Regressor-Guided Generative Image Editing Balances User Emotions to Reduce Time Spent Online
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914215729561600 |
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| author | Gebhardt, Christoph Willardt, Robin Sadat, Seyedmorteza Ning, Chih-Wei Brombach, Andreas Song, Jie Hilliges, Otmar Holz, Christian |
| author_facet | Gebhardt, Christoph Willardt, Robin Sadat, Seyedmorteza Ning, Chih-Wei Brombach, Andreas Song, Jie Hilliges, Otmar Holz, Christian |
| contents | Internet overuse is a widespread phenomenon in today's digital society. Existing interventions, such as time limits or grayscaling, often rely on restrictive controls that provoke psychological reactance and are frequently circumvented. Building on prior work showing that emotional responses mediate the relationship between content consumption and online engagement, we investigate whether regulating the emotional impact of images can reduce online use in a non-coercive manner. We introduce and systematically analyze three regressor-guided image-editing approaches: (i) global optimization of emotion-related image attributes, (ii) optimization in a style latent space, and (iii) a diffusion-based method using classifier and classifier-free guidance. While the first two approaches modify low-level visual features (e.g., contrast, color), the diffusion-based method enables higher-level changes (e.g., adjusting clothing, facial features). Results from a controlled image-rating study and a social media experiment show that diffusion-based edits balance emotional responses and are associated with lower usage duration while preserving visual quality. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_12289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regressor-Guided Generative Image Editing Balances User Emotions to Reduce Time Spent Online Gebhardt, Christoph Willardt, Robin Sadat, Seyedmorteza Ning, Chih-Wei Brombach, Andreas Song, Jie Hilliges, Otmar Holz, Christian Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Internet overuse is a widespread phenomenon in today's digital society. Existing interventions, such as time limits or grayscaling, often rely on restrictive controls that provoke psychological reactance and are frequently circumvented. Building on prior work showing that emotional responses mediate the relationship between content consumption and online engagement, we investigate whether regulating the emotional impact of images can reduce online use in a non-coercive manner. We introduce and systematically analyze three regressor-guided image-editing approaches: (i) global optimization of emotion-related image attributes, (ii) optimization in a style latent space, and (iii) a diffusion-based method using classifier and classifier-free guidance. While the first two approaches modify low-level visual features (e.g., contrast, color), the diffusion-based method enables higher-level changes (e.g., adjusting clothing, facial features). Results from a controlled image-rating study and a social media experiment show that diffusion-based edits balance emotional responses and are associated with lower usage duration while preserving visual quality. |
| title | Regressor-Guided Generative Image Editing Balances User Emotions to Reduce Time Spent Online |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.12289 |