Automatic acquisition of high-fidelity facial performances using monocular videos

By Fuhao Shi, Hsiang-Tao Wu, Xin Tong, Jinxiang Chai. 2014

143 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

A Hybrid Model for Identity Obfuscation by Face Replacement (2018, neutral): “We zero-pad the image if its dimensions are smaller than the crop size, thereby obtaining the ﬁnal crop with the desired 256 × 256 size.”
Production-level facial performance capture using deep convolutional neural networks (2016, methodology): “…or photometric reconstruction of individual input frames [Beeler et al. 2011; Bradley et al. 2010; Furukawa and Ponce 2009; Fyffe et al. 2011; Shi et al. 2014; Valgaerts et al. 2012] that often require carefully crafted 3D tracking model [Alexander et al. 2009; Borshukov et al. 2005; Vlasic…”
Deep video portraits (2018, background): “…and Vetter 1999], unstructured image collections [KemelmacherShlizerman 2013; Kemelmacher-Shlizerman et al. 2011; Roth et al. 2017] or video [Cao et al. 2014b; Fyfe et al. 2014; Garrido et al. 2016; Ichim et al. 2015; Shi et al. 2014; Suwajanakorn et al. 2014; Thies et al. 2016; Wu et al. 2016].”
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos (2016, methodology): “Shi et al. 2014 [26]: They capture face performances of-ﬂine from monocular unconstrained RGB video.”
Reconstruction of Personalized 3D Face Rigs from Monocular Video (2016, unknown): “However, generic blendshape models and identity models alone [Cao et al. 2014a; Garrido et al. 2013; Shi et al. 2014] fail to capture important person-specific expression and identity details learned by our approach.”
Photorealistic Facial Texture Inference Using Deep Neural Networks (2016, background): “As an effort to make facial digitization more deployable, monocular systems [47, 22, 8, 44, 49] that record multiple views have recently been introduced to generate seamlessly integrated texture maps for virtual avatars.”
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.”
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].”
InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image (2017, background): “Recent techniques approach monocular face reconstruction by ﬁtting active appearance models [1, 12], blendshape models [6, 13, 14], multi-linear face models [35, 36, 41, 46], or mesh geometry [19, 25, 37] to input images or videos.”
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.”
Real-time expression transfer for facial reenactment (2015, methodology): “2013] or multi-linear face model [Shi et al. 2014] to RGB video; both approaches extract fine-scale detail via lighting and albedo estimation from video, followed by shadingbased shape refinement.”