Urban green spaces such as parks and gardens are indispensable in both virtual and real-world environments. Therefore, planning such spaces is highly valuable. While scene synthesis literature has limited interest in this topic, many existing parametric design and procedural content generation approaches can be adapted to generate urban green spaces. However, these approaches heavily rely on manual work or are prone to producing monotonously repeated objects. This paper presents a framework that can automatically plan urban green spaces. Tailored to urban green space design, our framework comprises three steps: road system generation, region type planning, and model placement. First, it constructs undirected graphs to generate a sound road system for an empty site and divides the space into separate regions. Then it applies a genetic algorithm to plan suitable surface and vegetation for every region. Finally, it places landscape models based on various patterns and adds embellishments to complete an appealing urban green space. Our framework enables the automatic production of urban green spaces. Through extensive experiments, we demonstrate that the generated results are plausible and reasonable.

Retail space planning, arranging store sections and product placements to optimize customer flow and stimulate purchases helps retailers to increase sales and enhances the customer shopping experience. It can be challenging for retailers to arrange numerous products within limited shelf space. This paper introduces StoreSketcher, an interactive tool that assists retailers in planning retail layouts efficiently at macro and micro levels by providing intelligent suggestions. We have extracted commercial relationships between products and categories, built spatial rules for commercial objects, and developed an interactive framework for synthesizing retail layouts. When the user points to shelf space in the layout, StoreSketcher evaluates the spatial significance of the location and its commercial relation to the surrounding context to present appropriate suggestions. Quantitative experiments demonstrate that StoreSketcher significantly assists in planning well-organized retail layouts. The suggestions provided by StoreSketcher not only boost cross-selling and impulse purchasing for retailers, but also enhance product findability for customers.

Inspired by the rapid progress of generative AI techniques, there have been huge advances made for the 3D (three-dimensional) reconstruction community, which promoted the traditional 3D reconstruction framework from deep implicit 3D reconstruction to generative 3D reconstruction, achieving more robust and expansive 3D reconstruction results with the help of generative AI models. Meanwhile, there is still a lack of corresponding review articles to provide a comprehensive analysis of recent advances from the perspective of 3D reconstruction. In response, this paper gives a comprehensive review for the generative 3D reconstruction approaches, especially on the recent advances made from the computer graphics and vision communities. Firstly, this paper mainly divides the recent generative 3D reconstruction approaches into four categories, including generative structure-from-motion/multiview-sterero (SfM/MVS), generative adversarial networks (GAN) based 3D reconstruction, diffusion-based 3D reconstruction, and cross-modal 3D reconstruction, which cover most generative-model aided 3D reconstruction work with a comprehensive review and analysis. Thereafter, some representative applications inspired by the generative 3D reconstruction including dynamic human avatars, 3D interactive editing, and autonomous driving are also reviewed. Besides, some major datasets widely used for the generative 3D reconstruction approaches are included. Finally, this paper makes a discussion of the potential future work in further improving the quality of generative 3D reconstruction, towards better and more intelligent 3D reconstruction and generation.