Charles Javerliat | Searcharxiv

arXiv2026

GPU accelerated surface-based gaze mapping for XR experiences

Extended reality is a fast-growing domain for which there is an increasing need to analyze and understand user behavior. In particular, understanding human visual attention during immersive experiences is crucial for many applications. The visualization and analysis of visual attention are commonly done by building fixation density maps from eye-tracking data. Such visual attention mapping is well mastered for 3 degrees of freedom (3DoF) experiences (\textit{i.e.}, involving 360 images or videos) but much less so for 6DoFs data, when the user can move freely in the 3D space. In that case, the visual attention information has to be mapped onto the 3D objects themselves. Some solutions exist for constructing such surface-based 6DoFs attention maps, however, they own several drawbacks: processing time, strong dependence on mesh resolution and/or texture mapping, and/or unpractical data representation for further processing. In this context, we propose a novel GPU-based algorithm that resolves the issues above while being generated in interactive time and rendered in real-time. Experiment on a challenging scene demonstrates the accuracy and robustness of our approach. To stimulate research in this area, the source code is publicly released and integrated into PLUME for ease of use in XR experiments.

cs.HC↗

arXiv2025

Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras

Markerless multiview motion capture is often constrained by the need for precise camera calibration, limiting accessibility for non-experts and in-the-wild captures. Existing calibration-free approaches mitigate this requirement but suffer from high computational cost and reduced reconstruction accuracy. We present Kineo, a fully automatic, calibration-free pipeline for markerless motion capture from videos captured by unsynchronized, uncalibrated, consumer-grade RGB cameras. Kineo leverages 2D keypoints from off-the-shelf detectors to simultaneously calibrate cameras, including Brown-Conrady distortion coefficients, and reconstruct 3D keypoints and dense scene point maps at metric scale. A confidence-driven spatio-temporal keypoint sampling strategy, combined with graph-based global optimization, ensures robust calibration at a fixed computational cost independent of sequence length. We further introduce a pairwise reprojection consensus score to quantify 3D reconstruction reliability for downstream tasks. Evaluations on EgoHumans and Human3.6M demonstrate substantial improvements over prior calibration-free methods. Compared to previous state-of-the-art approaches, Kineo reduces camera translation error by approximately 83-85%, camera angular error by 86-92%, and world mean-per-joint error (W-MPJPE) by 83-91%. Kineo is also efficient in real-world scenarios, processing multi-view sequences faster than their duration in specific configuration (e.g., 36min to process 1h20min of footage). The full pipeline and evaluation code are openly released to promote reproducibility and practical adoption at https://liris-xr.github.io/kineo/.

cs.CV↗