Publications

Climate-driven environmental change is driving an increase in both the frequency and severity of wildfire events, making accurate simulation and prediction critical for effective risk mitigation and landscape management. While recent physics-based wildfire models achieve high realism by explicitly simulating combustion, heat transfer, and fuel dynamics, they remain largely restricted to synthetic environments with complete and idealized knowledge of forest structure, limiting their applicability to real-world environments captured via aerial imagery. To provide a pathway toward real-world wildfire digital twins derived directly from observational data, we present WildFireGS, a physics-based wildfire simulation framework operating directly on large-scale, semantics-enriched 3D Gaussian Splatting forest reconstructions. Our approach bridges learning-based scene reconstruction and environmental simulation by augmenting Gaussian primitives with semantics and material properties that encode vegetation type and fuel characteristics. We introduce a particle-based combustion model that operates natively on Gaussian representations, simulating ignition, heat transfer, combustion, and flame propagation across complex forest structures. This enables direct physics-based simulation of fire behavior on reconstructed real-world environments, without requiring conversion to explicit meshes or volumetric grids. We demonstrate the modularity of WildFireGS through a rain-driven cooling mechanism in terms of an energy-sink process to realistically model fire containment. Evaluations on synthetic scenes and real aerial forest captures show physically consistent wildfire behavior, reproducing characteristic dynamics including propagation scaling with vegetation density, wind velocity, and terrain slope. In addition, we validate our model through novel firebreak experiments and biomass loss estimation.

We propose Gaussian point splatting, a stochastic method for rendering massive 3DGS scenes. By sampling pixel-sized opaque points, splatting them atomically, and applying stochastic transparency, we eliminate the need for sorting. Our approach distributes workload evenly across GPU threads, enabling the real-time display of hundreds of millions of Gaussians efficiently.

Holonomy is a virtual environment based on the mathematical concept of hyperbolic geometry. Unlike other environments, Holonomy allows users to seamlessly explore an infinite hyperbolic space by physically walking. They use their body as the controller, eliminating the need for teleportation or other artificial VR locomotion methods. This paper discusses the development of Holonomy, highlighting the technical challenges faced and overcome during its creation, including rendering complex hyperbolic environments, populating the space with objects, and implementing algorithms for finding shortest paths in the underlying non-Euclidean geometry. Furthermore, we present a proof-of-concept implementation in the form of a VR navigation game and some preliminary learning outcomes from this implementation.

Learning motor skills is essential to many different aspects of life, from big movements needed for sports to small and simple movements used in the rehabilitation of stroke patients. In recent years, sonification, i.e. using sounds as feedback for actions, has been researched as a promising technique for studying motor behavior. In particular, we explore how to use sonification to make the process of learning motor skills accessible and engaging. We posit that an interactive and gamified environment can increase the engagement in that process. Moreover, an enjoyable setting is more likely to stimulate repetition, an indispensable feature of any learning endeavor. We, therefore, designed and developed Pizzicato, a rhythm-based serious game that leads players to move their arms and hands to actively play music. The game uses a common webcam to track your hand movements: pinching one finger to the thumb at the right position and moment will play musical notes that pleasantly add up to a full musical track. Our player tests have shown that players find Pizzicato accessible and engaging, and report that playing the game gives them a strong sense of agency. Pizzicato was developed in collaboration with neuropsychology colleagues, who are now starting to use it as a flexible tool for motor behavior research, both for diagnostic and rehabilitation purposes.