Analyzing the Impact of Motion Artifacts on Full-Body Pose Reconstruction for Self-Avatars in Virtual Reality

Core Concepts

The accuracy of full-body pose reconstruction for self-avatars in virtual reality is significantly impacted by motion artifacts such as latency, framerate discrepancies, occlusions, and inaccuracies in position estimation.

Abstract

The paper examines the impact of various motion artifacts on the full-body pose reconstruction of self-avatars in virtual reality (VR) applications. The authors focus on four key issues: Latency between the Cartesian position data estimated from RGB videos and the motion signals from VR devices. Discrepancy in the data acquisition rate between the two motion data sources. Occlusions and other motion artifacts in the estimation of full-body Cartesian coordinates. Inaccuracies in the 3D position estimation algorithm. The authors manually introduce these artifacts into the motion data and evaluate their impact on the performance of two state-of-the-art animation models, AvatarPoser and HybridTrack. The results show that the models are highly sensitive to these artifacts, especially in terms of velocity reconstruction error. The authors also explore using the YOLOv8 pose estimation algorithm to generate the 3D Cartesian positions, which better reflects the noise and inaccuracies present in real-world scenarios. The findings highlight the importance of addressing these motion artifacts in the design of self-avatar animation systems for VR applications, as they can significantly degrade the quality and realism of the avatar's movements.

Stats

The mean per joint positional error (MPJPE) increases from 0.72 cm to 4.18 cm when the noise level (σ) in the 3D Cartesian positions is increased from 1 cm to 5 cm. The mean per joint rotational error (MPJRE) increases from 2.52° to 15.04° when the noise level (σ) is increased from 1 cm to 5 cm. The mean per joint velocity error (MPJVE) increases from 8.1 cm/s to 326.92 cm/s when the noise level (σ) is increased from 1 cm to 5 cm.

Quotes

"The latency between the Cartesian position estimated from RGB videos and the VR motion signals is a crucial factor that needs to be taken into account in the implementation of a VR animation system." "Depending on the cameras setup, a significant latency between the Cartesian positions and the sparse inputs can occur." "Occlusion poses a challenge for motion tracking systems because it can lose sight of one or multiple joints during the occluded period, leading to gaps or inaccuracies in the tracking pose."

Key Insights Distilled From

Self-Avatar Animation in Virtual Reality: Impact of Motion Signals Artifacts on the Full-Body Pose Reconstruction

by Anto... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18628.pdf

Deeper Inquiries

How can the animation models be made more robust to the identified motion artifacts, such as through the use of advanced sensor fusion techniques or adaptive algorithms

To enhance the robustness of the animation models against the identified motion artifacts, advanced sensor fusion techniques and adaptive algorithms can be employed. Sensor fusion involves integrating data from multiple sensors to improve accuracy and reliability. By combining information from different sources such as IMUs, RGB cameras, and VR motion devices, the models can have a more comprehensive dataset to work with, reducing the impact of occlusions and noise. Adaptive algorithms can dynamically adjust parameters based on the quality of input data, helping the models adapt to varying conditions. For example, machine learning algorithms can be trained to recognize and compensate for specific artifacts like occlusions or latency, improving the overall performance of the animation models.

What are the potential trade-offs between the accuracy of the full-body pose reconstruction and the computational complexity or latency of the animation system

There are trade-offs between the accuracy of full-body pose reconstruction and the computational complexity or latency of the animation system. Higher accuracy typically requires more sophisticated algorithms and processing power, leading to increased computational complexity. This complexity can result in higher latency, as more computations are needed to reconstruct the pose accurately. On the other hand, reducing computational complexity can lead to faster processing times but may sacrifice accuracy. Balancing these factors is crucial in designing an effective animation system. Optimal trade-offs can be achieved by optimizing algorithms for efficiency, utilizing parallel processing techniques, and implementing real-time adaptive strategies to minimize latency while maintaining accuracy.

How can the insights from this study be applied to improve the user experience and realism of self-avatars in collaborative virtual environments, where multiple users may be interacting simultaneously

The insights from this study can be applied to enhance the user experience and realism of self-avatars in collaborative virtual environments with multiple users. By addressing issues such as desynchronization, occlusions, and noise in motion data, the animation models can provide more accurate and immersive representations of users' movements. This improvement can lead to a more engaging and realistic interaction between avatars, enhancing the sense of presence and embodiment in virtual environments. Additionally, by optimizing the animation system for real-time responsiveness and robustness, the overall user experience in collaborative virtual environments can be significantly enhanced, fostering more natural and seamless interactions between users.