Learning Feature-Preserving Portrait Editing from Generated Pairs
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910546079514624 |
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| author | Chen, Bowei Zhi, Tiancheng Zhu, Peihao Sang, Shen Liu, Jing Luo, Linjie |
| author_facet | Chen, Bowei Zhi, Tiancheng Zhu, Peihao Sang, Shen Liu, Jing Luo, Linjie |
| contents | Portrait editing is challenging for existing techniques due to difficulties in preserving subject features like identity. In this paper, we propose a training-based method leveraging auto-generated paired data to learn desired editing while ensuring the preservation of unchanged subject features. Specifically, we design a data generation process to create reasonably good training pairs for desired editing at low cost. Based on these pairs, we introduce a Multi-Conditioned Diffusion Model to effectively learn the editing direction and preserve subject features. During inference, our model produces accurate editing mask that can guide the inference process to further preserve detailed subject features. Experiments on costume editing and cartoon expression editing show that our method achieves state-of-the-art quality, quantitatively and qualitatively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20455 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Feature-Preserving Portrait Editing from Generated Pairs Chen, Bowei Zhi, Tiancheng Zhu, Peihao Sang, Shen Liu, Jing Luo, Linjie Computer Vision and Pattern Recognition Portrait editing is challenging for existing techniques due to difficulties in preserving subject features like identity. In this paper, we propose a training-based method leveraging auto-generated paired data to learn desired editing while ensuring the preservation of unchanged subject features. Specifically, we design a data generation process to create reasonably good training pairs for desired editing at low cost. Based on these pairs, we introduce a Multi-Conditioned Diffusion Model to effectively learn the editing direction and preserve subject features. During inference, our model produces accurate editing mask that can guide the inference process to further preserve detailed subject features. Experiments on costume editing and cartoon expression editing show that our method achieves state-of-the-art quality, quantitatively and qualitatively. |
| title | Learning Feature-Preserving Portrait Editing from Generated Pairs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.20455 |