Segmentation and Completion of Human Motion Sequence via Temporal Learning of Subspace Variety Model.

Subspace-based models have been extensively employed in unsupervised segmentation and completion of human motion sequence (HMS). However, existing approaches often neglect the incorporation of temporal priors embedded in HMS, resulting in suboptimal results. This paper presents a subspace variety model for HMS, along with an innovative Temporal Learning of Subspace Variety Model (TL-SVM) method for enhanced segmentation and completion in HMS. The key idea is to segment incomplete HMS into motion clusters and extracting the subspace features of each motion through the temporal learning of the subspace variety model. Subsequently, the HMS is completed based on the extracted subspace features. Thus, the main challenge is to learn the subspace variety model with temporal priors when confronted with missing entries. To tackle this, the paper develops a spatio-temporal assignment consistency (STAC) constraint for the subspace variety model, leveraging temporal priors embedded in HMS. In addition, a subspace clustering approach under the STAC constraint is proposed to learn the subspace variety model by extracting subspace features from HMS and segmenting HMS into motion clusters alternatively. The proposed subspace clustering model can also handle missing entries with theoretical guarantees. Furthermore, the missing entries of HMS are completed by minimizing the distance between each human motion frame and its corresponding subspace. Extensive experimental results, along with comparisons to state-of-the-art methods on four benchmark datasets, underscore the advantages of the proposed method.