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Found 6 papers, 4 papers with code
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions
Namely, we extend NeuS, a state-of-the-art neural implicit function formulation, to represent multiple objects of a class (human heads in our case) simultaneously.
Neural Head Reenactment with Latent Pose Descriptors
We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image.
Textured Neural Avatars
In particular, our system estimates an explicit two-dimensional texture map of the model surface.
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
In order to create a personalized talking head model, these works require training on a large dataset of images of a single person.
Learnable Triangulation of Human Pose
We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views.
Ranked #3 on
3D Human Pose Estimation
on Panoptic
(using extra training data)
Deep Neural Networks with Box Convolutions
Box filters computed using integral images have been part of the computer vision toolset for a long time.
