ArtKrit分三步引导新手画作，帮其掌握构图、明暗和色彩。
Computational Scaffolding of Composition, Value, and Color for Disciplined Drawing
- 将临摹分解为构图、明暗、色彩三阶段，逐步指导。
- 提供自适应构图线与明暗色彩准确度自动反馈。
- 适合想提升绘画基本功的中级数字艺术家使用。
插画师通过研究和临摹参考图来训练技术，但对许多初学者和中级数字艺术家而言，如何有效分析参考图及获得即时反馈仍具挑战。为此，我们提出ArtKrit工具，将临摹过程拆解为构图、明暗、色彩三个阶段，每阶段提供计算辅助，如自适应构图线生成、明暗与色彩准确度自动反馈。在中等水平数字艺术家上的评估显示，ArtKrit可灵活适配不同工作流程。代码与补充材料见https://majiaju.io/artkrit。
原文摘要 · Abstract (English)
One way illustrators engage in disciplined drawing - the process of drawing to improve technical skills - is through studying and replicating reference images. However, for many novice and intermediate digital artists, knowing how to approach studying a reference image can be challenging. It can also be difficult to receive immediate feedback on their works-in-progress. To help these users develop their professional vision, we propose ArtKrit, a tool that scaffolds the process of replicating a reference image into three main steps: composition, value, and color. At each step, our tool offers computational guidance, such as adaptive composition line generation, and automatic feedback, such as value and color accuracy. Evaluating this tool with intermediate digital artists revealed that ArtKrit could flexibly accommodate their unique workflows. Our code and supplemental materials are available at https://majiaju.io/artkrit .
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