Optimizing Portrait Lighting at Capture-Time
Using a 360 Camera as a Light Probe

Jane L. E
Stanford University

Ohad Fried
Stanford University

Maneesh Agrawala
Stanford University

Figure 1. In a fixed lighting environment, photographers can produce many different lighting styles (e.g. butterfly, right loop, right split, and right rim) just by rotating the subject in place without changing their location. Given an HDR environment map from a 360 camera at some initial orientation and a target lighting style (bottom left), our tool automatically identifies the optimal angle for reorienting the subject to match the desired lighting — e.g. 90° for butterfly lighting. We use a precomputed radiance transfer-based method on a generic head, skin, and camera model for efficiently optimizing lighting orientation and for visualizing the best orientation match (bottom right).


We present a capture-time tool designed to help casual photographers orient their subject to achieve a user-specified target facial appearance. The inputs to our tool are an HDR environment map of the scene captured using a 360 camera, and a target facial appearance, selected from a gallery of common studio lighting styles. Our tool computes the optimal orientation for the subject to achieve the target lighting using a computationally efficient precomputed radiance transfer-based approach. It then tells the photographer how far to rotate about the subject. Optionally, our tool can suggest how to orient a secondary external light source (e.g. a phone screen) about the subject's face to further improve the match to the target lighting. We demonstrate the effectiveness of our approach in a variety of indoor and outdoor scenes using many different subjects to achieve a variety of looks. A user evaluation suggests that our tool reduces the mental effort required by photographers to produce well-lit portraits.

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    title={Optimizing Portrait Lighting at Capture-Time Using a 360 Camera as a Light Probe},
    author={E, Jane L. and Fried, Ohad and Agrawala, Maneesh},
    booktitle={Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology},
    url = {https://dl.acm.org/doi/10.1145/3332165.3347893},
    doi = {10.1145/3332165.3347893},
    series = {UIST ’19}