Sparse motion bases selection for human motion denoising
A fine-grained pose representation model is proposed to boost the performance. We present a data-driven based motion denoising method by solving ?1-minimization problems. The proposed model selects the most correlated motion bases for motion reconstruction. Our method does not need to choose the training data and can be implemented in parallel mode.
human motion denoising data-driven ?1-minimization poselet model