R
Ronan Boulic
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 202
Citations - 5583
Ronan Boulic is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Inverse kinematics & Computer animation. The author has an hindex of 37, co-authored 194 publications receiving 5248 citations. Previous affiliations of Ronan Boulic include École Polytechnique & École Normale Supérieure.
Papers
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Journal ArticleDOI
A global human walking model with real-time kinematic personification
TL;DR: This paper presents a human walking model built from experimental data based on a wide range of normalized velocities that allows a personification of the walking action in an interactive real-time context in most cases.
Journal ArticleDOI
An inverse kinematics architecture enforcing an arbitrary number of strict priority levels
Paolo Baerlocher,Ronan Boulic +1 more
TL;DR: This paper progressively describes the strategic components of a very general and robust inverse kinematics architecture and presents an efficient recursive algorithm enforcing an arbitrary number of strict priorities to arbitrate the fulfillment of conflicting constraints.
Proceedings ArticleDOI
Task-priority formulations for the kinematic control of highly redundant articulated structures
Paolo Baerlocher,Ronan Boulic +1 more
TL;DR: Two formulations for the kinematic control of redundant manipulators, based on task prioritization are compared and an incremental method to speed up the evaluation of the solution is proposed.
Proceedings ArticleDOI
Skeleton-based motion capture for robust reconstruction of human motion
TL;DR: This work uses a sophisticated anatomic human model to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3D reconstruction process.
Book ChapterDOI
Local and Global Skeleton Fitting Techniques for Optical Motion Capture
TL;DR: This paper discusses two skeleton fitting techniques based on 3D optical marker data and a local technique is proposed based on relative marker trajectories and a global optimization of a skeleton model is compared.