Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency

Yun, Haoran; Luis, Ponton, Jose; Andújar, Carlos; Pelechano, Nuria

doi:10.1109/vr55154.2023.00044

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…With the increasing availability of low-cost wearable technology, and the establishment of robust joint-angle calculation methods, greater applications of IMUs will be realized, including remote real-time monitoring and telemedicine, particularly in the elderly and motor-compromised [ 138 , 139 , 140 ], sports training, and human performance optimization [ 141 , 142 , 143 ], defense applications such as measurement and monitoring of front-line soldiers [ 98 , 144 ], habitual motion evaluation over extended periods including in submarines and spaceflight, and film and animation applications [ 145 , 146 ].…”

Section: Discussionmentioning

confidence: 99%

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Fang

Woodford

Senanayake

et al. 2023

Sensors

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Inertial measurement units (IMUs) have become the mainstay in motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; the second was to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion–extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction–retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapular locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.

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Section: Discussionmentioning

confidence: 99%

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Fang

Woodford

Senanayake

et al. 2023

Sensors

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“…The generator synthesizes high-quality and continuous human poses. However, for certain use cases such as VR, precise positioning of end-effectors may be necessary [Yun et al 2023]. We found that the generator's convolutional-based architecture struggles to preserve actual positions and rotations from the limited input data Q S , resulting in inaccuracies when positioning the endeffectors even when FK-based losses [Pavllo et al 2020[Pavllo et al , 2018 are utilized, as shown in Section 7.2.…”

Section: Network Structurementioning

confidence: 99%

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

Ponton,

Yun,

Aristidou

et al. 2023

ACM Trans. Graph.

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Accurate and reliable human motion reconstruction is crucial for creating natural interactions of full-body avatars in Virtual Reality (VR) and entertainment applications. As the Metaverse and social applications gain popularity, users are seeking cost-effective solutions to create full-body animations that are comparable in quality to those produced by commercial motion capture systems. In order to provide affordable solutions though, it is important to minimize the number of sensors attached to the subject’s body. Unfortunately, reconstructing the full-body pose from sparse data is a heavily under-determined problem. Some studies that use IMU sensors face challenges in reconstructing the pose due to positional drift and ambiguity of the poses. In recent years, some mainstream VR systems have released 6-degree-of-freedom (6-DoF) tracking devices providing positional and rotational information. Nevertheless, most solutions for reconstructing full-body poses rely on traditional inverse kinematics (IK) solutions, which often produce non-continuous and unnatural poses. In this paper, we introduce SparsePoser, a novel deep learning-based solution for reconstructing a full-body pose from a reduced set of six tracking devices. Our system incorporates a convolutional-based autoencoder that synthesizes high-quality continuous human poses by learning the human motion manifold from motion capture data. Then, we employ a learned IK component, made of multiple lightweight feed-forward neural networks, to adjust the hands and feet towards the corresponding trackers. We extensively evaluate our method on publicly available motion capture datasets and with real-time live demos. We show that our method outperforms state-of-the-art techniques using IMU sensors or 6-DoF tracking devices, and can be used for users with different body dimensions and proportions.

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“…When having a small number of trackers, it is challenging to provide full-body animation, and thus it is common to visualize only a part of the body (e.g., head and hands or upper body). The type of avatar visualization preferred by the user may depend on the type of task (Pastel et al 2020) and also on the quality of the animations provided by the tracking system (Yun et al 2023). In our work we developed a method to enhance the animation of full-body avatars to be used in immersive VR regardless of task requirements.…”

Section: Embodimentmentioning

confidence: 99%

Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality

et al. 2023

Self Cite

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In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinematics (IK), it is essential to have a good match in size between the avatar and the user, as otherwise mismatches in self-avatar posture could be noticeable for the user. To achieve such a correct match in dimensions, a manual process is often required, with the need for a second person to take measurements of body limbs and introduce them into the system. This process can be time-consuming, and prone to errors. In this paper, we propose an automatic measuring method that simply requires the user to do a small set of exercises while wearing a Head-Mounted Display (HMD), two hand controllers, and three trackers. Our work provides an affordable and quick method to automatically extract user measurements and adjust the virtual humanoid skeleton to the exact dimensions. Our results show that our method can reduce the misalignment produced by the IK system when compared to other solutions that simply apply a uniform scaling to an avatar based on the height of the HMD, and make assumptions about the locations of joints with respect to the trackers.

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Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency

Cited by 7 publications

References 41 publications

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality

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