TokenLight: Precise Lighting Control in Images using Attribute Tokens
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917416284454912 |
|---|---|
| author | Chaturvedi, Sumit Hold-Geoffroy, Yannick Ren, Mengwei Liu, Jingyuan Zhang, He Mei, Yiqun Dorsey, Julie Shu, Zhixin |
| author_facet | Chaturvedi, Sumit Hold-Geoffroy, Yannick Ren, Mengwei Liu, Jingyuan Zhang, He Mei, Yiqun Dorsey, Julie Shu, Zhixin |
| contents | This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image generation task and introduce attribute tokens to encode distinct lighting factors such as intensity, color, ambient illumination, diffuse level, and 3D light positions. The model is trained on a large-scale synthetic dataset with ground-truth lighting annotations, supplemented by a small set of real captures to enhance realism and generalization. We validate our approach across a variety of relighting tasks, including controlling in-scene lighting fixtures and editing environment illumination using virtual light sources, on synthetic and real images. Our method achieves state-of-the-art quantitative and qualitative performance compared to prior work. Remarkably, without explicit inverse rendering supervision, the model exhibits an inherent understanding of how light interacts with scene geometry, occlusion, and materials, yielding convincing lighting effects even in traditionally challenging scenarios such as placing lights within objects or relighting transparent materials plausibly. Project page: vrroom.github.io/tokenlight/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15310 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TokenLight: Precise Lighting Control in Images using Attribute Tokens Chaturvedi, Sumit Hold-Geoffroy, Yannick Ren, Mengwei Liu, Jingyuan Zhang, He Mei, Yiqun Dorsey, Julie Shu, Zhixin Computer Vision and Pattern Recognition Graphics This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image generation task and introduce attribute tokens to encode distinct lighting factors such as intensity, color, ambient illumination, diffuse level, and 3D light positions. The model is trained on a large-scale synthetic dataset with ground-truth lighting annotations, supplemented by a small set of real captures to enhance realism and generalization. We validate our approach across a variety of relighting tasks, including controlling in-scene lighting fixtures and editing environment illumination using virtual light sources, on synthetic and real images. Our method achieves state-of-the-art quantitative and qualitative performance compared to prior work. Remarkably, without explicit inverse rendering supervision, the model exhibits an inherent understanding of how light interacts with scene geometry, occlusion, and materials, yielding convincing lighting effects even in traditionally challenging scenarios such as placing lights within objects or relighting transparent materials plausibly. Project page: vrroom.github.io/tokenlight/ |
| title | TokenLight: Precise Lighting Control in Images using Attribute Tokens |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2604.15310 |