Reconstructing detailed dynamic face geometry from monocular video

By Pablo Garrido, Levi Valgaerts, Chenglei Wu, Christian Theobalt. 2013

225 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Hybrid Model for Identity Obfuscation by Face Replacement (2018, methodology): “1], or by using information from in-the-wild images [12,13]. Such parametric models can act as strong regularizers for 3D face reconstruction problems, and have been widely used in optimization based [5,14,12,15,16,17] and learningbased [18,19,20,21,22,23] settings. Recently, a model-based face auto”
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018, methodology): “Dense photometric alignment is commonly used by many approaches [GVWT13, SSS14, TZN ∗ 15, CBZB15, TZS ∗ 16a, TZS ∗ 16b, GZC ∗ 16, WBGB16].”
Automatic acquisition of high-fidelity facial performances using monocular videos (2014, methodology): “We compare our method against state of the art in facial performance capture using monocular videos [Garrido et al. 2013].”
Total Moving Face Reconstruction (2014, methodology): “Instead of requiring the person to be scanned in the lab or participate in the reconstruction process (as many other methods require [24,18,10,41,33]), we leverage whatever existing imagery is available online or in personal photo collections.”
InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image (2017, methodology): “In reading order: 300-VW [40], LFW [16], Volker [47], FaceWarehouse [7], Thomas [13] and CelebA [29].”
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies (2018, background): “Recently, light-weighed systems based on a single camera show a compelling performance by leveraging morphable 3D face model on 2D measurements[22, 18, 31, 47, 13, 12, 56].”
Reconstruction of Personalized 3D Face Rigs from Monocular Video (2016, methodology): “The creation of the rig and the animation is fully automatic and solely based on one single monocular video sequence (i.e., neither a high-quality face scan [Garrido et al. 2013] nor a community photo collection [Suwajanakorn et al. 2014] of the actor has been used in the process).”
3D Morphable Face Models—Past, Present, and Future (2019, unknown): “Cao et al. [2015]; Garrido et al. [2013, 2016b]; Shi et al. [2014]; Suwajanakorn et al. [2014] also add high-frequency wrinkle-level details.”
Real-time expression transfer for facial reenactment (2015, methodology): “Recently, monocular off-line methods were proposed that fit a parametric blend shape [Garrido et al. 2013] or multi-linear face model [Shi et al.”
RGBD Datasets: Past, Present and Future (2016, background): “One option is to use deformable meshes provided for face datasets [36, 104] or fabrics [35], which can be synthetically re-rendered to give dense correspondences between frames (e.g. Zollh ¨ oefer et al. [121] re-render data from [104]).”
Adaptive 3D Face Reconstruction from Unconstrained Photo Collections (2017, background): “A video provides sufficient information for unconstrained reconstruction [27], [28], [29], [30], [31], [32], [33], [34], [70], [71] where each frame comes from the same camera and temporal coherence may be assumed.”
InverseFaceNet: Deep Monocular Inverse Face Rendering (2017, methodology): “Recent techniques approach monocular face reconstruction by fitting active appearance models [1, 17], blendshape models [9, 18, 19, 61], affine face models [15, 16, 20, 46, 51, 54, 58, 62], mesh geometry [26, 33, 47, 48, 52], or volumetric geometry [24] to input images or videos.”
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos (2016, methodology): “Offline RGB Performance Capture Recent offline performance capture techniques approach the hard monocular reconstruction problem by fitting a blendshape [15] or a multi-linear face [26] model to the input video sequence.”
Review of statistical shape spaces for 3D data with comparative analysis for human faces (2012, background): “…ergonomic design (eg. designing sizes for eye-glasses that sufﬁciently cover a population from a relatively small training set), tele-presence, and detailed facial capture in the entertainment industry, where state of the art methods require subject speciﬁc models [70] or sophisticated setups [71].”