Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.21074 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908303378874368 |
|---|---|
| author | Zhang, Xiao Gao, William Jain, Seemandhar Maire, Michael Forsyth, David A. Bhattad, Anand |
| author_facet | Zhang, Xiao Gao, William Jain, Seemandhar Maire, Michael Forsyth, David A. Bhattad, Anand |
| contents | Image relighting is the task of showing what a scene from a source image would look like if illuminated differently. Inverse graphics schemes recover an explicit representation of geometry and a set of chosen intrinsics, then relight with some form of renderer. However error control for inverse graphics is difficult, and inverse graphics methods can represent only the effects of the chosen intrinsics. This paper describes a relighting method that is entirely data-driven, where intrinsics and lighting are each represented as latent variables. Our approach produces SOTA relightings of real scenes, as measured by standard metrics. We show that albedo can be recovered from our latent intrinsics without using any example albedos, and that the albedos recovered are competitive with SOTA methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_21074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Latent Intrinsics Emerge from Training to Relight Zhang, Xiao Gao, William Jain, Seemandhar Maire, Michael Forsyth, David A. Bhattad, Anand Computer Vision and Pattern Recognition Image relighting is the task of showing what a scene from a source image would look like if illuminated differently. Inverse graphics schemes recover an explicit representation of geometry and a set of chosen intrinsics, then relight with some form of renderer. However error control for inverse graphics is difficult, and inverse graphics methods can represent only the effects of the chosen intrinsics. This paper describes a relighting method that is entirely data-driven, where intrinsics and lighting are each represented as latent variables. Our approach produces SOTA relightings of real scenes, as measured by standard metrics. We show that albedo can be recovered from our latent intrinsics without using any example albedos, and that the albedos recovered are competitive with SOTA methods. |
| title | Latent Intrinsics Emerge from Training to Relight |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.21074 |