3D face reconstruction is the task of reconstructing a face from an image into a 3D form (or mesh).
( Image credit: Deep3DFaceReconstruction )
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Moreover, the identity and expression representations are entangled in these models, which hurdles many facial editing applications.
Recent convolutional neural network (CNN) based method has shown excellent performance by learning mapping relation using pairs of low-resolution (LR) and high-resolution (HR) facial images.
The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage.
The model consists of two main branches: i) a face video deblurring sub-network based on an encoder-decoder architecture, and ii) a 3D face reconstruction and rendering branch for predicting 3D priors of salient facial structures and identity knowledge.
To tackle this problem, we propose a semi-supervised monocular reconstruction method, which jointly optimizes a shape-preserved domain-transfer CycleGAN and a shape estimation network.
In recent decades, 3D morphable model (3DMM) has been commonly used in image-based photorealistic 3D face reconstruction.
By improving the nonlinear 3D morphable model in both learning objective and network architecture, we present a model which is superior in capturing higher level of details than the linear or its precedent nonlinear counterparts.
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
SOTA for 3D Face Reconstruction on Florence (Average 3D Error metric )