Embedded Key-Frame Extraction for CG Animation by Frame Decimation

This paper proposes a method for key-frame selection of captured motion data. In many cases, it is desirable to obtain a compact representation of the human motion. Key-framing is often used to express CG animation with a set of frames. In general, the animation is described by a set of curves that give the value of the rotation of all joints in each frame. Our method automatically detects the key-frames in captured motion data by using frame decimation. We decimate less important frames one by one, and then rank them by their importance. Our method has an embedded property, that is, all the frames are ranked by their importance, and thus users can specify any number of key-frames from one data set. We demonstrate the validity of our method in the experimental section by several typical motions such as walking and throwing.