## 6 Conclusions  
This paper proposes a novel method for assisting architects in the conceptual design stage by intelligently generating high-rise building concepts from textual descriptions. The model comprises a design concept extraction (DCE) module and an architectural appearance generation (AAG) module. The DCE module adopts contrastive learning to train a contrastive text-image matching model (CTIMM) to obtain a pre-trained text encoder that can accurately extract design concepts. Furthermore, the AAG module, which consists of a deep sparse and text fusion generative adversarial network (DSTF-GAN) and a pre-trained rendering (PR) module, transforms the extracted semantics into high-rise building sketches and renders them as realistic high-rise appearances. The effectiveness of the proposed method is validated through qualitative and quantitative experiments, as well as the comparison with existing buildings. Furthermore, the feasibility of applying the method to assist architects in the conceptual design phase of a building is demonstrated through 3D modeling of a generated high-rise rendering. The proposed method demonstrates the potential of leveraging deep learning techniques to facilitate the generation of high-rise building concepts from textual descriptions, offering valuable support to architects during the initial design process. Limitations. The dataset used in this study consists of only 856 real-world high-rise sketches paired with corresponding textual descriptions. Expanding the dataset could incorporate a wider range of architectural styles and features, leading to more diverse and robust high-rise sketch generation. Furthermore, the rendering process using the pre-trained diffusion model introduces some uncertainty, which may result in variations in architectural style and features, causing a few discrepancies between the generated renderings and the original textual descriptions. Although the visual plausibility of the generated sketches has been validated through 3D modeling and comparisons with existing buildings, incorporating qualitative or quantitative evaluations from experienced architects could further enhance the validation of the method's effectiveness. Future work. In future work, we will collaborate with professional architects to enhance the dataset and evaluate the generated designs, employing methods similar to focus groups [39]. By incorporating additional architectural elements and inspirations, we aim to improve the model's ability to generate high-rise buildings with diverse styles, cultural features, and structural variations while also improving its practical applicability. Addition  
Figure 16: Three-dimensional conceptualization of a rendered high-rise building appearance in an office building type. ally, we will refine the diffusion model's rendering process for more controlled outputs and enable interactive editing. We also plan to investigate the generation of 3D architectural massing by decomposing the overall volume into sub-blocks and representing it through a graph-based representation. During the generation process, spatial and functional constraints will be incorporated to ensure design feasibility. Additionally, by integrating the generated 2D high-rise renderings, we aim to achieve a cohesive design that synthesizes both massing and form, contributing to advancements in 3D architectural design. Conceptualization, W.Q. and F.Y. ; methodology, W.Q. and F.Y. ; validation, F.Y. ; formal analysis, W.Q. and F.Y. ; investigation, W.Q. ; resources, W.Q. ; data curation, F.Y. ; writing--original draft preparation, F.Y. ; writing--review and editing, W.Q. ; visualization, F.Y. ; supervision, W.Q. ; project administration, W.Q. ; funding acquisition, W.Q. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Key Research and Development Program of China (Grant No. 2023 YFC3805700), National Natural Science Foundation of China (Grant No. 52408171), and China Postdoctoral Science Foundation (Grant No. 2024M764196).