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.

Scene synthesis is crucial to computer graphics. However, the current interactive scene synthesis methods usually cost the user too much time and interactions to edit objects. This paper presents a new interactive scene synthesis method that alleviates the designer from interacting with the 3D scene and its objects. Designers only need to select an object group in an independent panel through coarse clustering and fine clustering. Then, the furniture objects will be automatically added to the scene. This paper proposes a two-level clustering that applies the Affinity Propagation Algorithm (APA) to groups of furniture objects such that the object groups can even be clustered without linear representations, latent encoding, etc. To fully apply the APA, we also propose quantitatively measuring how different the two layouts are, i.e., how quantitatively the arrangements of two object groups differ. Experiments first show that our method is more user-friendly and interactively efficient than other interactive synthesis methods. By comparing our method with recent automatic scene synthesis methods, we demonstrate that our methods still have competitive plausibility. We also verify that our method does not harm the diversity and generalization of 3D scenes.

Diffusion model has been used in indoor scene synthesis and has made significant progress. Current works encode an indoor scene as a top-down view of the room, a list of objects, and their world co-ordinates and orientation. In this paper, we develop a diffusion-based training and synthetic method which incorporates indoor scene ''characteristics''. Firstly, we calculate the relative transformations among objects to capture the local characteristics of the scene. We send this relative transformation into the self-attention layer of the denoising network as ''relative positional encoding''. Secondly, we use room guidance to guide the objects to fit the room's geometry. This improvement uses the room's characteristics to solve the physical collision problem occurring in former diffusion-based works, while preserving plausibilities. Experiments show that our improvements improve the scene variety and quality.

Recently, indoor scene synthesis has gathered significant attention, leading to the development of numerous indoor datasets. However, existing datasets only address static furniture and scenes, ignoring the need for dynamic interior design scenarios that emphasize flexible functionalities. Addressing this gap, we present DIFF (Dataset for Indoor Flexible Furniture), featuring expertly crafted and labeled furniture modules capable of inter-transforming between different states, e.g., a cabinet can be inter-transformed to a desk. Each module exhibits flexibility in shifting to multiple shapes and functionalities. Additionally, we propose a method that adapts our dataset to generate flexible layouts. By matching our flexible objects to objects from existing datasets, we use a graph-based approach to migrate the spatial relation priors for optimizing a layout; subsequent layouts are then generated by minimizing a transition-cost function. Analyses and user studies validate the quality of our modules and demonstrate the plausibility of the proposed method.

Nowadays, 3D scenes are not merely static arrangements of objects. With the development of transformable modules, furniture objects can be translated, rotated, and even reshaped to achieve scenes with different functions (e.g., from a bedroom to a living room). Transformable domestic space, therefore, studies how a layout can change its function by reshaping and rearranging transformable modules, resulting in various transformable layouts. In practice, a rearrangement is dynamically conducted by reshaping/translating/rotating furniture objects with proper schedules, which can consume more time for designers than static scene design. Due to changes in objects' functions, potential transformable layouts may also be extensive, making it hard to explore desired layouts. We present a system for exploring transformable layouts. Given a single input scene consisting of transformable modules, our system first attempts to derive more layouts by reshaping and rearranging the modules. The derived scenes are organized into a graph-like hierarchy according to their functions, where edges represent functional evolutions (e.g., a living room can be reshaped to a bedroom), and nodes represent layouts that are dynamically transformable through translating/rotating/reshaping modules. The resulting hierarchy lets scene designers interactively explore possible scene variants and preview the animated rearrangement process. Experiments show that our system is efficient for generating transformable layouts, sensible for organizing functional hierarchies, and inspiring for providing ideas during interactions.

Scene synthesis has gained significant attention recently, and interactive scene synthesis focuses on yielding scenes according to user preferences. Existing literature either generates floor plans or scenes according to the floor plans. The system proposed in this paper generates scenes over floor plans in real-time. Given an initial scene, the only interaction a user needs is changing the room shapes. Our framework splits/merges rooms and adds/rearranges/removes objects for each transient moment during interactions. A systematic pipeline achieves our framework by compressing objects' arrangements over modified room shapes in a transient moment, thus enabling real-time performances. We also propose elastic boxes that indicate how objects should be arranged according to their continuously changed contexts, such as room shapes and other objects. Through a few interactions, a floor plan filled with object layouts is generated concerning user preferences on floor plans and object layouts according to floor plans. Experiments show that our framework is efficient at user interactions and plausible for synthesizing 3D scenes.

Intelligent tools for creating synthetic scenes have been developed significantly in recent years. Existing techniques on interactive scene synthesis only incorporate a single object at every interaction, i.e., crafting a scene through a sequence of single-object insertions with user preferences. These techniques suggest objects by considering existent objects in the scene instead of fully picturing the eventual result, which is inherently problematic since the sets of objects to be inserted are seldom fixed during interactive processes. In this article, we introduce SceneDirector, a novel interactive scene synthesis tool to help users quickly picture various potential synthesis results by simultaneously editing groups of objects. Specifically, groups of objects are rearranged in real-time with respect to a position of an object specified by a mouse cursor or gesture, i.e., a movement of a single object would trigger the rearrangement of the existing object group, the insertions of potentially appropriate objects, and the removal of redundant objects. To achieve this, we first propose an idea of coherent group set which expresses various concepts of layout strategies. Subsequently, we present layout attributes, where users can adjust how objects are arranged by tuning the weights of the attributes. Thus, our method gives users intuitive control of both how to arrange groups of objects and where to place them. Through extensive experiments and two applications, we demonstrate the potentiality of our framework and how it enables concurrently effective and efficient interactions of editing groups of objects.

Commercial scenes such as markets and shops are everyday scenes for both virtual scenes and real-world interior designs. However, existing literature on interior scene synthesis mainly focuses on formulating and optimizing residential scenes such as bedrooms, living rooms, etc. Existing literature typically presents a set of relations among objects. It recognizes each furniture object as the smallest unit while optimizing a residential room. However, object relations become less critical in commercial scenes since shelves are often placed next to each other so pre-calculated relations of objects are less needed. Instead, interior designers resort to evaluating how groups of objects perform in commercial scenes, i.e., the smallest unit to be evaluated is a group of objects. This paper presents a system automatically synthesizes market-like commercial scenes in virtual environments. Following the rules of commercial layout design, we parameterize groups of objects as "patterns" contributing to a scene. Each pattern directly yields a human-centric routine locally, provides potential connectivity with other routines, and derives the arrangements of objects concerning itself according to the assigned parameters. In order to optimize a scene, the patterns are iteratively multiplexed to insert new routines or modify existing ones under a set of constraints derived from commercial layout designs. Through extensive experiments, we demonstrate the ability of our framework to generate plausible and practical commercial scenes.

Selecting views is one of the most common but overlooked procedures in topics related to 3D scenes. Typically, existing applications and researchers manually select views through a trial-and-error process or "preset" a direction, such as the top-down views. For example, literature for scene synthesis requires views for visualizing scenes. Research on panorama and VR also require initial placements for cameras, etc. This article presents SceneViewer, an integrated system for automatic view selections. Our system is achieved by applying rules of interior photography, which guides potential views and seeks better views. Through experiments and applications, we show the potentiality and novelty of the proposed method.

We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors. Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our method measures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based on samples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration and plausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD) based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations among objects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works.

While recent researches on computational 3D scene synthesis have achieved impressive results, automatically synthesized scenes do not guarantee satisfaction of end users. On the other hand, manual scene modelling can always ensure high quality, but requires a cumbersome trial-and-error process. In this paper, we bridge the above gap by presenting a data-driven 3D scene synthesis framework that can intelligently infer objects to the scene by incorporating and simulating user preferences with minimum input. While the cursor is moved and clicked in the scene, our framework automatically selects and transforms suitable objects into scenes in real time. This is based on priors learnt from the dataset for placing different types of objects, and updated according to the current scene context. Through extensive experiments we demonstrate that our framework outperforms the state-of-the-art on result aesthetics, and enables effective and efficient user interactions.