Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23094 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908993183547392 |
|---|---|
| author | Huang, Qian Jaiswal, Mayoore Selvarasa Zhong, Zhen Pereira, Rochelle Min, Jianyuan |
| author_facet | Huang, Qian Jaiswal, Mayoore Selvarasa Zhong, Zhen Pereira, Rochelle Min, Jianyuan |
| contents | The real-world adoption of portrait relighting is hindered by dataset domain gaps, camera sensitivity, and computational costs. We address these challenges with Hybrid Domain Knowledge Fusion, a paradigm that fuses the specialized strengths of synthetic, One-Light-at-A-Time (OLAT), and real-world datasets into a compact model. Our approach features specialized prior models hardened by domain-aware adaptation, followed by augmented knowledge distillation into a lightweight student model with multi-domain expertise. Our method demonstrates a 6x to 240x inference speedup while maintaining state-of-the-art (SOTA) visual quality in the experiments. Additionally, we construct a massive, high-fidelity synthetic dataset with diverse ground-truth intrinsics to support our training pipeline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23094 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion Huang, Qian Jaiswal, Mayoore Selvarasa Zhong, Zhen Pereira, Rochelle Min, Jianyuan Computer Vision and Pattern Recognition Graphics Machine Learning The real-world adoption of portrait relighting is hindered by dataset domain gaps, camera sensitivity, and computational costs. We address these challenges with Hybrid Domain Knowledge Fusion, a paradigm that fuses the specialized strengths of synthetic, One-Light-at-A-Time (OLAT), and real-world datasets into a compact model. Our approach features specialized prior models hardened by domain-aware adaptation, followed by augmented knowledge distillation into a lightweight student model with multi-domain expertise. Our method demonstrates a 6x to 240x inference speedup while maintaining state-of-the-art (SOTA) visual quality in the experiments. Additionally, we construct a massive, high-fidelity synthetic dataset with diverse ground-truth intrinsics to support our training pipeline. |
| title | Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion |
| topic | Computer Vision and Pattern Recognition Graphics Machine Learning |
| url | https://arxiv.org/abs/2604.23094 |