Computational bounce flash for indoor portraits
Portraits taken with direct flash look harsh and unflattering because the light source comes from a small set of angles very close to the camera. Advanced photographers address this problem by using bounce flash, a technique where the flash is directed towards other surfaces in the room, creating a...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery (ACM)
2021
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| Online Access: | https://hdl.handle.net/1721.1/129416 |
| _version_ | 1826191109001314304 |
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| author | Murmann, Lukas Davis, Abe Kautz, Jan Durand, Frédo Davis, Abe |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Murmann, Lukas Davis, Abe Kautz, Jan Durand, Frédo Davis, Abe |
| author_sort | Murmann, Lukas |
| collection | MIT |
| description | Portraits taken with direct flash look harsh and unflattering because the light source comes from a small set of angles very close to the camera. Advanced photographers address this problem by using bounce flash, a technique where the flash is directed towards other surfaces in the room, creating a larger, virtual light source that can be cast from different directions to provide better shading variation for 3D modeling. However, finding the right direction to point a bounce flash requires skill and careful consideration of the available surfaces and subject configuration. Inspired by the impact of automation for exposure, focus and flash metering, we automate control of the flash direction for bounce illumination. We first identify criteria for evaluating flash directions, based on established photography literature, and relate these criteria to the color and geometry of a scene. We augment a camera with servomotors to rotate the flash head, and additional sensors (a fisheye and 3D sensors) to gather information about potential bounce surfaces. We present a simple numerical optimization criterion that finds directions for the flash that consistently yield compelling illumination and demonstrate the effectiveness of our various criteria in common photographic configurations. |
| first_indexed | 2024-09-23T08:50:41Z |
| format | Article |
| id | mit-1721.1/129416 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:50:41Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1294162022-09-23T14:55:30Z Computational bounce flash for indoor portraits Murmann, Lukas Davis, Abe Kautz, Jan Durand, Frédo Davis, Abe Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Portraits taken with direct flash look harsh and unflattering because the light source comes from a small set of angles very close to the camera. Advanced photographers address this problem by using bounce flash, a technique where the flash is directed towards other surfaces in the room, creating a larger, virtual light source that can be cast from different directions to provide better shading variation for 3D modeling. However, finding the right direction to point a bounce flash requires skill and careful consideration of the available surfaces and subject configuration. Inspired by the impact of automation for exposure, focus and flash metering, we automate control of the flash direction for bounce illumination. We first identify criteria for evaluating flash directions, based on established photography literature, and relate these criteria to the color and geometry of a scene. We augment a camera with servomotors to rotate the flash head, and additional sensors (a fisheye and 3D sensors) to gather information about potential bounce surfaces. We present a simple numerical optimization criterion that finds directions for the flash that consistently yield compelling illumination and demonstrate the effectiveness of our various criteria in common photographic configurations. 2021-01-13T22:13:12Z 2021-01-13T22:13:12Z 2016-11 2019-05-29T12:45:01Z Article http://purl.org/eprint/type/JournalArticle 0730-0301 1557-7368 https://hdl.handle.net/1721.1/129416 Murmann, Lukas et al. "Computational bounce flash for indoor portraits." ACM Transactions on Graphics 35, 6 (November 2016): 190 © 2016 The Authors en http://dx.doi.org/10.1145/2980179.2980219 ACM Transactions on Graphics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Association for Computing Machinery (ACM) ACM |
| spellingShingle | Murmann, Lukas Davis, Abe Kautz, Jan Durand, Frédo Davis, Abe Computational bounce flash for indoor portraits |
| title | Computational bounce flash for indoor portraits |
| title_full | Computational bounce flash for indoor portraits |
| title_fullStr | Computational bounce flash for indoor portraits |
| title_full_unstemmed | Computational bounce flash for indoor portraits |
| title_short | Computational bounce flash for indoor portraits |
| title_sort | computational bounce flash for indoor portraits |
| url | https://hdl.handle.net/1721.1/129416 |
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