Total Moving Face Reconstruction

By Supasorn Suwajanakorn, Ira Kemelmacher-Shlizerman, S. M. Seitz. 2014

193 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

Category-specific object reconstruction from a single image (2014, background): “[23, 32] which aims to learn morphable models for faces from 2D images, but we focus on richer shapes in unconstrained settings, at the expense of lower resolution reconstructions.”
Gibson Env: Real-World Perception for Embodied Agents (2018, background): “View Synthesis and Image-Based Rendering: Rendering novel views of objects and scenes is one of the classic problems in vision and graphics [80, 84, 91, 23, 60].”
Extreme 3D Face Reconstruction: Seeing Through Occlusions (2017, methodology): “Before reviewing single view 3D reconstruction methods, we note that some methods were developed for multi view reconstruction [18, 27, 41, 42, 46].”
Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set (2019, methodology): “Our method is also related to traditional methods working on unconstrained photo collections [27, 47, 40, 41].”
Extreme 3D Face Reconstruction: Looking Past Occlusions (2017, methodology): “h resolution images are available on our project page. 1 2. Related work Before reviewing single view 3D reconstruction methods, we note that some methods were developed for multi view reconstruction [19,28,42,43,47]. With multiple views, better guarantees can be made on reconstruction accuracy and ”
Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision (2019, background): “Until very recently, this has been the dominant paradigm [2, 30, 11].”
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network (2016, background): “Before listing them, it is important to mention recent multi image methods which use image sets for reconstruction (e.g., [23, 28, 32, 33, 36]).”
InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image (2017, background): “We focus on single-view techniques and do not further discuss multi-view or multi-image approaches [17, 37, 43].”
Learning a model of facial shape and expression from 4D scans (2017, methodology): “They animate the face using 3D flow, warping, and a texture synthesis approach driven by a video sequence [Suwajanakorn et al. 2014, 2015].”
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).”
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction (2017, background): “For further work on general template-based mesh tracking, please refer to [25, 26, 51, 52, 31].”
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.”
Learning Detailed Face Reconstruction from a Single Image (2016, methodology): “Various methods mitigate this uncertainty by using additional data such as a large photo collection of the same subject [36, 35, 21, 28, 33], continuous video frames [44, 40, 5, 11] or a rough depth map [44, 18].”
InverseFaceNet: Deep Monocular Inverse Face Rendering (2017, background): “We focus on single-view techniques and do not further discuss multi-view or multi-image approaches [23, 29, 44, 48, 57].”
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.”
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression (2017, methodology): “Given training data like the one produced by [10, 23], then we believe that our method has the capacity to learn finer facial details, too.”