arXiv subjects

Rosalie Martin

Publications and source records attributed to Rosalie Martin.

6 recordsLinked to original sources

arXiv2026

HeatMat: Simulation of City Material Impact on Urban Heat Island Effect

The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult to study in real cities using sensor data (satellites or in-situ stations) due to their coarse spatial and temporal resolution. Among the factors contributing to this effect are the properties of urban materials, which differ from those in rural areas. To analyze their individual impact and to test new material configurations, a high-resolution simulation at the city scale is required. Estimating the current materials used in a city, including those on building facades, is also challenging. We propose HeatMat, an approach to analyze at high resolution the individual impact of urban materials on the UHI effect in a real city, relying only on open data. We estimate building materials using street-view images and a pre-trained vision-language model (VLM) to supplement existing OpenStreetMap data, which describes the 2D geometry and features of buildings. We further encode this information into a set of 2D maps that represent the city's vertical structure and material characteristics. These maps serve as inputs for our 2.5D simulator, which models coupled heat transfers and enables random-access surface temperature estimation at multiple resolutions, reaching an x20 speedup compared to an equivalent simulation in 3D.

cs.GR↗

arXiv2025

{\Phi}eat: Physically Grounded Material Feature Representation

While foundation models have emerged as general-purpose visual backbones, their representations are primarily optimized for semantics and lack explicit modeling of physical factors, such as reflectance, hindering their efficacy in tasks requiring explicit material reasoning. We introduce $\Phi$eat$, a novel material-grounded visual backbone that encourages a representation sensitive to material identity, including reflectance and mesostructure. Instead of relying on generic data augmentations, we pretrain our model by contrasting observations of the same material under controlled variations in lighting and geometry. This encourages invariance to extrinsic factors while preserving sensitivity to intrinsic material properties. We show that the resulting representation provides strong priors for material-centric tasks, including feature-based material selection and classification. Our results demonstrate that physically inspired weak supervision is an effective strategy for learning representations tailored to material perception.

cs.CV↗

arXiv2025

Unlocking Thickness Modeling for Codimensional Contact Simulation

In this work we analyze and address a fundamental restriction that blocks the reliable application of codimensional yarn-level and shell models with thickness, to simulate real-world woven and knit fabrics. As discretizations refine toward practical and accurate physical modeling, such models can generate non-physical contact forces with stencil-neighboring elements in the simulation mesh, leading to severe locking artifacts. While not well-documented in the literature, this restriction has so far been addressed with two alternatives with undesirable tradeoffs. One option is to restrict the mesh to coarse resolutions, however, this eliminates the possibility of accurate (and consistent) resolution simulations across real-world material variations. A second alternative instead seeks to cull contact pairs that can create such locking forces in the first place. This relaxes resolution restrictions but compromise robustness. Culling can and will generate unacceptable and unpredictable geometric intersections and tunneling that destroys weaving and knitting structures and cause unrecoverable pull-throughs. We address these challenges to simulating real-world materials with a new and practical contact-processing model for thickened codimensional simulation, that removes resolution restrictions, while guaranteeing contact-locking-free, non-intersecting simulations. We demonstrate the application of our model across a wide range of previously unavailable simulation scenarios, with real-world material yarn and fabric parameters and patterns, challenging simulation conditions and mesh resolutions, and both rod and shell models, integrated with the IPC barrier.

cs.GR↗

arXiv2025

MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data

Terrain modeling has traditionally relied on procedural techniques, which often require extensive domain expertise and handcrafted rules. In this paper, we present MESA - a novel data-centric alternative by training a diffusion model on global remote sensing data. This approach leverages large-scale geospatial information to generate high-quality terrain samples from text descriptions, showcasing a flexible and scalable solution for terrain generation. The model's capabilities are demonstrated through extensive experiments, highlighting its ability to generate realistic and diverse terrain landscapes. The dataset produced to support this work, the Major TOM Core-DEM extension dataset, is released openly as a comprehensive resource for global terrain data. The results suggest that data-driven models, trained on remote sensing data, can provide a powerful tool for realistic terrain modeling and generation.

cs.GR↗

arXiv2024

ControlMat: A Controlled Generative Approach to Material Capture

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space-optimization methods, and carefully validate our diffusion process design choices. Supplemental materials and additional details are available at: https://gvecchio.com/controlmat/.

cs.CV↗

arXiv2024

MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment

We present MatAtlas, a method for consistent text-guided 3D model texturing. Following recent progress we leverage a large scale text-to-image generation model (e.g., Stable Diffusion) as a prior to texture a 3D model. We carefully design an RGB texturing pipeline that leverages a grid pattern diffusion, driven by depth and edges. By proposing a multi-step texture refinement process, we significantly improve the quality and 3D consistency of the texturing output. To further address the problem of baked-in lighting, we move beyond RGB colors and pursue assigning parametric materials to the assets. Given the high-quality initial RGB texture, we propose a novel material retrieval method capitalized on Large Language Models (LLM), enabling editabiliy and relightability. We evaluate our method on a wide variety of geometries and show that our method significantly outperform prior arts. We also analyze the role of each component through a detailed ablation study.

cs.CV↗