SearcharxivSearch

arXiv · 1904.11704

XR: Enabling training mode in the human brain XR: Enabling training mode in the human brain

Abstract

The face of simulation-based training has greatly evolved, with the most recent tools giving the ability to create virtual environments that rival realism. At first glance, it might appear that what the training sector needs is the most realistic simulators possible, but traditional simulators are not necessarily the most efficient or practical training tools. With all that these new technologies have to offer; the challenge is to go back to the core of training needs and identify the right vector of sensory cues that will most effectively enable training mode in the human brain. Bigger and Pricier doesn't necessarily mean better. Simulation with cross-reality content (XR), which by definition encompasses virtual reality (VR), mixed reality (MR), and augmented reality (AR), is the most practical solution for deploying any kind of simulation-based training. The authors of this paper (a teacher and a technology expert) share their experiences and expose XR-specific best practices to maximize learning transfer. ABOUT THE AUTHORS Sebastien Loze : Starting his career in the modeling and simulation community more than 15 years ago, S{\'e}bastien has focused on learning about the latest simulation innovations and sharing information on how experts have solved their challenges. He worked on the COTS integration at CAE and the Presagis focusing on Simulation and Visualization products. More recently, Sebastien put together simulation and training teams and strategies for emerging companies like CM Labs and D-BOX. He is now the Simulations Industry Manager at Epic Games, focusing on helping companies develop real-time solutions for simulation-based training. Philippe Lepinard: Former military helicopter pilot and simulation officer, Philippe L{\'e}pinard is now an associate professor at the University of Paris-Est Cr{\'e}teil (UPEC). His research is focusing on playful learning and training through simulation. He is one of the founding members of the French simulation association.

Explore related subjects

Keep this discovery

BibTeXRIS

Philippe Lépinard, Sébastien Lozé. 2019-04-26. XR: Enabling training mode in the human brain XR: Enabling training mode in the human brain. https://arxiv.org/abs/1904.11704

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

ReCHOIR: Contact-guided Human Object Interaction Retargeting to Diverse Characters

We present ReCHOIR, a novel contact-guided motion retargeting method for transferring human object interaction (HOI) motions across diverse humanoid characters. Unlike prior motion retargeting methods that primarily focus on transferring human motion alone, our goal is to preserve not only the semantics of the original body movement but also consistent interaction between the character and the manipulated object, while jointly producing aligned target human and object motions. Given source HOI motion, object geometry, and contact cues extracted from the source interaction, ReCHOIR retargets an HOI sequence to target characters with different skeletal configurations while maintaining both motion semantics and contact-consistent interaction patterns. Our method builds on a Part-Aware Motion Embedding (PAME) autoencoder, which encodes full-body motion into a shared body-part-wise latent space. This representation enables generalization across heterogeneous skeletons while preserving local motion semantics beneficial for part-aware adaptation in HOI retargeting. On top of this representation, we introduce a contact-guided retargeting module and an object motion decoder for HOI retargeting. The contact-guided retargeting module treats the source object interaction as a condition for refining target character motion: object- and contact-related signals are encoded into a body-part-aligned latent representation and injected into decoding through a residual control branch, enabling stronger adaptation in interaction-relevant body regions without discarding the underlying motion prior. In parallel, the object motion decoder predicts a target object motion aligned with the refined target character motion, ensuring that the object trajectory remains consistent with how the interaction is realized by the target character.

cs.GR

Gaussian Light Transport

We present a novel method for computing global illumination by expressing the solution to the light transport equation as a 13D Gaussian mixture model over positions, directions, surface normals, and material properties. We show that including scene properties in the Gaussian representation drastically reduces the number of functions and speeds up evaluation. As opposed to traditional light transport methods based on Neumann series, the parameters of our model are directly estimated by minimizing the residual of the rendering equation. While both optimization and rendering require repeated evaluations of a linear combination of high-dimensional Gaussian functions, we introduce an efficient culling strategy to keep the optimization tractable and produce renderings in real time. Our representation enables to render fast, view-independent solutions to the light transport equation, achieving rendering times on the order of milliseconds, with a fraction of the memory requirements of conventional neural rendering approaches.

cs.GR

Hologram Representation via Quadratic Phase Gaussian Splatting

We introduce Complex-Valued Quadratic Phase Gaussian (CVQPG), a novel hologram representation method that replaces standard 2D Gaussian representations used in 2D Gaussian Splatting with 2D quadratic phase functions. CVQPG incorporates additional learnable parameters to control the curvature of these bases. We evaluate our approach against state-of-the-art methods, exceeding the visual quality by +0.19 dB (RGB) and +0.33 dB (grayscale) on average in holographic reconstructions. Specifically, our equal parameter count evaluations show that modulating the primitive's wavefront is an effective and lightweight enhancement for hologram representations. In addition, our frequency domain analysis illustrates that CVQPG has successfully preserved the mid-to-high frequency band of natural images.

cs.GR