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Acquiring the Reflectance Field of a Human Face
Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, Mark Sagar
Light stage system capturing the full reflectance field of a human face enabling relighting with arbitrary illumination for photoreal CG faces.
Abstract
This paper presents a method to acquire the reflectance field of a human face using a light stage, capturing face images from multiple viewpoints under dense sampling of incident illumination directions. Reflectance functions are constructed for each pixel, enabling photorealistic re-rendering of the face under arbitrary novel lighting. The authors develop techniques to extrapolate the reflectance field to novel viewpoints using a skin reflectance model that separates specular and subsurface components, accounting for effects like shadows and interreflections.
How to read this
- Category
- Capture system: face reflectance acquisition for relighting
- Contributions
- A light stage that captures a face under dense sampling of incident illumination directions from multiple viewpoints
- Per-pixel reflectance functions that allow photorealistic re-rendering under arbitrary novel lighting
- A skin reflectance model separating specular and subsurface components to extrapolate the field to novel viewpoints
- Context
- Foundational photoreal-face capture work; relates to image-based rendering and reflectance modeling, treating a face as a measured reflectance field rather than a hand-built shading model.
- Correctness
- Demonstrated on captured human faces with the stated specular/subsurface separation; reading caveat is that viewpoint extrapolation and effects like shadows and interreflections rely on the chosen reflectance model, so accuracy away from sampled views and lighting depends on how well that model holds.
- Clarity
- Accessible at a conceptual level; a first pass conveys the light-stage idea, do a second pass for the reflectance-function construction and the skin model.
- How to read it
- Focus first on what is measured (reflectance field) versus what is modeled (specular/subsurface split); a second pass pays off if you care about how relighting and novel-view rendering are actually computed.
Builds on
Nothing in the archive, this is a starting point.
Built upon by
- Facial Performance Synthesis using Deformation-Driven Polynomial Displacement Maps 2008
- The Digital Emily Project: Achieving a Photorealistic Digital Actor 2010
- Next-Gen Characters: From Facial Scans to Facial Animation 2014
- Driving High-Resolution Facial Scans with Video Performance Capture 2015
- Democratizing the Creation of Animatable Facial Avatars 2024
Related work
- The Digital Emily Project: Achieving a Photorealistic Digital Actor 2010 / Tech Note
- Single-Shot High-Quality Facial Geometry and Skin Appearance Capture 2020 / SIGGRAPH
- Democratizing the Creation of Animatable Facial Avatars 2024 / arXiv
- Creating an Actor-Specific Facial Rig from Performance Capture 2016 / DigiPro
Keywords
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