Compared to traditional dance, intangible cultural heritage dance often involves the isotropic extension of choreographic actions, utilizing both upper and lower limbs. This characteristic choreography style makes the remote joints lack interaction, consequently reducing accuracy in existing human motion prediction methods. Therefore, we propose a human motion prediction method based on the multi-scale hypergraph convolutional network of the intangible cultural heritage dance video. Firstly, this method inputs the 3D human posture sequence from intangible cultural heritage dance videos. The hypergraph is designed according to the synergistic relationship of the human joints in the intangible cultural heritage dance video, which is used to represent the spatial correlation of the 3D human posture. Then, a multi-scale hypergraph convolutional network is constructed, utilizing multi-scale transformation operators to segment the human skeleton into different scales. This network adopts a graph structure to represent the 3D human posture at different scales, which is then used by the single-scalar fusion operator to spatial features in the 3D human posture sequence are extracted by fusing the feature information of the hypergraph and the multi-scale graph. Finally, the Temporal Graph Transformer network is introduced to capture the temporal dependence among adjacent frames within the time domain. This facilitates the extraction of temporal features from the 3D human posture sequence, ultimately enabling the prediction of future 3D human posture sequences. Experiments show that we achieve the best performance in both short-term and long-term human motion prediction when compared to Motion-Mixer and Motion-Attention algorithms on Human3.6M and 3DPW datasets. In addition, ablation experiments show that our method can predict more precise 3D human pose sequences, even in the presence of isotropic extensions of upper and lower limbs in intangible cultural heritage dance videos. This approach effectively addresses the issue of missing segments in intangible cultural heritage dance videos.

Recently, Asymmetric pixel–shuffle downsampling and Blind–Spot Network (AP-BSN) has made some progress in unsupervised image denoising. However, the method tends to damage the texture and edge information of the image when using pixel-shuffle downsampling (PD) to destroy pixel-related large-scale noise. To tackle this issue, we suggest a denoising method for mural images based on Cross Attention and Blind–Spot Network (CA-BSN). First, the input image is downsampled using PD, and after passing through a masked convolution module (MCM), the features are extracted respectively; then, a cross attention network (CAN) is constructed to fuse the extracted feature; finally, a feed-forward network (FFN) is introduced to strengthen the correlation between the feature, and the denoised processed image is output. The experimental results indicate that our proposed CA-BSN algorithm achieves a PSNR growth of 0.95 dB and 0.15 dB on the SIDD and DND datasets, respectively, compared to the AP-BSN algorithm. Furthermore, our method demonstrates a SSIM growth of 0.7% and 0.2% on the SIDD and DND datasets, respectively. The experiments show that our algorithm preserves the texture and edge details of the mural images better than AP-BSN, while also ensuring the denoising effect.