The current research on text-guided 3D synthesis predominantly utilizes complex diffusion models, posing significant challenges in tasks like terrain generation. This study ventures into the direct synthesis of text-to-3D terrain in a zero-shot fashion, circumventing the need for diffusion models. By exploiting the large language model’s inherent spatial awareness, we innovatively formulate a method to update existing 3D models through text, thereby enhancing their accuracy. Specifically, we introduce a Gaussian–Voronoi map data structure that converts simplistic map summaries into detailed terrain heightmaps. Employing a chain-of-thought behavior tree approach, which combines action chains and thought trees, the model is guided to analyze a variety of textual inputs and extract relevant terrain data, effectively bridging the gap between textual descriptions and 3D models. Furthermore, we develop a text–terrain re-editing technique utilizing multiagent reasoning, allowing for the dynamic update of the terrain’s representational structure. Our experimental results indicate that this method proficiently interprets the spatial information embedded in the text and generates controllable 3D terrains with superior visual quality.

Smart homes are the most important sustainability technology of our future. In smart homes, intelligent monitoring is an important component. However, there is currently no effective method for human posture detection for monitoring in smart homes. So, in this paper, we provide an infrared human posture recognition method for monitoring in sustainable smart homes based on a Hidden Markov Model (HMM). We also trained the model parameters. Our model can be used to effectively classify human postures. Compared with the traditional HMM, this paper puts forward a method to solve the problem of human posture recognition. This paper tries to establish a model of training data according to the characteristics of human postures. Accordingly, this complex problem can be decomposed. Thereby, it can reduce computational complexity. In practical applications, it can improve system performance. Through experimentation in a real environment, the model can identify the different body movement postures by observing the human posture sequence, matching identification and classification process. The results show that the proposed method is feasible and effective for human posture recognition. In addition, for human movement target detection, this paper puts forward a human movement target detection method based on a Gaussian mixture model. For human object contour extraction, this paper puts forward a human object contour extraction method based on the Sobel edge detection operator. Here, we have presented an experiment for human posture recognition, and have also examined our cloud-based monitoring system for elderly people using our method. We have used our method in our actual projects, and the experimental results show that our method is feasible and effective.

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.