Pith. sign in

REVIEW 4 cited by

Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2110.07775 v1 pith:DJ3ODGJJ submitted 2021-10-14 cs.HC cs.AIcs.LG

Forrest Huang , Gang Li , Xin Zhou , John F. Canny , Yang Li This is my paper · ORCID
classification cs.HCcs.AIcs.LG
keywords designmock-upshigh-leveldesignersmethodmethodsprocessdeep-learning
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The design process of user interfaces (UIs) often begins with articulating high-level design goals. Translating these high-level design goals into concrete design mock-ups, however, requires extensive effort and UI design expertise. To facilitate this process for app designers and developers, we introduce three deep-learning techniques to create low-fidelity UI mock-ups from a natural language phrase that describes the high-level design goal (e.g. "pop up displaying an image and other options"). In particular, we contribute two retrieval-based methods and one generative method, as well as pre-processing and post-processing techniques to ensure the quality of the created UI mock-ups. We quantitatively and qualitatively compare and contrast each method's ability in suggesting coherent, diverse and relevant UI design mock-ups. We further evaluate these methods with 15 professional UI designers and practitioners to understand each method's advantages and disadvantages. The designers responded positively to the potential of these methods for assisting the design process.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The GenUI Study: Exploring the Design of Generative UI Tools to Support UX Practitioners and Beyond

    cs.HC 2025-01 conditional novelty 7.0 of 10

    A formative diary study with 37 UX professionals found that generative UI tools help most with first drafts, ideation, and cross-role communication, while editing, context, and integration remain weak.

  2. SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection

    cs.AI 2025-09 conditional novelty 6.0 of 10

    SheetDesigner uses zero-shot multimodal LLMs with rule- and vision-based reflection to generate spreadsheet layouts, and claims a 22.6% gain over baselines on a new seven-criterion benchmark.

  3. Athena: Intermediate Representations for Iterative Scaffolded App Generation with an LLM

    cs.HC 2025-08 conditional novelty 6.0 of 10

    Using intermediate representations (a storyboard, data model, and GUI skeletons) alongside chat lets developers iteratively generate multi-screen SwiftUI apps, and 9 of 12 study participants preferred this approach ov...

  4. Zero-Shot Prompting Approaches for LLM-based Graphical User Interface Generation

    cs.SE 2024-12 conditional novelty 6.0 of 10

    A self-critique prompting loop outperformed retrieval-augmented and decomposed prompting for zero-shot generation of high-fidelity GUI prototypes, based on over 3,000 crowdworker ratings.

Pith tools