Motion Style Extraction Based on Sparse Coding Decomposition
This work addresses motion synthesis for animation or robotics, but it appears incremental as it builds on existing sparse coding methods with specific optimizations.
The authors tackled the problem of motion style decomposition and synthesis by proposing a sparse coding-based framework that includes preprocessing with Dynamic Time Warping and post-processing with limb length constraints, resulting in smooth and natural synthesized motions with advantages like reduced time consumption and no manual alignment.
We present a sparse coding-based framework for motion style decomposition and synthesis. Dynamic Time Warping is firstly used to synchronized input motions in the time domain as a pre-processing step. A sparse coding-based decomposition has been proposed, we also introduce the idea of core component and basic motion. Decomposed motions are then combined, transfer to synthesize new motions. Lastly, we develop limb length constraint as a post-processing step to remove distortion skeletons. Our framework has the advantage of less time-consuming, no manual alignment and large dataset requirement. As a result, our experiments show smooth and natural synthesized motion.