Eval: YOLO26 pose estimation for gesture-based hands-free input #26

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opened 2026-06-23 09:29:10 -07:00 by pyr0ball · 0 comments

pyr0ball commented

2026-06-23 09:29:10 -07:00

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Paper: https://arxiv.org/abs/2606.03748 — Ultralytics YOLO26 (June 2026)
Code/weights: https://github.com/ultralytics/ultralytics

What it is

Unified real-time vision model with a dedicated pose estimation head. Real-time
latency (1.7–11.8ms on T4 TensorRT). Exports to CoreML, TFLite, OpenVINO, NCNN,
ExecuTorch for edge/on-device deployment.

Raven use cases

Gesture detection: Body/hand pose keypoints as input signals for hands-free
computing — replaces or augments mouse/keyboard for users who cannot use traditional
input devices.
Head pose tracking: Head orientation as cursor control input (complement to
DIY EEG / BCI track).
Proximity/attention: Detect whether user is present and looking at screen to
trigger context-aware adaptations.
Low-latency requirement: Raven needs real-time response for viable input UX.
YOLO26's <12ms latency on GPU, plus TFLite path for CPU fallback, is viable.

License situation

AGPL-3.0 for open source; commercial license for distribution. For Raven (local,
on user's own hardware), AGPL-3.0 is acceptable in the free tier. Revisit if Raven
cloud features are added.

Recommendation

Evaluate alongside existing gesture/pose libraries when Raven enters active
development. YOLO26 is a strong candidate for the vision input layer given its
real-time pose head and edge export support.

raven#X: Virtual KVM gesture routing
Alan's ADS1299-based DIY EEG (BCI track — parallel input modality)

## Source Paper: https://arxiv.org/abs/2606.03748 — Ultralytics YOLO26 (June 2026) Code/weights: https://github.com/ultralytics/ultralytics ## What it is Unified real-time vision model with a dedicated pose estimation head. Real-time latency (1.7–11.8ms on T4 TensorRT). Exports to CoreML, TFLite, OpenVINO, NCNN, ExecuTorch for edge/on-device deployment. ## Raven use cases - **Gesture detection:** Body/hand pose keypoints as input signals for hands-free computing — replaces or augments mouse/keyboard for users who cannot use traditional input devices. - **Head pose tracking:** Head orientation as cursor control input (complement to DIY EEG / BCI track). - **Proximity/attention:** Detect whether user is present and looking at screen to trigger context-aware adaptations. - **Low-latency requirement:** Raven needs real-time response for viable input UX. YOLO26's <12ms latency on GPU, plus TFLite path for CPU fallback, is viable. ## License situation AGPL-3.0 for open source; commercial license for distribution. For Raven (local, on user's own hardware), AGPL-3.0 is acceptable in the free tier. Revisit if Raven cloud features are added. ## Recommendation Evaluate alongside existing gesture/pose libraries when Raven enters active development. YOLO26 is a strong candidate for the vision input layer given its real-time pose head and edge export support. ## Related - raven#X: Virtual KVM gesture routing - Alan's ADS1299-based DIY EEG (BCI track — parallel input modality)

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Reference: Circuit-Forge/raven#26

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