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Found 11 papers, 5 papers with code
BlendFields: Few-Shot Example-Driven Facial Modeling
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance.
VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations
The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time.
CoNeRF: Controllable Neural Radiance Fields
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i. e. camera control).
TrajeVAE: Controllable Human Motion Generation from Trajectories
The creation of plausible and controllable 3D human motion animations is a long-standing problem that requires a manual intervention of skilled artists.
FastNeRF: High-Fidelity Neural Rendering at 200FPS
Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.
A high fidelity synthetic face framework for computer vision
Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others.
High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images
In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data.
CONFIG: Controllable Neural Face Image Generation
Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind.
HoloFace: Augmenting Human-to-Human Interactions on HoloLens
Head pose estimation is accomplished by fitting a deformable 3D model to the landmarks localized using face alignment.
Face Alignment Using K-Cluster Regression Forests With Weighted Splitting
In this work we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests and 3D Affine Pose Regression for face shape initialization.
Deep Alignment Network: A convolutional neural network for robust face alignment
Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches.
Ranked #4 on
Face Alignment
on 300W Split 2
(NME (inter-ocular) metric)
