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Ruida Tang

dblp:384/2964 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0005-9452-9355ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 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 · 77% Collaborative and social computing · 23%

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

TopicWeightPapersLastEvidence papers
Immersive interaction › virtual reality
virtual reality user behavior
1.012026
Monkey See, Monkey Break? Study of Rule-Breaking Imitation in Virtual Crowds · IEEE Trans. Vis. Comput. Graph. 2026
Collaborative and social computing › online communities
social norms
0.312026
Monkey See, Monkey Break? Study of Rule-Breaking Imitation in Virtual Crowds · IEEE Trans. Vis. Comput. Graph. 2026

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

gaze tracking · 1.0VR study · 1.0
YearPublicationVenuePosition
2026 Monkey See, Monkey Break? Study of Rule-Breaking Imitation in Virtual Crowds
abstract
Rule-breaking behaviors, such as jaywalking or skipping queues, are common in crowds but difficult to study in real-world settings due to limited control and observability. Virtual reality (VR) provides a controlled alternative, but its validity depends on whether VR elicits realistic rule-breaking behavior. We conducted a VR study with 65 participants navigating a virtual city with four scenarios differing in social norms: walking on grass, crossing outside a crosswalk, jaywalking at a red light, and skipping a line. In each scenario, the proportion of rule-breaking virtual characters was manipulated (0%, 25%, 50%). Participants' movements and gaze were recorded to assess behavior and attention. Results showed higher rule-breaking as the number of violators increased, except in the low-stakes crossing scenario. Rule-breakers attended more to violating characters, whereas rule-followers focused on compliant ones. Participants cited efficiency, safety, and social norms as key factors guiding their decisions. Overall, VR reproduced natural patterns of social compliance and noncompliance, supporting its use for studying rule-breaking and applications in crowd simulation, urban design, safety training, and immersive media.
Kexiang Huang, Tairan Yin, Jose Luis Ponton, Ruida Tang, Reiya Itatani, Oscar Argudo, Nuria Pelechano
IEEE Trans. Vis. Comput. Graph.4
2024 Choreographing multi-degree of freedom behaviors in large-scale crowd simulations
Kexiang Huang, Ruida Tang, Nuria Pelechano
Comput. Graph.4