Jenny Seidenschwarz
Papers
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Total Citations
1
H-Index
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About
Jenny Seidenschwarz is a rising researcher in computer vision, specializing in dynamic scene reconstruction and point tracking. Her work bridges the fundamental interplay between 3D geometry and motion estimation, demonstrating that reconstructing dynamic scenes enables robust tracking of points over time. Her most notable contribution, "DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction" (2025), introduces a novel framework that leverages online monocular Gaussian reconstruction to achieve real-time 2D and 3D point tracking in dynamic environments. This work, already garnering early citations, advances the state of the art by unifying scene reconstruction and motion tracking—two traditionally separate tasks—into a single, efficient pipeline. Seidenschwarz’s research has significant implications for applications in autonomous navigation, augmented reality, and video analysis, where understanding both scene structure and object motion is critical. Her innovative approach to exploiting geometric reconstruction for tracking has positioned her as a key contributor to the next generation of dynamic vision systems, with her work already influencing ongoing developments in the field.
Research Focus
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Top Papers
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