DreamVu HandTrack Outperforms HaMeR and WiLoR in Hand Tracking Evaluation on Ego-Exo4D

Finds 98% of hands in first-person video it was never trained on, with about 60% fewer missed hands than two leading open models

PHILADELPHIA, PA, UNITED STATES, October 20, 2026 /EINPresswire.com/ -- DreamVu, a physical AI data infrastructure company, today released results showing that its hand pose model, DreamVu HandTrack, outperformed HaMeR and WiLoR, two widely used open hand pose models, on all six measures in an evaluation on Ego-Exo4D data. HandTrack also runs faster than real time.

Robots that learn manipulation from human demonstrations need accurate hand tracking. Every missed hand or misplaced joint becomes an error in the training data. DreamVu built HandTrack for real-world first-person video. It follows the camera wearer's two hands through a whole recording and keeps left and right apart. Given a camera's calibration, it also adapts to wide-angle lenses that produce a circular image.

DreamVu tested HandTrack on the hand pose validation set of Ego-Exo4D, a large research dataset of skilled human activity such as cooking, bike repair and playing music, recorded with camera glasses. HandTrack was trained on DreamVu's own footage from a different type of camera. No Ego-Exo4D data was used to train or tune it.

All three models processed the same 65 clips and were scored in 2D against the same manual labels: 4,144 frames and 6,477 hands. HaMeR and WiLoR were run with their official pretrained weights and a shared hand detector. Both open models work frame by frame; HandTrack uses the whole video, as it is designed to.

DreamVu HandTrack consistently leads across all evaluated metrics. For Joint Accuracy (PCK@0.10), DreamVu HandTrack scored 84.32%, outperforming HaMeR at 82.93% and WiLoR at 74.33%. In Hand Coverage (recall), DreamVu HandTrack reached 98.02%, compared to 95.12% for HaMeR and 94.94% for WiLoR.

For Left/Right Accuracy, DreamVu HandTrack achieved 99.80%, while WiLoR and HaMeR recorded 99.22% and 99.16%, respectively. Reference-Joint Coverage saw DreamVu HandTrack at 98.20%, followed by HaMeR at 95.63% and WiLoR at 95.47%. In detection performance, DreamVu HandTrack attained a perfect 100.00% Detection Precision and a 99.49% Detection F1 score, whereas HaMeR scored 99.47% Precision and 98.13% F1, and WiLoR logged 99.36% Precision and 98.02% F1.

The largest gain is in hand coverage. HandTrack found 98% of hands in the test footage, compared with about 95% for the other models. It made about four times fewer left/right mistakes and produced no false detections. Fewer gaps and fewer mix-ups mean cleaner robot training data.
In DreamVu's tests, HandTrack processes 1080p video at 36.4 frames per second, faster than the video plays. It also has a camera setting for glasses and fisheye cameras.

"Robot learning depends on the quality of the human data behind it," said Sashi Reddi, Co-Founder and CEO of DreamVu. "DreamVu HandTrack tracks hands more completely and accurately than the leading open models, on a dataset it was never trained on. And it now runs faster than real time."

Availability

Robotics teams interested in using DreamVu HandTrack can contact DreamVu at sales@dreamvu.ai. A side-by-side video comparison is available at https://www.dreamvu.ai/handtrack

About DreamVu

DreamVu designs and builds patented 360° 3D cameras for robots, and the software that runs on them. The company is headquartered in Philadelphia, PA, with offices in Palo Alto, CA and R&D in Hyderabad, India.

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Sanju Pillai
DreamVu
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