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Author

Munchurl Kim

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Preprint Sep 2026

GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations

Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and observation settings. We introduce GeoCR, a generalist model that unifies RGB-only-based CR and multispectral-based CR from single- or multi-temporal cloudy observations, w...

Jeonghyeok Do, Munchurl Kim · 0 citations
Preprint Sep 2026

GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation

Paired synthetic aperture radar (SAR) and electro-optical (EO) imagery is increasingly available across sensors, resolutions, and geographic regions. Yet existing SAR-to-EO image translation (SET) methods are typically trained on a single, limited-scale dataset, producing models specialized to particular sensing condit...

Jeonghyeok Do, Munchurl Kim · 0 citations
Preprint Sep 2026

MotionMaestro: Masked Tokenization for Unified Motion Generation

Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While existing approaches have achieved remarkable progress, many of them are developed for individual tasks, including text-to-motion, pose-conditioned generation, and trajectory...

Yun Chen, Munchurl Kim, Jeonghyeok Do · 0 citations
Preprint Aug 2026

GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation

GeoCore-9B, a 9-billion-parameter generative foundation model, is introduced, which is the first of its scale to be trained from scratch exclusively on EO data and establishes new state-of-the-art performance in both visual fidelity and geographic structural accuracy.

Jeonghyeok Do, Munchurl Kim · 0 citations

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