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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/2512.21637 |
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| _version_ | 1866912789213216768 |
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| author | Shabrina, Mutiara Putri, Nova Kurnia Ferdiansyah, Jefri Satria Dewi, Sabita Khansa Yudistira, Novanto |
| author_facet | Shabrina, Mutiara Putri, Nova Kurnia Ferdiansyah, Jefri Satria Dewi, Sabita Khansa Yudistira, Novanto |
| contents | Text-driven image manipulation often suffers from attribute entanglement, where modifying a target attribute (e.g., adding bangs) unintentionally alters other semantic properties such as identity or appearance. The Predict, Prevent, and Evaluate (PPE) framework addresses this issue by leveraging pre-trained vision-language models for disentangled editing. In this work, we analyze the PPE framework, focusing on its architectural components, including BERT-based attribute prediction and StyleGAN2-based image generation on the CelebA-HQ dataset. Through empirical analysis, we identify a limitation in the original regularization strategy, where latent updates remain dense and prone to semantic leakage. To mitigate this issue, we introduce a sparsity-based constraint using L1 regularization on latent space manipulation. Experimental results demonstrate that the proposed approach enforces more focused and controlled edits, effectively reducing unintended changes in non-target attributes while preserving facial identity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Training-Free Disentangled Text-Guided Image Editing via Sparse Latent Constraints Shabrina, Mutiara Putri, Nova Kurnia Ferdiansyah, Jefri Satria Dewi, Sabita Khansa Yudistira, Novanto Computer Vision and Pattern Recognition Text-driven image manipulation often suffers from attribute entanglement, where modifying a target attribute (e.g., adding bangs) unintentionally alters other semantic properties such as identity or appearance. The Predict, Prevent, and Evaluate (PPE) framework addresses this issue by leveraging pre-trained vision-language models for disentangled editing. In this work, we analyze the PPE framework, focusing on its architectural components, including BERT-based attribute prediction and StyleGAN2-based image generation on the CelebA-HQ dataset. Through empirical analysis, we identify a limitation in the original regularization strategy, where latent updates remain dense and prone to semantic leakage. To mitigate this issue, we introduce a sparsity-based constraint using L1 regularization on latent space manipulation. Experimental results demonstrate that the proposed approach enforces more focused and controlled edits, effectively reducing unintended changes in non-target attributes while preserving facial identity. |
| title | Training-Free Disentangled Text-Guided Image Editing via Sparse Latent Constraints |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.21637 |