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research-article

High-quality passive facial performance capture using anchor frames

Published: 25 July 2011 Publication History

Abstract

We present a new technique for passive and markerless facial performance capture based on anchor frames. Our method starts with high resolution per-frame geometry acquisition using state-of-the-art stereo reconstruction, and proceeds to establish a single triangle mesh that is propagated through the entire performance. Leveraging the fact that facial performances often contain repetitive subsequences, we identify anchor frames as those which contain similar facial expressions to a manually chosen reference expression. Anchor frames are automatically computed over one or even multiple performances. We introduce a robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching. This allows us to propagate one reconstructed frame to an entire sequence in parallel, in contrast to previous sequential methods. Our anchored reconstruction approach also limits tracker drift and robustly handles occlusions and motion blur. The parallel tracking and mesh propagation offer low computation times. Our technique will even automatically match anchor frames across different sequences captured on different occasions, propagating a single mesh to all performances.

Supplementary Material

Supplemental material. (a75-beeler.zip)

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 30, Issue 4
July 2011
829 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2010324
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2011
Published in TOG Volume 30, Issue 4

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Author Tags

  1. facial performance capture
  2. motion capture
  3. space-time geometry reconstruction

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  • (2024)Polarimetric BSSRDF Acquisition of Dynamic FacesACM Transactions on Graphics10.1145/368776743:6(1-11)Online publication date: 19-Dec-2024
  • (2024)Universal Facial Encoding of Codec Avatars from VR HeadsetsACM Transactions on Graphics10.1145/365823443:4(1-22)Online publication date: 19-Jul-2024
  • (2024)4D Facial Expression Diffusion ModelACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365345521:1(1-23)Online publication date: 16-Dec-2024
  • (2024)Local Geometric Indexing of High Resolution Data for Facial Reconstruction From Sparse MarkersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328949530:8(5289-5298)Online publication date: Aug-2024
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  • (2024)Formulating facial mesh tracking as a differentiable optimization problem: a backpropagation-based solutionVisual Intelligence10.1007/s44267-024-00054-x2:1Online publication date: 19-Jul-2024
  • (2024)High-Quality Mesh Blendshape Generation from Face Videos via Neural Inverse RenderingComputer Vision – ECCV 202410.1007/978-3-031-72897-6_7(106-125)Online publication date: 2-Dec-2024
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