Daniela Ivanova

This work generates synthetic plankton imagery conditioned on taxonomy: a CLIP encoder is adapted on a large plankton corpus with a ranked contrastive objective extended to deep, ragged taxonomies, then frozen to condition a parameter-efficient diffusion transformer.

High-resolution, multi-view consistent 3D texturing no longer requires costly per-scene optimization or custom fine-tuning: frozen 2D diffusion models can directly synthesize production-ready texture atlases with baked shadows at an 80% speedup.

Vision foundation models can effectively track animal poses with minimal labeled data, achieving superior accuracy and cross-species robustness.