Tag: in-the-wild

• GANFIT Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction (01 May 2021)
  

  This is my reading note for GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction. The code is available in GANFit. GANFit reconstructs high quality texture and geometry from a single image with precise identity recovery. To do this, it utilizes GANs to train a very powerful generator of facial texture in UV space. Then, it revisits the original 3D Morphable Models (3DMMs) fitting approaches making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. It optimizes the parameters with the supervision of pretrained deep identity features through our end-to-end differentiable framework.

• Avatarme Realistically renderable 3d facial reconstruction in-The-wild (28 Apr 2021)
  

  This is reading note for Avatarme: Realistically renderable 3d facial reconstruction ‘in-The-wild’, which was published in CVPR 2020 and code is available in github. Avatarme aims to reconstruct photorealistic 3D faces from a single “in-the-wild” image with an increasing level of detail. It could generate 4K by 6K-resolution 3D faces from a single low-resolution image that, for the first time, bridges the uncanny valley.