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Found 7 papers, 3 papers with code
Implicit Shape and Appearance Priors for Few-Shot Full Head Reconstruction
Recent advancements in learning techniques that employ coordinate-based neural representations have yielded remarkable results in multi-view 3D reconstruction tasks.
InstantAvatar: Efficient 3D Head Reconstruction via Surface Rendering
In order to speed up the reconstruction process, we propose a system that combines, for the first time, a voxel-grid neural field representation with a surface renderer.
Network-Free, Unsupervised Semantic Segmentation With Synthetic Images
We derive a method that yields highly accurate semantic segmentation maps without the use of any additional neural network, layers, manually annotated training data, or supervised training.
Photorealistic Facial Wrinkles Removal
In this work, we revisit a two-stage approach for retouching facial wrinkles and obtain results with unprecedented realism.
SIRA: Relightable Avatars from a Single Image
Our key ingredients are two data-driven statistical models based on neural fields that resolve the ambiguities of single-view 3D surface reconstruction and appearance factorization.
H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction
In this paper, we tackle these limitations for the specific problem of few-shot full 3D head reconstruction, by endowing coordinate-based representations with a probabilistic shape prior that enables faster convergence and better generalization when using few input images (down to three).
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM).
