How can generative models synthesize and represent information across sensors, scales, and modalities? We develop generative methods for images, 3D scenes, panoramas, and multimodal earth data. Recent work includes open-world generation of stereo images with unsupervised matching (GenStereo), fine-grained satellite image synthesis with structured semantics (VectorSynth), geospatially guided diffusion for mixed-view panorama synthesis, zero-shot soundscape mapping from satellite imagery (Sat2Sound), and diffusion-guided visual active search in partially observable environments (DiffVAS). Our research connects generative modeling to geospatial science, from consistent text-to-360 scene generation to synthetic data for downstream vision and ecology tasks.
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