Example‐based motion cloning

Abstract

In this paper, we pose a motion cloning problem of retargeting the motion of a source character to a target character with a different structure. Based on scattered data interpolation, an example-based approach to motion cloning is proposed. Provided with a set of example motions, our method automatically extracts a small number of representative postures called source key-postures. The animator then creates the corresponding keypostures of the target character, breathing his/her imagination and creativity into the output animation. Exploiting this correspondence, each input posture is cloned frame by frame to the target character to produce an initial animation, which is further adjusted in space and time for retargeting and timewarping and then finalized with some interactive fine tuning. With rich animation data available, our motion cloning method aims at rapid prototyping of an animation to verify an animator's concept at an early stage.

Keyframe Extraction from Human Motion Capture Data Based on a Multiple Population Genetic Algorithm

“…For example, Liu et al [1] divided N-frames of motion data into K sets according to the features of the motion data, before selecting the first frame in each set as the keyframe. Park and Shin [2] employed a quaternion to represent the motion sequences, before applying PCA (Principal Component Analysis) and the K-means method to process the motion data, and the scattered data were used for interpolation to extract the keyframe. Zhu and Wang [3] proposed a similar clustering method for extracting keyframes, where the high-dimensional original data were mapped to a low-dimensional space and the mean squared error was then used to segment the low-dimensional data.…”

Animation Key-Frame Extraction and Simplification Using Deformation Analysis

“…The key-frame extraction is converted into a curve simplification problem. In [10], the key-frame extraction becomes a clustering problem that attempts to group frames with similar posture. Once the key-frames are extracted, the remaining animation frames can be reconstructed by a linear combination of the extracted key-frames.…”

“…This matrix is then approximately factorized into a weight matrix and a key-frame matrix. For example, in [8]- [10], the optimal solution to the key-frame search is obtained under similar constraints or objective functions. However, these approaches are very time-consuming and not efficient for practical applications.…”

“…We introduce the deformation-driven GA to efficiently search good representative animation key-frames. Once the key-frames are extracted, similar to [8] and [10], the animation is reconstructed by a linear combination of the extracted key-frames for better approximation.…”

“…In this situation, the reconstructed animation will have the maximum error. Under this assumption, the normalization process is calculated using (10) where the represents the worst case error and the NonKeyframeError represents the candidate solution error (i.e., extracted key-frames).…”