Tag
This paper introduces a physics-guided machine learning framework that integrates physical constraints into deep learning models (ConvLSTM, AFNONet, ViViT) to predict fuel density for wildfire management, outperforming purely data-driven approaches.
AnyBokeh is a physics-guided framework for any-to-any bokeh editing that estimates source blur states and transfers optical characteristics between different focus and aperture settings without requiring all-in-focus reconstruction.
StampFormer is a physics-guided deep learning framework that fuses geometry and material properties to predict FEA outcomes for sheet metal stamping in under a second, achieving high fidelity with less than 8.5% relative error.
This paper introduces WildRelight, a new real-world benchmark dataset for single-image relighting that addresses the gap between synthetic and natural scenes. It proposes a physics-guided adaptation framework using diffusion posterior sampling and test-time adaptation to improve model performance on real-world data.