algorithms on the motion capture data until a predefined threshold of number of keyframes is reached. Although the error rate varies with different motions, the results show that curve fitting with dy- namic programming performs as good as curve simplification methods. Index Terms— Motion capture, keyframe reduction, curve fitting, curve simplification, noise filtering. 1. INTRODUCTION Motion capture systems enable the animators to create real- istic animations. These systems replicate the real world in a virtual environment. When a motion capture data is gathered, animators apply it on a virtual character. This step may pro- duce two common problems, the action should be controlled by the animator, and the motion information should not con- tain any noise. This paper presents approaches to solve these problems by using two different techniques: curve fitting with dynamic programming and curve simplification. Motion capturing is a costly process where the hardware and setting requirements play an important role. Thus there are motion capture data libraries where some common mo- tions (such as running, jumping, walking, etc.) are available to the animators. Animators can use those predefined data for custom animations. If the animator needs to change any part of the motion, he/she would need to modify joint information on every frame. This process takes a lot of time since many joint information has to be modified for every frame of the animation. If a small number of keyframes could be used to represent the motion capture data, then the animator would have an easier control over the motion. * This work is supported by EC within FP6 under Grant 511568 with the acronym 3DTV. Keyframes can be selected to represent the motion capture data by curve fitting, where keyframes are located and mod- ified as necessary using a dynamic programming approach. Keyframes can also be selected by curve simplification, where they are found by simplifying the motion curves. The rest of this paper is organized as follows: Section 2 gives background information and previous works, Section 3 describes two different approaches for the given problems, Section 4 states the results of the approaches and compares them, and Section 5 discusses the conclusions. 2. BACKGROUND There are some approaches for finding keyframes of the mo- tion capture data. In [1], Terra and Metroyer proposed a so- lution to find the timings of the keyframes. They have used a performance based approach. Huang et al. propose an it- erative approach, called key-probing, for keyframe extraction [2]. Also in [3], Park defined a method to extract key-postures from a motion. Curve fitting algorithms are available in various topics. Using these algorithms, animation control is covered in [4, 5]. There is no generic research that uses dynamic programming to optimize the curve fitting process. Curve simplification algorithms are generally used in mo- tion summarization. The main algorithm is defined by Lowe [6] that is used in many other approaches [7, 8]. A curve fitting with dynamic programming approach is initially defined in [9]. This paper extends this study, presents its results and compares it with a curve simplification algo- rithm. 3. CONTRIBUTIONS This section defines two different approaches to the keyframe reduction problem for motion capture data: curve fitting with dynamic programming and curve simplification. 3.1. Curve fitting with dynamic programming In this approach, a Hermite curve is fit onto the motion graph of the joints in the motion capture data. Initially, some key- frames are predicted on the motion data. Then these keyframes 978-1-4244-1755-1/08/$25.00 ©2008 IEEE 3DTV-CON'08, May 28-30, 2008, Istanbul, Turkey 293