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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2512.11883 |
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| _version_ | 1866917482485252096 |
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| author | Guo, Wenqi Marshall Qian, Qingyun Hasan, Khalad Du, Shan |
| author_facet | Guo, Wenqi Marshall Qian, Qingyun Hasan, Khalad Du, Shan |
| contents | Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when "anti-aesthetic" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism. We test this bias by constructing a wide-spectrum aesthetics dataset and evaluating state-of-the-art generation and reward models. This position paper finds that aesthetic-aligned generation models frequently default to conventionally beautiful outputs, failing to respect instructions for low-quality or negative imagery. Crucially, reward models penalize anti-aesthetic images even when they perfectly match the explicit user prompt. We confirm this systemic bias through image-to-image editing and evaluation against real abstract artworks. Our code, fine-tuned models, and datasets are available on our meta-expression intentionally anti-aesthetics webpage: https://weathon.github.io/icml2026_position/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position: Universal Aesthetic Alignment Narrows Artistic Expression Guo, Wenqi Marshall Qian, Qingyun Hasan, Khalad Du, Shan Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when "anti-aesthetic" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism. We test this bias by constructing a wide-spectrum aesthetics dataset and evaluating state-of-the-art generation and reward models. This position paper finds that aesthetic-aligned generation models frequently default to conventionally beautiful outputs, failing to respect instructions for low-quality or negative imagery. Crucially, reward models penalize anti-aesthetic images even when they perfectly match the explicit user prompt. We confirm this systemic bias through image-to-image editing and evaluation against real abstract artworks. Our code, fine-tuned models, and datasets are available on our meta-expression intentionally anti-aesthetics webpage: https://weathon.github.io/icml2026_position/. |
| title | Position: Universal Aesthetic Alignment Narrows Artistic Expression |
| topic | Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.11883 |