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Learning controls for blend shape based realistic facial animation

Published: 30 July 2006 Publication History

Abstract

Blend shape animation is the method of choice for keyframe facial animation: a set of blend shapes (key facial expressions) are used to define a linear space of facial expressions. However, in order to capture a significant range of complexity of human expressions, blend shapes need to be segmented into smaller regions where key idiosyncracies of the face being animated are present. Performing this segmentation by hand requires skill and a lot of time. In this paper, we propose an automatic, physically-motivated segmentation that learns the controls and parameters directly from the set of blend shapes. We show the usefulness and efficiency of this technique for both, motion-capture animation and keyframing. We also provide a rendering algorithm to enhance the visual realism of a blend shape model.

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cover image ACM Conferences
SIGGRAPH '06: ACM SIGGRAPH 2006 Courses
July 2006
83 pages
ISBN:1595933646
DOI:10.1145/1185657
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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Published: 30 July 2006

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Cited By

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  • (2024)Refined Inverse Rigging: A Balanced Approach to High-fidelity Blendshape AnimationSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687670(1-9)Online publication date: 3-Dec-2024
  • (2024)Sophia-in-Audition: Virtual Production with a Robot PerformerProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685509(11184-11193)Online publication date: 28-Oct-2024
  • (2024)A Survey on Realistic Virtual Human Animations: Definitions, Features and EvaluationsComputer Graphics Forum10.1111/cgf.1506443:2Online publication date: 30-Apr-2024
  • (2024)Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)10.1109/VR58804.2024.00064(429-438)Online publication date: 16-Mar-2024
  • (2024)Correspondence-Free Online Human Motion Retargeting2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00032(707-716)Online publication date: 18-Mar-2024
  • (2023)Synthesizing Human Activity for Data GenerationJournal of Imaging10.3390/jimaging91002049:10(204)Online publication date: 29-Sep-2023
  • (2023)Transforming graph data visualisations from 2D displays into augmented reality 3D space: A quantitative studyFrontiers in Virtual Reality10.3389/frvir.2023.11556284Online publication date: 28-Mar-2023
  • (2023)Defending Low-Bandwidth Talking Head Videoconferencing Systems From Real-Time Puppeteering Attacks2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00105(983-992)Online publication date: Jun-2023
  • (2022)Temporal Shape Transfer Network for 3D Human Motion2022 International Conference on 3D Vision (3DV)10.1109/3DV57658.2022.00054(424-432)Online publication date: Sep-2022
  • (2022)Customizing blendshapes to capture facial detailsThe Journal of Supercomputing10.1007/s11227-022-04885-779:6(6347-6372)Online publication date: 3-Nov-2022
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