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Qiaolian Zhu

dblp:374/3502 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0002-4488-5624ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 56% Collaborative and social computing · 44%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Immersive interaction
virtual reality training
0.812024
Enhancing Tai Chi Training System: Towards Group-Based and Hyper-Realistic Training Experiences · IEEE Trans. Vis. Comput. Graph. 2024
Immersive interaction
mixed reality interaction
0.212024
Enhancing Tai Chi Training System: Towards Group-Based and Hyper-Realistic Training Experiences · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

user study · 0.8motion guidance trajectories · 0.8
YearPublicationVenuePosition
2024 Enhancing Tai Chi Training System: Towards Group-Based and Hyper-Realistic Training Experiences
abstract
In this article, we propose a lightweight and flexible enhanced Tai Chi training system composed of multiple standalone virtual reality (VR) devices. The system aims to enable a hyper-realistic multi-user action training platform at low cost by displaying real-time action guidance trajectories, providing real-world impossible visual effects and functions, and rapidly enhancing movement precision and communication interest for learners. We objectively evaluate participants' action quality at different levels of immersion, including traditional coach guidance (TCG), VR, and mixed reality (MR), along with subjective measures like motion sickness, quality of interaction, social meaning, presence/immersion to comprehensively explore the system's feasibility. The results indicate VR performs the best in training accuracy, but MR provides superior social experience and relatively high accuracy. Unlike TCG, MR offers hyper-realistic hand movement trajectories and Tai Chi social references. Compared with VR, MR provides more realistic avatar companions and a safer environment. In summary, MR balances accuracy and social experience.
Shuting Ni, Qiaolian Zhu, Chunyi Xu, Yuzhi Li
IEEE Trans. Vis. Comput. Graph.5