Face Parsing
15 papers with code • 4 benchmarks • 4 datasets
Classify pixels of a face image into different classes based on a given bounding box.
Helen
CelebAMask-HQ
LaPa
iBugMask
Most implemented papers
RoI Tanh-polar Transformer Network for Face Parsing in the Wild
Face parsing aims to predict pixel-wise labels for facial components of a target face in an image.
E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion
wty-ustc/e2style
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This paper studies the problem of StyleGAN inversion, which plays an essential role in enabling the pretrained StyleGAN to be used for real image editing tasks.
Weakly-supervised Caricature Face Parsing through Domain Adaptation
ZJULearning/CariFaceParsing
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However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive.
Face Parsing with RoI Tanh-Warping
aod321/Face-parsing-via-tanh-warping
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CVPR 2019
It uses hierarchical local based method for inner facial components and global methods for outer facial components.
End-to-End Face Parsing via Interlinked Convolutional Neural Networks
aod321/STN-iCNN
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Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.
Edge-aware Graph Representation Learning and Reasoning for Face Parsing
Specifically, we encode a facial image onto a global graph representation where a collection of pixels ("regions") with similar features are projected to each vertex.
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
chaofengc/PSFRGAN
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CVPR 2021
Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs.
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
High Resolution Face Editing with Masked GAN Latent Code Optimization
The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i. e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially--selective treatment of local image areas.
Fully Transformer Networks for Semantic Image Segmentation
BR-IDL/PaddleViT
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Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
