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Lianyan Liu

dblp:387/4811 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0006-9088-1076ORCID · reported

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

Human-computer interaction and ubiquitous computing · 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
Design research and methods · 33% Personal fabrication and tangible interfaces · 33% Human-AI interaction · 33%

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

TopicWeightPapersLastEvidence papers
Design research and methods › design process
conceptual design
0.812024
ProtoDreamer: A Mixed-prototype Tool Combining Physical Model and Generative AI to Support Conceptual Design · UIST 2024
Human-AI interaction › human-AI co-creation
generative AI co-creation
0.812024
ProtoDreamer: A Mixed-prototype Tool Combining Physical Model and Generative AI to Support Conceptual Design · UIST 2024
Personal fabrication and tangible interfaces › physical computing
physical prototyping
0.812024
ProtoDreamer: A Mixed-prototype Tool Combining Physical Model and Generative AI to Support Conceptual Design · UIST 2024

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

large-scale generative models · 0.8evaluation study · 0.8
YearPublicationVenuePosition
2025 Exploring the role of Mixed Reality on Design Representations to Enhance User-Involved Co-Design Communication
abstract
As users transition from passive subjects to active partners in the co-design process, they bring unique insights based on their experiences, collaboratively envisioning a better future with designers. However, unlike designers who are adept at various forms of representation, most users lack advanced modeling or sketching skills to concretely present the three-dimensional (3D) forms or dynamic features of a design proposal. This hinders user expression and increases the cognitive load on designers, thereby reducing communication efficiency in the co-design process. Mixed Reality (MR) technology enables users to depict 3D information in real physical space using natural gestures. This means that MR can provide a low-learning-cost concrete expression method without compromising traditional communication methods. This study explores the role of MR in enhancing communication between designers and users during the early stages of design. A formative study was conducted to identify four key requirements, which informed the development of the DuoMR system. DuoMR supports designers and users in expressing design ideas through gesture modeling in a collaborative MR space. Results from the user study and practical case study show that DuoMR effectively reduces cognitive load and enhances mutual understanding during the co-design process.
Pei Chen 0005, Kexing Wang, Lianyan Liu, Xuanhui Liu, Zhuyu Teng, Lingyun Sun
Proc. ACM Hum. Comput. Interact.3
2024 ProtoDreamer: A Mixed-prototype Tool Combining Physical Model and Generative AI to Support Conceptual Design
abstract
Prototyping serves as a critical phase in the industrial conceptual design process, enabling exploration of problem space and identification of solutions. Recent advancements in large-scale generative models have enabled AI to become a co-creator in this process. However, designers often consider generative AI challenging due to the necessity to follow computer-centered interaction rules, diverging from their familiar design materials and languages. Physical prototype is a commonly used design method, offering unique benefits in prototype process, such as intuitive understanding and tangible testing. In this study, we propose ProtoDreamer, a mixed-prototype tool that synergizes generative AI with physical prototype to support conceptual design. ProtoDreamer allows designers to construct preliminary prototypes using physical materials, while AI recognizes these forms and vocal inputs to generate diverse design alternatives. This tool empowers designers to tangibly interact with prototypes, intuitively convey design intentions to AI, and continuously draw inspiration from the generated artifacts. An evaluation study confirms ProtoDreamer’s utility and strengths in time efficiency, creativity support, defects exposure, and detailed thinking facilitation.
Pei Chen 0005, Xuelong Xie, Chaoyi Lin, Lianyan Liu, Zhuoshu Li, Weitao You, Lingyun Sun
UIST5