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Existing Labanotation generation methods have some drawbacks due to low efficiency and incapability to recognize existing videos, which can also be affected by the quality of hardware equipment. To address the issues in existing methods, we propose a new Labanotation generation method for folk dance videos based on pose estimation. Specifically, our method first extracts the key frame images from the fork dance video using temporal differences. Afterward, the 2D joint points of a dancer can be detected from key frame images by using multi-scale fusion of high-resolution net (HRNet), then maps the 2D–3D joint point sequence of the dancer using a pose projection generative adversarial network (pose projection GAN) to predict the coordinates of the 3D joint point position. Finally, the corresponding Labanotation can be generated by analyzing the estimate posture. Experimental results show that the method can achieve the conversion of dance movements in folk dance videos into digital Labanotation, and the automatic generation is much more efficient than manual recording. This method can quickly record endangered folk dances and contribute to the preservation and transmission of movement-based intangible cultural heritage. [ABSTRACT FROM AUTHOR]

Music-driven automatic dance movement generation has become a hot research topic in the field of computer vision and internet of things in the recent past. To address the problems of increasing loss of Chinese folk dance culture, high cost of manual choreography methods and requirements for professional background, this paper proposes an automatic generation method for folk dance movements. Firstly, the proposed method collects paired folk music and dance videos to construct a synchronized folk music–dance dataset, extracting music and dance features using a feature extraction tool and a multi-scale fusion high-resolution network, respectively. Afterward, a sequence-to-sequence network model is constructed and then trained based on music features and dance features to synthesize rhythmically matched dance sequences for new music clips. Finally, an easy-to-use and effective automatic folk dance choreography method is implemented. Experimental data show that the proposed method performs well in automatic folk dance generation and the generated dances have folk characteristics and match the rhythm of the given music. [ABSTRACT FROM AUTHOR]

To address the issue that point cloud data is often incomplete and difficult to obtain, we propose a point cloud completion method to improve the PoinTr method based on feature enhancement. In dataset preprocessing, the farthest point of the original point cloud is sampled to obtain the central point coordinates. Our method constructs an MLP network, where the local information of these central points is obtained and the location embedding is performed. Combining network and SENet network, the local features of the point cloud are extracted and enhanced, and the location embedding and local features are added to obtain the point proxies of the original point cloud. Afterward, our method predicts the missing part of the point cloud by using an Encoder to model the relationship between the point cloud structure information and points, and then using a Decoder to learn the relationship between the missing and existing parts of the point cloud and reconstruct the missing point cloud. Our method also modifies the attention mechanism to make the features more global and enhance the network expression. Finally, the point cloud is refined, and is realized by predicting multiple points around each point of the coarse point cloud through the FoldingNet network, and the final output is the complete point cloud. Experimental results show that the proposed method can not only reduce the performance overhead, but also improve the effects of point cloud completion. [ABSTRACT FROM AUTHOR]

Due to the complexity and diversity of indoor environment objects and interference occlusions, the accuracy of multi-object target detection based on 3D point cloud is limited. To address this issue, we present a multi-target detection method based on adaptive feature adjustment (AFA) of 3D point cloud. First, our method preprocesses the dataset and constructs a backbone module. Afterwards, our method uses an improved PointNet + + network for feature adaptive learning, where an AFA module is added to learn the influence relationship between point pairs. The proposed method then establishes the relationship between contexts in the local point set area and extracts the feature of point cloud. Using the idea of Hough voting, our method can generate some votes close to the particle. Using these votes to generate proposal, the proposed method adds CBAM attention mechanisms to both modules of voting and proposal, which can fuse the feature information of the channel and expand the receptive field in space. Our method can enhance the important features and weaken the unimportant features, making the extracted features more directional and enhancing the expressiveness of the network. Finally, the generated results are visualized to complete the multi-target detection of 3D point cloud. To verify the effectiveness of our proposed method, two large datasets with real 3D scanning, scanNet2 and SunRGB-D, are used for training the network. The experimental results show that the proposed method can improve the effectiveness of point cloud target detection in indoor scenes, getting a higher detection accuracy. [ABSTRACT FROM AUTHOR]