Tag: 3dmm

• HeadNeRF A Real-time NeRF-based Parametric Head Model (27 Sep 2022)
  

  HeadNeRF: A Real-time NeRF-based Parametric Head Model provides a parametric head model which could generates photorealistic face images conditioned on identity, expression, head pose and appearance (lighting). Compared with traditional mesh and texture, it provides higher fidelity, inherently differetiable and doesn’t required a 3D dataset; compared with GAN, it provides rendering at different head pose with accurate 3D information. This is achived with NeRF. In addition, it could render in real time (5ms) with a model GPU.

• Landmark Detection for Animal Face and 3D Reconstructions (01 Jun 2021)
  

  This is my literature survey of landmark detection and 3D reconstruction for cat and dog’s face.

• 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.

• 3D Morphorble Model (24 Apr 2021)
  

  This notebooks describes the models which could represent animatable 3D face mesh, which is usually referred 3D morphorble model (3DMM).

• Face Reconstruction in CVPR 2019 (18 Jul 2019)
  

  

• Face Detection, Landmark Detection in CVPR 2019 (17 Jul 2019)