Xuanchi Ren

Papers

Total Citations

H-Index

About

Xuanchi Ren is a rising researcher in computer vision and machine learning, with a focus on video restoration and perception for autonomous systems. Their most notable contribution is a pioneering approach to video deblurring, introduced in the 2020 paper "Video Deblurring by Fitting to Test Data." This work addresses a critical challenge for autonomous vehicles and robots: motion blur that degrades perception in real-world video feeds. Ren’s key insight—that some frames within a blurred video are inherently sharper—led to a novel method that fits a deep network directly to the test video, enabling dynamic, on-the-fly deblurring without requiring large pre-trained datasets. Though early in their career, with the paper garnering 5 citations, this work has laid a foundation for test-time adaptation techniques in video enhancement. Ren’s research bridges the gap between robust visual perception and practical deployment in robotics, offering a scalable solution for improving camera-based sensing in dynamic environments. Their innovative approach signals a promising trajectory in advancing real-world computer vision applications.