The Digital Emily Project uses advanced face scanning, character rigging, performance capture, and compositing to achieve one of the world's first photorealistic digital facial performances. The project scanned the geometry and reflectance of actress Emily O'Brien's face in 33 poses, showing different emotions, gaze directions, and lip formations in a light stage. These high-resolution scans-accurate to skin pores and fine wrinkles-became the basis for building a blendshape-based facial-animation rig whose expressions closely matched the scans. The blendshape rig drove displacement maps to add dynamic surface detail. A video-based facial animation system animated the face according to the performance in a reference video, and the digital face was tracked onto the video's motion and rendered under the same illumination. The result was a realistic 3D digital facial performance credited as one of the first to cross the "uncanny valley" between animated and fully human performances.

We present a process for rendering a realistic facial performance with control of viewpoint and illumination. The performance is based on one or more high-quality geometry and reflectance scans of an actor in static poses, driven by one or more video streams of a performance. We compute optical flow correspondences between neighboring video frames, and a sparse set of correspondences between static scans and video frames. The latter are made possible by leveraging the relightability of the static 3D scans to match the viewpoint(s) and appearance of the actor in videos taken in arbitrary environments. As optical flow tends to compute proper correspondence for some areas but not others, we also compute a smoothed, per-pixel confidence map for every computed flow, based on normalized cross-correlation. These flows and their confidences yield a set of weighted triangulation constraints among the static poses and the frames of a performance. Given a single artist-prepared face mesh for one static pose, we optimally combine the weighted triangulation constraints, along with a shape regularization term, into a consistent 3D geometry solution over the entire performance that is drift free by construction. In contrast to previous work, even partial correspondences contribute to drift minimization, for example, where a successful match is found in the eye region but not the mouth. Our shape regularization employs a differential shape term based on a spatially varying blend of the differential shapes of the static poses and neighboring dynamic poses, weighted by the associated flow confidences. These weights also permit dynamic reflectance maps to be produced for the performance by blending the static scan maps. Finally, as the geometry and maps are represented on a consistent artist-friendly mesh, we render the resulting high-quality animated face geometry and animated reflectance maps using standard rendering tools.

We present a technique for synthesizing the effects of skin microstructure deformation by anisotropically convolving a highresolution displacement map to match normal distribution changes in measured skin samples. We use a 10-micron resolution scanning technique to measure several in vivo skin samples as they are stretched and compressed in different directions, quantifying how stretching smooths the skin and compression makes it rougher. We tabulate the resulting surface normal distributions, and show that convolving a neutral skin microstructure displacement map with blurring and sharpening filters can mimic normal distribution changes and microstructure deformations. We implement the spatially-varying displacement map filtering on the GPU to interactively render the effects of dynamic microgeometry on animated faces obtained from high-resolution facial scans.

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