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Item Neurosymbolic Models for Computer Graphics(The Eurographics Association and John Wiley & Sons Ltd., 2023) Ritchie, Daniel; Guerrero, Paul; Jones, R. Kenny; Mitra, Niloy J.; Schulz, Adriana; Willis, Karl D. D.; Wu, Jiajun; Bousseau, Adrien; Theobalt, ChristianProcedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design parameters, stochastic variations, high-quality outputs, compact representation, and more. But they also have some limitations, such as the difficulty of authoring a procedural model from scratch. More recently, AI-based methods, and especially neural networks, have become popular for creating graphic content. These techniques allow users to directly specify desired properties of the artifact they want to create (via examples, constraints, or objectives), while a search, optimization, or learning algorithm takes care of the details. However, this ease of use comes at a cost, as it's often hard to interpret or manipulate these representations. In this state-of-the-art report, we summarize research on neurosymbolic models in computer graphics: methods that combine the strengths of both AI and symbolic programs to represent, generate, and manipulate visual data. We survey recent work applying these techniques to represent 2D shapes, 3D shapes, and materials & textures. Along the way, we situate each prior work in a unified design space for neurosymbolic models, which helps reveal underexplored areas and opportunities for future research.Item Neural Geometry Processing via Spherical Neural Surfaces(The Eurographics Association and John Wiley & Sons Ltd., 2025) Williamson, Romy; Mitra, Niloy J.; Bousseau, Adrien; Day, AngelaNeural surfaces (e.g., neural map encoding, deep implicit, and neural radiance fields) have recently gained popularity because of their generic structure (e.g., multi-layer perceptron) and easy integration with modern learning-based setups. Traditionally, we have a rich toolbox of geometry processing algorithms designed for polygonal meshes to analyze and operate on surface geometry. Without an analogous toolbox, neural representations are typically discretized and converted into a mesh, before applying any geometry processing algorithm. This is unsatisfactory and, as we demonstrate, unnecessary. In this work, we propose a spherical neural surface representation for genus-0 surfaces and demonstrate how to compute core geometric operators directly on this representation. Namely, we estimate surface normals and first and second fundamental forms of the surface, as well as compute surface gradient, surface divergence and Laplace Beltrami operator on scalar/vector fields defined on the surface. Our representation is fully seamless, overcoming a key limitation of similar explicit representations such as Neural Surface Maps [MAKM21]. These operators, in turn, enable geometry processing directly on the neural representations without any unnecessary meshing. We demonstrate illustrative applications in (neural) spectral analysis, heat flow and mean curvature flow, and evaluate robustness to isometric shape variations. We propose theoretical formulations and validate their numerical estimates, against analytical estimates, mesh-based baselines, and neural alternatives, where available. By systematically linking neural surface representations with classical geometry processing algorithms, we believe this work can become a key ingredient in enabling neural geometry processing. Code is available via the project webpage.Item Controlled Image Variability via Diffusion Processes(The Eurographics Association, 2025) Zhu, Yueze; Mitra, Niloy J.; Ceylan, Duygu; Li, Tzu-MaoDiffusion models have shown remarkable abilities in generating realistic images. Unfortunately, diffusion processes do not directly produce diverse samples. Recent work has addressed this problem by applying a joint-particle time-evolving potential force that encourages varied and distinct generations. However, such a method focuses on improving the diversity across any batch of generation rather than producing variations of a specific sample. In this paper, we propose a method for creating subtle variations of a single (generated) image - specifically, we propose Single Sample Refinement, a simple and training-free method to improve the diversity of one specific sample at different levels of variability. This mode is useful for creative content generation, allowing users to explore controlled variations without sacrificing the identity of the main objects.Item FlairGPT: Repurposing LLMs for Interior Designs(The Eurographics Association and John Wiley & Sons Ltd., 2025) Littlefair, Gabrielle; Dutt, Niladri Shekhar; Mitra, Niloy J.; Bousseau, Adrien; Day, AngelaInterior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive style, but also ensure they are arranged in a way that maximizes accessibility, while adhering to a variety of affordability and usage considerations. Data-driven solutions have been proposed, but these are typically room- or domain-specific and lack explainability in their design design considerations used in producing the final layout. In this paper, we investigate if large language models (LLMs) can be directly utilized for interior design. While we find that LLMs are not yet capable of generating complete layouts, they can be effectively leveraged in a structured manner, inspired by the workflow of interior designers. By systematically probing LLMs, we can reliably generate a list of objects along with relevant constraints that guide their placement. We translate this information into a design layout graph, which is then solved using an off-the-shelf constrained optimization setup to generate the final layouts. We benchmark our algorithm in various design configurations against existing LLM-based methods and human designs, and evaluate the results using a variety of quantitative and qualitative metrics along with user studies. In summary, we demonstrate that LLMs, when used in a structured manner, can effectively generate diverse high-quality layouts, making them a viable solution for creating large-scale virtual scenes. Code is available via the project webpage.