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Photographic Lighting Design with Photographer-in-the-Loop Bayesian Optimization

Photographic Lighting Design with Photographer-in-the-Loop Bayesian Optimization

Digital Nature Group

January 29, 2023
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  1. Photographic Lighting Design with


    Photographer-in-the-Loop Bayesian Optimization
    Kenta Yamamoto1 / Yuki Koyama2 / Yoichi Ochiai1
    1 University of Tsukuba, Digital Nature Group


    2 National Institute of Advanced Industrial Science and Technology (AIST)

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  2. 2
    Overview of Our Proposal
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  3. 3
    Related Work
    Schoeneman, Chris, et al. "Painting with light." Proceedings of the 20th annual conference on Computer graphics and
    interactive techniques. 1993.
    Murmann, Lukas, et al. "Computational bounce flash for indoor
    portraits." ACM Transactions on Graphics (TOG) 35.6 (2016): 1-9.
    Srikanth, Manohar, Kavita Bala, and Frédo Durand.
    "Computational rim illumination with aerial robots." Proceedings
    of the Workshop on Computational Aesthetics. 2014.
    Jane, L. E., Ohad Fried, and Maneesh Agrawala. "Optimizing
    Portrait Lighting at Capture-Time Using a 360 Camera as a Light
    Probe." UIST. 2019.
    Boyadzhiev, Ivaylo, Sylvain Paris, and Kavita Bala. "User-assisted
    image compositing for photographic lighting." ACM Trans.
    Graph. 32.4 (2013): 36-1.
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  4. 4
    Lighting Design Interface in CG World
    Schoeneman, Chris, et al. "Painting with light." Proceedings of the 20th annual conference on Computer graphics and interactive techniques.
    1993.
    Pellacini, Fabio, Parag Tole, and Donald P. Greenberg. "A user
    interface for interactive cinematic shadow design." ACM Transactions
    on Graphics (TOG) 21.3 (2002): 563-566.
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  5. 5
    Lighting Design in Real World (automatic lighting)
    Murmann, Lukas, et al. "Computational bounce flash for indoor portraits." ACM Transactions on
    Graphics (TOG) 35.6 (2016): 1-9.
    Srikanth, Manohar, Kavita Bala, and Frédo Durand. "Computational rim illumination with aerial
    robots." Proceedings of the Workshop on Computational Aesthetics. 2014.
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  6. 6
    Lighting Design in Real World (at capture-time)
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    Jane, L. E., Ohad Fried, and Maneesh Agrawala. "Optimizing Portrait Lighting at Capture-Time Using a 360 Camera as a Light
    Probe." UIST. 2019.
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  7. 7
    Lighting Design in Real World (post-processing; re-lighting)
    Boyadzhiev, Ivaylo, Sylvain Paris, and Kavita Bala. "User-assisted image compositing for
    photographic lighting." ACM Trans. Graph. 32.4 (2013): 36-1.
    #PZBE[IJFWFUBM<5P(>
    Sun, Tiancheng, et al. "Single image portrait relighting." ACM Trans. Graph. 38.4 (2019): 79-1.
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  8. Our Proposed Framework

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  9. 9
    Overview of our framework
    1. Selection 2. Painting 3. Search for Next Options
    Photographer-in-the-Loop
    Photographer Part System Part
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  10. 10
    Human-in-the-loop Design Optimization (Human Evaluator)
    1. Selection 2. Painting 3. Search for Next Options
    Photographer-in-the-Loop
    Photographer Part System Part
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    Human-in-the-loop Design Optimization (Parameter Optimization)
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  13. 13
    Bayesian Optimization (BO)
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    Koyama, Yuki, Toby Chong, and Takeo Igarashi. "Preferential Bayesian Optimisation for Visual Design." Bayesian Methods for Interaction and Design (2022): 239.
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  14. 14
    Preferential Bayesian Optimization (PBO) / Preferential Data Model
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  15. 15
    Preferential Bayesian Optimization in Our Framework
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  16. 16
    Preferential Bayesian Optimization in Our Framework



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    PBO with Paint-based Guidance in Our Framework
    Selection Paint
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    #Iteration 1
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  18. 18
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    Chong, Toby, et al. "Interactive Optimization of Generative Image Modelling using
    Sequential Subspace Search and Content-based Guidance." Computer Graphics
    Forum. Vol. 40. No. 1. 2021.
    PBO with Paint-based Guidance in Our Framework
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    Lighting Prediction in Our Framework
    Ground Truth Prediction

    View full-size slide

  20. 20
    Overall Workflow
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  21. 21
    Overall Workflow
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  22. 22
    Overall Workflow
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  23. 23
    Overall Workflow
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  24. Live Demonstration


    (simulated virtual environment; not physical environment)

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  25. Result & Evaluation

    View full-size slide

  26. 26
    Result
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  27. 27
    Technical Evaluation
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    (a) 3D (Light Position) (b) 5D (Light and Reflector Position)
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    (c) 8D (2 Lights Position and Power)
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    (d) 12D (2 Lights Position and Color)
    Target Paint

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  28. 28
    User Study
    (a) Proposed Framework
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    White Light
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    Orange Light
    with Robot Arm
    Reflector
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    White Light
    with Tripod
    Orange Light
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    Reflector
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    (b) Traditional Framework
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    Shooting Objects
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  29. 29
    User Study
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    (b) Satisfaction
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  30. 30
    Discussion
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  31. Photographic Lighting Design with


    Photographer-in-the-Loop Bayesian Optimization
    Kenta Yamamoto1 / Yuki Koyama2 / Yoichi Ochiai1
    1 University of Tsukuba, Digital Nature Group


    2 National Institute of Advanced Industrial Science and Technology (AIST)

    View full-size slide