ABSTRACT City‐scale 3D urban generation requires planning‐level semantic grounding from user intent and scalable geometric synthesis with structural validity and editability. Procedural content generation (PCG) offers controllability and scalability, but is hard to author due to high‐dimensional parameters and nonintuitive workflows. Meanwhile, directly generating city geometry or scripts from text with LLMs can suffer from weak large‐scale consistency and limited geometric validity, hindering downstream editing and engine deployment. We present Text‐to‐3D City, a plan‐then‐execute framework that couples an LLM‐based City Planner with a PCG‐based Implementer. Given a natural language description, the Planner grounds textual intent into a structured city plan by composing PCG parameters via a schema and in‐context exemplars. The Implementer deterministically executes road generation, block extraction, lot subdivision, and asset placement with validity checks and reproducible seeding to synthesize an engine‐ready 3D city. Experiments on multi‐view renderings evaluate text‐scene alignment, diversity, realism, and runtime, demonstrating rapid generation and scalability to large urban scenes.

In contemporary rehabilitation treatments, it is common to integrate advanced technologies such as virtual reality and human-computer interaction. However, there is insufficient research on their efficacy in enhancing the fine motor skills of children with developmental coordination disorder (DCD). This paper proposes a virtual motion training system that employs gesture manipulation to improve the hand movement skills of young children. The system utilizes virtual reality devices to provide a more immersive and experiential training method compared to traditional physical rehabilitation. Finally, we recruited 60 volunteers for the study, and the results demonstrated that this training approach had promising effects on the hand motor coordination abilities of children with developmental coordination issues.

Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Abstract As an ancient performing art, shadow puppetry is a treasure of Chinese art. However, with the development of society, shadow puppetry becomes less well‐known among the young generation. To preserve and further spread this traditional culture, the digitization of shadow puppets is playing an increasingly important role in shadow puppetry conservation. Despite this, the spread of shadow puppetry culture in modern times still faces many hindrances. In digitalized shadow puppet art, the virtual scenes in certain degree determine the artistic effect of shadow puppet performance. The commonly used method for digital shadow puppet scene construction is via artificially created models which are then placed in corresponding positions. Obviously, this is a cumbersome and time‐consuming task. Therefore, a semantic‐based scene generation method for digital shadow puppet performance scene is proposed in this paper. According to this method, the key information is extracted from the descriptive text using the Chinese text segmentation technology. Meanwhile, we generate semantic scene graphs and search the corresponding shadow puppet models in the model library to construct the virtual scenes of digital shadow puppet performance. In the evaluation experiment, we invited 30 volunteers (10 female and 20 male) who had been exposed to traditional shadow puppet play in their daily lives. As suggested by the experimental results, the digital shadow puppet performance scene generated in this paper exhibit advantages of convenient use and high availability, which largely enhance the effect of digital shadow puppet performance. It should nonetheless be noted that it's not easy to extract the spatial relations from complicated texts, which inevitably limits the effectiveness of our scene generation method. The research work of this paper aims to promote digital shadow puppet technology and provide insights for the inheritance and conservation of traditional shadow puppet art.

The lack of social activities in the elderly for physical reasons can make them feel lonely and prone to depression. With the spread of COVID-19, it is difficult for the elderly to conduct the few social activities stably, causing the elderly to be more lonely. The metaverse is a virtual world that mirrors reality. It allows the elderly to get rid of the constraints of reality and perform social activities stably and continuously, providing new ideas for alleviating the loneliness of the elderly. Through the analysis of the needs of the elderly, a virtual social center framework for the elderly was proposed in this study. Besides, a prototype system was designed according to the framework. The elderly can socialize in virtual reality with metaverse-related technologies and human-computer interaction tools. Additionally, a test was jointly conducted with the chief physician of the geriatric rehabilitation department of a tertiary hospital. The results demonstrated that the mental state of the elderly who had used the virtual social center was significantly better than that of the elderly who had not used it. Thus, virtual social centers alleviated loneliness and depression in older adults. Virtual social centers can help the elderly relieve loneliness and depression when the global epidemic is normalizing and the population is aging. Hence, they have promotion value

Abstract The emergence of digital opera has enriched the stage performance of Chinese opera and expanded its dissemination means. However, the modern spread of traditional Chinese opera still faces hindrances. Digital opera performances require the generation of virtual scenes of the stages and characters. However, traditional virtual scene generation requires workers to build 3D models using modeling software and incorporate them into the performance scene. This article proposes a semantic‐based generation method for Chinese opera performance scenes. First, we analyze the scene description scripts to understand the elements in Chinese opera virtual scenes. The prior probability is subsequently used to learn the model placement rules in the opera scene model. A digital scene suitable for Chinese opera performance is then generated. The final results show that the method can generate natural and receptive opera digital performance scenes. This article's research ideas and achievements are conducive to the promotion of development of Chinese digital opera technology. They possess substantial significance to the inheritance and development of traditional Chinese opera art.