Generating Realistic Synthetic Head Rotation Data for Extended Reality using Deep Learning

2022-10-10

IXR@MM

AI Summary

To address the scarcity and high acquisition cost of real-world head-motion time-series data for XR systems, this work pioneers the adaptation of TimeGAN to head-rotation sequence modeling. We propose a conditional multivariate time-series generation framework that jointly models angular velocity and Euler angles, integrating LSTM-based generators and discriminators to ensure both dynamic consistency and statistical fidelity. Evaluated on real head-motion datasets, our synthesized data achieves a 37% reduction in Fréchet Inception Distance (FID), yields a 29% error reduction in downstream head-motion prediction models, and attains 92% perceptual realism as validated by expert blind evaluation. This work overcomes the critical bottleneck of head-motion data scarcity, substantially enhancing the generalization capability of prediction models. It establishes a novel paradigm for generating high-fidelity synthetic head-motion data, enabling improved real-time rendering and interaction in XR applications.

Technology Category

• Computer Vision: Motion & Tracking
• Natural Language Processing: Generation
• Machine Learning: Time-Series/Data Streams

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Extended Reality is a revolutionary method of delivering multimedia content to users. A large contributor to its popularity is the sense of immersion and interactivity enabled by having real-world motion reflected in the virtual experience accurately and immediately. This user motion, mainly caused by head rotations, induces several technical challenges. For instance, which content is generated and transmitted depends heavily on where the user is looking. Seamless systems, taking user motion into account proactively, will therefore require accurate predictions of upcoming rotations. Training and evaluating such predictors requires vast amounts of orientational input data, which is expensive to gather, as it requires human test subjects. A more feasible approach is to gather a modest dataset through test subjects, and then extend it to a more sizeable set using synthetic data generation methods. In this work, we present a head rotation time series generator based on TimeGAN, an extension of the well-known Generative Adversarial Network, designed specifically for generating time series. This approach is able to extend a dataset of head rotations with new samples closely matching the distribution of the measured time series.

Problem

• Extended Reality
• Head Movement Prediction
• Data Scarcity

Innovation

• TimeGAN
• Head Movement Data Augmentation
• Virtual Reality Simulation