Skip to content

Author

Karan Ahuja

We have 4 of 10 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#computer vision Preprint Oct 2026

FlowAvatar: Real-Time Full-Body Avatars from Sparse Egocentric Inputs on Consumer XR Devices

Prior avatar embodiment research has studied tracking completeness (how faithfully the body, hands, and face are sensed and reproduced) and personalized appearance in isolation, each requiring specialized lab equipment. How these factors interact on consumer hardware remains an open question, because no existing system...

Chen-Feng Gao, Taeyoung Yeon, Sungheon Park et al. · 0 citations
#computer vision Book Open access Sep 2026

ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU

Arm pose estimation enables applications in fitness, extended reality input, rehabilitation, and life logging. Prior smartwatch-based approaches rely on calibration poses and preprocessing pipelines that transform raw IMU measurements into standardized training formats. These steps hinder deployment in everyday setting...

B. Dev, Vasco Xu, Xi-Aan Loh et al. · 0 citations
#human-computer interacti... Preprint Aug 2026

EITWatch: Smartwatch-Integrated Planar Electrical Impedance Tomography for Hand Gesture Recognition

EITWatch is the first wrist-EIT system built around smartwatch case-back geometry, asking whether this contact patch alone can support gesture recognition: eight planar electrodes in a 31 mm ring acquire 35 impedance measurements at 48 Hz.

Xuan-You Liu, Novel Alam, Karan Ahuja · 0 citations
#machine learning Preprint Aug 2026

TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition

TransfHAR is implemented as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations, and indicates that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition...

Aidan Bradshaw, Riku Arakawa, Xin Liu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.