Facial Performance Synthesis using Deformation-Driven Polynomial
Displacement Maps
SIGGRAPH Asia 2008
Wan-Chun Ma1,2    Andrew Jones1    Jen-Yuan Chiang1    Tim Hawkins1    Sune Frederiksen1   
Pieter Peers1    Marko Vukovic3    Ming Ouhyoung2    Paul Debevec1   
University of Southern California Institute for Creative Technologies1   
National Taiwan University2    Sony Pictures Imageworks3   
Abstract

We present a novel method for acquisition, modeling, compression, and synthesis of realistic facial deformations using polynomial displacement maps. Our method consists of an analysis phase where the relationship between motion capture markers and detailed facial geometry is inferred, and a synthesis phase where novel detailed animated facial geometry is driven solely by a sparse set of motion capture markers. For analysis, we record the actor wearing facial markers while performing a set of training expression clips.

We capture real-time high-resolution facial deformations, including dynamic wrinkle and pore detail, using interleaved structured light 3D scanning and photometric stereo. Next, we compute displacements between a neutral mesh driven by the motion capture markers and the high-resolution captured expressions. These geometric displacements are stored in a polynomial displacement map which is parameterized according to the local deformations of the motion capture dots. For synthesis, we drive the polynomial displacement map with new motion capture data. This allows the recreation of large-scale muscle deformation, medium and fine wrinkles, and dynamic skin pore detail. Applications include the compression of existing performance data and the synthesis of new performances.

Our technique is independent of the underlying geometry capture system and can be used to automatically generate high-frequency wrinkle and pore details on top of many existing facial animation systems.

Introduction

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Currently, creating realistic virtual faces often involves capturing textures, geometry, and facial motion of real people. However, it is difficult to capture and represent facial dynamics accurately at all scales. Face scanning systems can acquire high-resolution facial textures and geometry, but typically only for static poses. Motion capture techniques record continuous facial motion, but only at a coarse level of detail. Straightforward techniques of driving high-resolution character models by relatively coarse motion capture data often fails to produce realistic motion at medium and fine scales (top image).

In this work, we introduce a novel automated method for modeling and synthesizing facial performances with realistic dynamic wrinkles and fine scale facial details. Our approach is to leverage a real-time 3D scanning system to record training data of the high-resolution geometry and appearance of an actor performing a small set of predetermined facial expressions. Additionally, a set of motion capture markers is placed on the face to track large scale deformations. Next, we relate these large scale deformations to the deformations at finer scales. We represent this relation compactly in the form of two deformation-driven polynomial displacement maps (PDMs), encoding variations in medium-scale and fine-scale displacements for a face undergoing motion (bottom image).


We synthesize new high-resolution geometry and surface detail from sparse motion capture markers using deformation-driven polynomial displacement maps; our results agree well with high-resolution ground truth geometry of dynamic facial performances.

Downloads

SIGGRAPH Asia 2008 Video
GradiantMocap_H264.mov, (74MB)

SIGGRAPH Asia 2008 Paper
pdm_sa2008.pdf, (18.5MB)

SIGGRAPH Asia 2008 Talk
a3-ma.pdf, (1.72MB)

Related Projects

Surface Reflectomy
High-Resolution Face Scanning, Eurographics Symposium on Rendering 2007
Spatially Varying Subsurface Scattering, USC ICT Technical Report ICT-TR-01-2006

Light Stage 1
Acquiring the Reflectance Field of a Human Face, SIGGRAPH 2000
Facial Reflectance Field Demo, SIGGRAPH 2000 Creative Applications Laboratory
Realistic Human Face Scanning and Rendering, ICT Graphics Lab 2001

Light Stage 2
A Photometric Approach to Digitizing Cultural Artifacts, VAST 2001
Animatable Facial Reflectance Fields, EGSR 2004
Reflectance Field Rendering of Human Faces for "Spider-Man 2", SIGGRAPH 2004 Sketch

Light Stage 3
A Lighting Reproduction Approach to Live-Action Compositing, SIGGRAPH 2002
Optimizing Color Matching in a Lighting Reproduction System for Complex Subject and Illuminant Spectra, EGSR 2003

Light Stage 5
Postproduction Re-Illumination of Live Action Using Interleaved Lighting, SIGGRAPH 2004 Poster
Performance Geometry Capture for Spatially Varying Relighting, SIGGRAPH 2005 Sketch