Realtime facial animation with on-the-fly correctives

By Hao Li, Jihun Yu, Yuting Ye, C. Bregler. ACM Transactions on Graphics, 2013

315 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/eca7098c427567c4d5f4c37872d8f3b7f5427c26

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In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services (2018, methodology): “For example, Apple’s A11 Bionic chip [7] used in iPhone X uses a neural engine in its GPU to speed up the DNN queries of applications such as face identification and facial motion capture [8].”
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018, methodology): “Given a monocular video as input, parameter estimation is commonly phrased as a general non-linear optimization problem [LYYB13,BWP13,CWS ∗ 13,CWLZ13,SWTC14,SSS14,CHZ14, GZC ∗ 16,TZN ∗ 15,TZS ∗ 16a,WSXC16,TZS ∗ 16b].”
FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction (2020, methodology): “M 0 is given by M 0 = max(0 , 1 There are many existing performance driven facial animation methods generating blendshape weights with depth camera[54, 29, 9] or single RGB camera[11, 10, 14].”
Learning a model of facial shape and expression from 4D scans (2017, background): “At the other end are simple generic face models that can be fit to images, video, or RGB-D data but that lack realism (e.g. [Li et al. 2013]).”
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction (2020, methodology): “Some methods [7, 18, 6, 12] also focused on corrective shapes [6], dynamically adapting the blendshape basis [18] or applied non-rigid mesh deformation [7] to compensate for the coarse geometry of the morphable model.”
Automatic acquisition of high-fidelity facial performances using monocular videos (2014, methodology): “Our system is also relevant to recent successes in tracking 3D facial expression using a single RGBD camera such as Microsoft Kinect or time-of-flight (TOF) cameras [Weise et al. 2011; Bouaziz et al. 2013; Li et al. 2013].”
Learning an appearance-based gaze estimator from one million synthesised images (2016, methodology): “It has since been used in a number of systems, including learning-by-synthesis for head pose estimation [Fanelli et al. 2013], real-time facial performance capture [Li et al. 2013], and learning a person-specific face model for gaze estimation [Mora and Odobez 2012].”
Real-time expression transfer for facial reenactment (2015, methodology): “In recent years, several approaches have been proposed for facial expression re-targeting, aimed at transferring facial expressions captured from a real subject to a virtual CG avatar [Weise et al. 2011; Li et al. 2013; Cao et al. 2014a].”
paGAN (2018, unknown): “Nevertheless, these dynamic face models are suitable for single-view face modeling and tracking applications [Blanz and Vetter 1999; Bouaziz et al. 2013; Cao et al. 2014, 2016; Garrido et al. 2016; Hsieh et al. 2015; Hu et al. 2017; Li et al. 2013; Saito et al. 2016, 2017; Thies et al. 2015, 2016a; ”
3D Morphable Face Models—Past, Present, and Future (2019, unknown): “Since monocular reconstruction methods solve a smaller optimization problem compared to multi-view methods, many real-time solutions exist [Bouaziz et al. 2013; Hsieh et al. 2015; Li et al. 2013; Thies et al. 2015; Weise et al. 2011a].”