SLAM algorithms struggle on endoscopy videos - specularity, textureless, & dynamic lighting.

We introduce a Near-Field Light Bundle Adjst. loss (NFL-BA): improves performance of SOTA SLAM, e.g. MonoGS (⬆️35% in tracking, ⬆️48% in mapping).

See asdunnbe.github.io/NFL-BA/
Led by Andrea & Daniel

💡Introducing ScribbleLight, a generative model that can relight an image with simple scribbles - making it easier for virtual staging and interior design of homes.

Led by Jun-Myeong, collabs with Anand (TTIC), Pieter (W&M), and Annie.
@unccs.bsky.social

👉 chedgekorea.github.io/ScribbleLight/

SOTA virtual face re-aging techniques often struggle to preserve identity for large age changes.
We present MyTimeMachine, a personalized virtual aging gen. model, trained with ~50 images across 20-40 years.
Check out more cool results here: mytimemachine.github.io
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