Intangible Cultural Heritage (ICH) dance refers to traditional dance forms and practices that are recognized as part of a community’s ICH, which is a living heritage. However, due to the decrease in the number of dance forms and inheritors, the urgency to protect this particular dance form has become imminent. The formations in dance play a crucial role in conveying the meaning of the dance. In this paper, we research 10 ICH dances in Yunnan Province, summarize their characteristics, and propose a dance formation choreography system by voice, FormationCreator. The dance formations are expressed parametrically through voice in our FormationCreator, allowing for a more flexible definition and reproduction of these formations. Our research seeks to offer initial insights and fresh design concepts for the field of virtual choreography.

In the field of computer vision, segmenting a 3D object into its component parts is crucial to understanding its structure and characteristics. Much work has focused on 3D object part segmentation directly from point clouds, and significant progress has been made in this area. This paper proposes a novel 3D object part segmentation method that focuses on integrating three key modules: a keypoint-aware module, a feature extension module, and an attention-aware module. Our approach starts by detecting keypoints, which provide the global feature of the inner shape that serves as the basis for segmentation. Subsequently, we utilize the feature extension module to expand the dimensions, obtain the local representation of the obtained features, provide richer object representation, and improve segmentation accuracy. Furthermore, we introduce an attention-aware module that effectively combines the features of the global and local parts of objects to enhance the segmentation process. To validate the proposed model, we also conduct experiments on the point cloud classification task. The experimental results demonstrate the effectiveness of our method, thus outperforming several state-of-the-art methods in 3D object part segmentation and classification.

Point cloud completion aims to recover partial geometric and topological shapes caused by equipment defects or limited viewpoints. Current methods either solely rely on the 3D coordinates of the point cloud to complete it or incorporate additional images with well-calibrated intrinsic parameters to guide the geometric estimation of the missing parts. Although these methods have achieved excellent performance by directly predicting the location of complete points, the extracted features lack fine-grained information regarding the location of the missing area. To address this issue, we propose a rapid and efficient method to expand an unimodal framework into a multimodal framework. This approach incorporates a position-aware module designed to enhance the spatial information of the missing parts through a weighted map learning mechanism. In addition, we establish a Point-Text-Image triplet corpus PCI-TI and MVP-TI based on the existing unimodal point cloud completion dataset and use the pre-trained vision-language model CLIP to provide richer detail information for 3D shapes, thereby enhancing performance. Extensive quantitative and qualitative experiments demonstrate that our method outperforms state-of-the-art point cloud completion methods.