首个分类型创意评分模型，可区分几何、材质、纹理三类创意。
CREward: A Type-Specific Creativity Reward Model
- 按图像生成流程的三类创意维度建模，实现精细化评分。
- 结合人类评价与大视觉语言模型预测，训练出高可信度评分模型。
- 适用于创意评估、解释生成过程及启发设计与生成优化。
创造力是复杂现象。将创造力视为单一整体显然过于简单。本文提出首个分类型的创意奖励模型CREward，涵盖几何、材质和纹理三个创意维度，使我们能从图像生成流程的角度审视创造力。我们首先通过人类基准评估，捕捉人们对各类创意图像的感知。接着分析人类判断与大视觉语言模型（LVLMs）预测之间的相关性，发现LVLMs与人类感知高度一致。基于此，我们收集LVLM生成的标签来训练CREward模型，该模型可应用于创意图像的评估与生成。我们探索了三个应用场景：创意评估、可解释的创意分析，以及用于人类设计启发和通过低秩适配引导创意生成的创意样本获取。
原文摘要 · Abstract (English)
Creativity is a complex phenomenon. When it comes to representing and assessing creativity, treating it as a single undifferentiated quantity would appear naive and underwhelming. In this work, we learn the \emph{first type-specific creativity reward model}, coined CREward, which spans three creativity ``axes," geometry, material, and texture, to allow us to view creativity through the lens of the image formation pipeline. To build our reward model, we first conduct a human benchmark evaluation to capture human perception of creativity for each type across various creative images. We then analyze the correlation between human judgments and predictions by large vision-language models (LVLMs), confirming that LVLMs exhibit strong alignment with human perception. Building on this observation, we collect LVLM-generated labels to train our CREward model that is applicable to both evaluation and generation of creative images. We explore three applications of CREward: creativity assessment, explainable creativity, and creative sample acquisition for both human design inspiration and guiding creative generation through low-rank adaptation.
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