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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.07340 |
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| _version_ | 1866915540828684288 |
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| author | Li, Yongzhi Zhang, Saining Chen, Yibing Li, Boying Zhang, Yanxin Du, Xiaoyu |
| author_facet | Li, Yongzhi Zhang, Saining Chen, Yibing Li, Boying Zhang, Yanxin Du, Xiaoyu |
| contents | Personalized image generation aims to faithfully preserve a reference subject's identity while adapting to diverse text prompts. Existing optimization-based methods ensure high fidelity but are computationally expensive, while learning-based approaches offer efficiency at the cost of entangled representations influenced by nuisance factors. We introduce SpotDiff, a novel learning-based method that extracts subject-specific features by spotting and disentangling interference. Leveraging a pre-trained CLIP image encoder and specialized expert networks for pose and background, SpotDiff isolates subject identity through orthogonality constraints in the feature space. To enable principled training, we introduce SpotDiff10k, a curated dataset with consistent pose and background variations. Experiments demonstrate that SpotDiff achieves more robust subject preservation and controllable editing than prior methods, while attaining competitive performance with only 10k training samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpotDiff: Spotting and Disentangling Interference in Feature Space for Subject-Preserving Image Generation Li, Yongzhi Zhang, Saining Chen, Yibing Li, Boying Zhang, Yanxin Du, Xiaoyu Graphics Machine Learning Personalized image generation aims to faithfully preserve a reference subject's identity while adapting to diverse text prompts. Existing optimization-based methods ensure high fidelity but are computationally expensive, while learning-based approaches offer efficiency at the cost of entangled representations influenced by nuisance factors. We introduce SpotDiff, a novel learning-based method that extracts subject-specific features by spotting and disentangling interference. Leveraging a pre-trained CLIP image encoder and specialized expert networks for pose and background, SpotDiff isolates subject identity through orthogonality constraints in the feature space. To enable principled training, we introduce SpotDiff10k, a curated dataset with consistent pose and background variations. Experiments demonstrate that SpotDiff achieves more robust subject preservation and controllable editing than prior methods, while attaining competitive performance with only 10k training samples. |
| title | SpotDiff: Spotting and Disentangling Interference in Feature Space for Subject-Preserving Image Generation |
| topic | Graphics Machine Learning |
| url | https://arxiv.org/abs/2510.07340 |