User interface (UI) design is a difficult yet important task for ensuring the
usability, accessibility, and aesthetic qualities of applications. In our
paper, we develop a machine-learned model, UIClip, for assessing the design
quality and visual relevance of a UI given its screenshot and natural language
description. To train UIClip, we used a combination of automated crawling,
synthetic augmentation, and human ratings to construct a large-scale dataset of
UIs, collated by description and ranked by design quality. Through training on
the dataset, UIClip implicitly learns properties of good and bad designs by i)
assigning a numerical score that represents a UI design's relevance and quality
and ii) providing design suggestions. In an evaluation that compared the
outputs of UIClip and other baselines to UIs rated by 12 human designers, we
found that UIClip achieved the highest agreement with ground-truth rankings.
Finally, we present three example applications that demonstrate how UIClip can
facilitate downstream applications that rely on instantaneous assessment of UI
design quality: i) UI code generation, ii) UI design tips generation, and iii)
quality-aware UI example search.

通过机器学习模型 UIClip，本研究开发了一种用于评估 UI 设计质量和视觉相关性的方法，通过截图和自然语言描述来对 UI 进行评估，实验证明 UIClip 在与其他基准方法和人工设计师评分进行对比时取得了最高一致性，从而为 UI 设计质量的直接评估提供了便利。

UIClip：一个基于数据的用户界面设计评估模型

UIClip: A Data-driven Model for Assessing User Interface Design

Modeling 3D avatars benefits various application scenarios such as AR/VR,
gaming, and filming. Character faces contribute significant diversity and
vividity as a vital component of avatars. However, building 3D character face
models usually requires a heavy workload with commercial tools, even for
experienced artists. Various existing sketch-based tools fail to support
amateurs in modeling diverse facial shapes and rich geometric details. In this
paper, we present SketchMetaFace - a sketching system targeting amateur users
to model high-fidelity 3D faces in minutes. We carefully design both the user
interface and the underlying algorithm. First, curvature-aware strokes are
adopted to better support the controllability of carving facial details.
Second, considering the key problem of mapping a 2D sketch map to a 3D model,
we develop a novel learning-based method termed "Implicit and Depth Guided Mesh
Modeling" (IDGMM). It fuses the advantages of mesh, implicit, and depth
representations to achieve high-quality results with high efficiency. In
addition, to further support usability, we present a coarse-to-fine 2D
sketching interface design and a data-driven stroke suggestion tool. User
studies demonstrate the superiority of our system over existing modeling tools
in terms of the ease to use and visual quality of results. Experimental
analyses also show that IDGMM reaches a better trade-off between accuracy and
efficiency. SketchMetaFace are available at
this https URL

本文介绍了 SketchMetaFace 系统，它为业余用户提供了一个快速建立逼真立体脸部的草图系统，使用了曲率感知笔画以及一种名为 “显式深度引导网格建模” 的新学习方法，通过数据驱动的笔画建议工具和分阶段标注接口设计增强了系统的可用性，并通过用户研究和实验分析验证其优越性。