论生成艺术的符号学基础阐释性评价

摘要

诠释是解读艺术语言的关键：观众通过从视觉作品中复原意义来与艺术家进行交流。然而当前生成艺术（GenArt）的评估方法仍局限于表面图像质量或对文本提示的字面遵循，未能考量创作者意图传递的深层象征或抽象意义。为弥补这一不足，我们通过形式化皮尔士符号学理论，将人机艺术交互（HGI）建模为级联符号过程。该框架揭示艺术意义通过三种模式传递——像似性、象征性和指示性，而现有评估方法主要停留在像似性模式，对后两者存在结构性盲区。为突破这种结构性局限，我们提出SemJudge评估器。该方法通过分层符号图（HSG）重构从文本提示到生成作品的意义建构过程，显式评估HGI中的象征性与指示性意义。大量定量实验表明，在注重诠释的精细艺术基准测试中，SemJudge比现有评估方法更贴近人类判断。用户研究进一步证明SemJudge能产生更具深度和洞察力的艺术解读，从而推动GenArt从生成"美观"图像向表达复杂人类经验的媒介演进。项目页面：https://github.com/songrise/SemJudge。

English

Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. However, current Generative Art (GenArt) evaluators remain fixated on surface-level image quality or literal prompt adherence, failing to assess the deeper symbolic or abstract meaning intended by the creator. We address this gap by formalizing a Peircean computational semiotic theory that models Human-GenArt Interaction (HGI) as cascaded semiosis. This framework reveals that artistic meaning is conveyed through three modes - iconic, symbolic, and indexical - yet existing evaluators operate heavily within the iconic mode, remaining structurally blind to the latter two. To overcome this structural blindness, we propose SemJudge. This evaluator explicitly assesses symbolic and indexical meaning in HGI via a Hierarchical Semiosis Graph (HSG) that reconstructs the meaning-making process from prompt to generated artifact. Extensive quantitative experiments show that SemJudge aligns more closely with human judgments than prior evaluators on an interpretation-intensive fine-art benchmark. User studies further demonstrate that SemJudge produces deeper, more insightful artistic interpretations, thereby paving the way for GenArt to move beyond the generation of "pretty" images toward a medium capable of expressing complex human experience. Project page: https://github.com/songrise/SemJudge.

论生成艺术的符号学基础阐释性评价

On Semiotic-Grounded Interpretive Evaluation of Generative Art

摘要

Support