# Markerless Head Tracking for Accurate and Accessible Neuronavigation

arXiv: https://arxiv.org/abs/2602.07052 · HTML: https://mlsanity.com/paper/2602.07052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 69 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction](https://mlsanity.com/paper/2209.12354/) (2022)
- [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019)
- [Towards a complete 3D morphable model of the human head](https://mlsanity.com/paper/1911.08008/) (2019)
- [Combining 3D Morphable Models: A Large scale Face-and-Head Model](https://mlsanity.com/paper/1903.03785/) (2019)
- [Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection](https://mlsanity.com/paper/1903.10661/) (2019)
- [A Comprehensive Survey on Pose-Invariant Face Recognition](https://mlsanity.com/paper/1502.04383/) (2015)
- [A Real-Time Error Prevention System for Gaze-Based Interaction in Virtual Reality Based on Anomaly Detection](https://mlsanity.com/paper/2601.15146/) (2026)

## Abstract (arXiv)

Neuronavigation is widely used in biomedical research and interventions to guide the precise placement of instruments around the head to support procedures such as transcranial magnetic stimulation. Traditional systems, however, rely on subject-mounted markers that require manual registration, may shift during procedures, and can cause discomfort. We introduce and evaluate markerless approaches that replace expensive hardware and physical markers with low-cost visible and infrared light cameras incorporating stereo and depth sensing, combined with algorithmic modeling of the facial geometry. Validation with 50 human subjects yielded a median tracking discrepancy of only 2.32 mm and 2.01^circ for the best markerless algorithm compared to a conventional marker-based system, which indicates sufficient accuracy for transcranial magnetic stimulation and a substantial improvement over prior markerless results. The study also suggests that integration of the data from the various camera sensors can improve the overall accuracy further. The proposed markerless neuronavigation methods can reduce setup cost and complexity, improve patient comfort, and expand access to neuronavigation in clinical and research settings.