The use of self-avatars is gaining popularity thanks to affordable VR headsets. Unfortunately, mainstream VR devices often use a small number of trackers and provide low-accuracy animations. Previous studies have show...
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ISBN:
(纸本)9798350348156
The use of self-avatars is gaining popularity thanks to affordable VR headsets. Unfortunately, mainstream VR devices often use a small number of trackers and provide low-accuracy animations. Previous studies have shown that the Sense of Embodiment, and in particular the Sense of Agency, depends on the extent to which the avatar's movements mimic the user's movements. However, few works study such effect for tasks requiring a precise interaction with the environment, i.e., tasks that require accurate manipulation, precise foot stepping, or correct body poses. In these cases, users are likely to notice inconsistencies between their self-avatars and their actual pose. In this paper, we study the impact of the animation fidelity of the user avatar on a variety of tasks that focus on arm movement, leg movement and body posture. We compare three different animation techniques: two of them using Inverse Kinematics to reconstruct the pose from sparse input (6 trackers), and a third one using a professional motion capture system with 17 inertial sensors. We evaluate these animation techniques both quantitatively (completion time, unintentional collisions, pose accuracy) and qualitatively (Sense of Embodiment). Our results show that the animation quality affects the Sense of Embodiment. Inertial-based MoCap performs significantly better in mimicking body poses. Surprisingly, IK-based solutions using fewer sensors outperformed MoCap in tasks requiring accurate positioning, which we attribute to the higher latency and the positional drift that causes errors at the end-effectors, which are more noticeable in contact areas such as the feet.
In this paper, we present AnaVu, a light weight visualization system for teaching 3D anatomy at classroom scale. We propose a stereoscopic system along with an easy to use interface as a scalable 3D visual aid as oppo...
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ISBN:
(纸本)9781665453318
In this paper, we present AnaVu, a light weight visualization system for teaching 3D anatomy at classroom scale. We propose a stereoscopic system along with an easy to use interface as a scalable 3D visual aid as opposed to learning from traditional 2D images. This is an alternative to VR/XR devices that can only serve a handful of students and are heavy on computational resources. For large scale classes (similar to 50-150 students) 3D visualization provides good feedback of spatial relations, with stereoscopic projection further providing depth cues to distinguish fine structures. The visualization is controllable by the lecturer with the ability to control interactive operators along with labels, animations and multimedia capabilities. Lessons can be premeditated and loaded quickly in class to integrate with the ongoing lecture. A quantitative evaluation on 43 students yielded results which show the proposed solution to be viable and effective for learning.
This paper deals with deep cucumber recognition using CG (computer graphics)-based dataset generation. The variety and the size of the dataset are crucial in deep learning. Although there are many public datasets for ...
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ISBN:
(纸本)9784885523434
This paper deals with deep cucumber recognition using CG (computer graphics)-based dataset generation. The variety and the size of the dataset are crucial in deep learning. Although there are many public datasets for common situations like traffic scenes, we need to make a dataset for a particular scene like cucumber farms. As it is costly and time-consuming to annotate much data manually, we proposed generating images by CG and converting them to realistic ones using adversarial learning approaches. We compare several image conversion methods using real cucumber plant images.
Educational virtual reality (VR) applications are the most recent addition to the learning management tools in this modern age. Due to health concerns, financial concerns, and convenience, people are looking for alter...
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ISBN:
(纸本)9798350328387
Educational virtual reality (VR) applications are the most recent addition to the learning management tools in this modern age. Due to health concerns, financial concerns, and convenience, people are looking for alternate ways to teach and learn. An efficient VR-based teaching interface could enhance student engagement, learning outcomes, and overall educational experience. Typically, teachers in a VR classroom do not have a way to know what students are doing since students are not visible. An efficient teaching interface should include some mechanism for a teacher to monitor students and alert the teacher if a student is trying to catch the attention of the teacher. An ideal interface would be one, which helps a teacher effectively monitor students while teaching without increasing the cognitive load of the teacher. In this paper, we present a comparative study of two such student monitoring interfaces. In the first interface, the student activity related information is shown using icons near the student avatar (representing a student in the VR environment). While in the second interface, a set of centrally-arranged emoticon-like visual indicators are present in addition to the student avatar, and the student activity related information is shown near the student emoticon. We present a detailed user experiment comparing the two interfaces in terms of teaching management, student monitoring capability, cognitive load, and user preference. Participants preferred and performed better with Indicator-located interface over avatar-located interface.
DeskVR allows users to experience Virtual Reality (VR) while sitting at a desk without requiring extensive movements. This makes it better suited for professional work environments where productivity over extended per...
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ISBN:
(纸本)9798350328387
DeskVR allows users to experience Virtual Reality (VR) while sitting at a desk without requiring extensive movements. This makes it better suited for professional work environments where productivity over extended periods is essential. However, tasks that typically resort to mid-air gestures might not be suitable for DeskVR. In this paper, we focus on the fundamental task of object selection. We present TouchRay, an object selection technique conceived specifically for DeskVR that enables users to select objects at any distance while resting their hands on the desk. It also allows selecting objects' sub-components by traversing their corresponding hierarchical trees. We conducted a user evaluation comparing TouchRay against state-of-the-art techniques targeted at traditional VR. Results revealed that participants could successfully select objects in different settings, with consistent times and on par with the baseline techniques in complex tasks, without requiring mid-air gestures.
With redirected walking (RDW) technology, people can explore large virtual worlds in smaller physical spaces. RDW controls the trajectory of the user's walking in the physical space through subtle adjustments, so ...
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ISBN:
(纸本)9798350348156
With redirected walking (RDW) technology, people can explore large virtual worlds in smaller physical spaces. RDW controls the trajectory of the user's walking in the physical space through subtle adjustments, so as to minimize the collision between the user and the physical space. Previous predictive algorithms place constraints on the user's path according to the spatial layouts of the virtual environment and work well when applicable, while reactive algorithms are more general for scenarios involving free exploration or unconstrained movements. However, even in relatively free environments, we can predict the user's walking to a certain extent by analyzing the user's historical walking data, which can help the decision-making of reactive algorithms. This paper proposes a novel RDW method that improves the effect of real-time unrestricted RDW by analyzing and utilizing the user's historical walking data. In this method, the physical space is discretized by considering the user's location and orientation in the physical space. Using the weighted directed graph obtained from the user's historical walking data, we dynamically update the scores of different reachable poses in the physical space during the user's walking. We rank the scores and choose the optimal target position and orientation to guide the user to the best pose. Since simulation experiments have been shown to be effective in many previous RDW studies, we also provide a method to simulate user walking trajectories and generate a dataset. Experiments show that our method outperforms multiple state-of-the-art methods in various environments of different sizes and spatial layouts.
In this study, we propose a method for an aerial display. The method uses a high-speed gaze control system and a laser display to perform projection mapping on a screen at a distance, which is suspended from a flying ...
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ISBN:
(纸本)9798350348156
In this study, we propose a method for an aerial display. The method uses a high-speed gaze control system and a laser display to perform projection mapping on a screen at a distance, which is suspended from a flying drone. A prototype system was developed and successfully demonstrated dynamic projection mapping on a screen attached to a flying drone at a distance of about 36 m, which indicated the effectiveness of the proposed method.
This work proposes a motion style transfer network that transfers motion style between different motion categories using variational autoencoders. The proposed network effectively transfers style among various motion ...
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ISBN:
(纸本)9798400701528
This work proposes a motion style transfer network that transfers motion style between different motion categories using variational autoencoders. The proposed network effectively transfers style among various motion categories and can create stylized motion unseen in the dataset. The network contains a content-conditioned module to preserve the characteristic of the content motion, which is important for real applications. We implement the network with variational autoencoders, which enable us to control the intensity of the style and mix different styles to enrich the motion diversity.
Pose-driven avatar animation is widely applied in VR fields. The flexible creation of animations is a significant yet challenging task due to the heterogeneous topologies and shapes of avatars. To alleviate this, we p...
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ISBN:
(纸本)9798350348392
Pose-driven avatar animation is widely applied in VR fields. The flexible creation of animations is a significant yet challenging task due to the heterogeneous topologies and shapes of avatars. To alleviate this, we propose a unified proxy skeleton for retargeting (UPSR) to achieve consistent motions of the virtual avatar. Particularly, the heterogeneous topologies are converted into a unified skeleton topology of the avatars by using a learned nonlinear mapping function. Furthermore, we propose to retarget the skeletons with different body shapes into 3D virtual avatars. Our UPSR can produce avatar animations with a higher level of authenticity without the dependency on high-cost motion capture devices or the restrictions of its topology. Additionally, we could drive detailed motions based on multiple sources of motion datasets, including monocular videos, motion capture devices, and standard motion files, with the precise motion capture of eyes, lips, hands, and bodies.
This work focuses on the problem of reconstructing a 3D human body mesh from a given 2D image. Despite the inherent ambiguity of the task of human mesh recovery, most existing works have adopted a method of regressing...
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ISBN:
(纸本)9798350307443
This work focuses on the problem of reconstructing a 3D human body mesh from a given 2D image. Despite the inherent ambiguity of the task of human mesh recovery, most existing works have adopted a method of regressing a single output. In contrast, we propose a generative approach framework, called "Diffusion-based Human Mesh Recovery (Diff-HMR)" that takes advantage of the denoising diffusion process to account for multiple plausible outcomes. During the training phase, the SMPL parameters are diffused from ground-truth parameters to random distribution, and Diff-HMR learns the reverse process of this diffusion. In the inference phase, the model progressively refines the given random SMPL parameters into the corresponding parameters that align with the input image. Diff-HMR, being a generative approach, is capable of generating diverse results for the same input image as the input noise varies. We conduct validation experiments, and the results demonstrate that the proposed framework effectively models the inherent ambiguity of the task of human mesh recovery in a probabilistic manner. Code is available at https: //***/hanbyel0105/Diff-HMR.
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