Unsupervised Facial Landmark Detection

8 papers with code • 5 benchmarks • 4 datasets

Facial landmark detection in the unsupervised setting popularized by [1]. The evaluation occurs in two stages: (1) Embeddings are first learned in an unsupervised manner (i.e. without labels); (2) A simple regressor is trained to regress landmarks from the unsupervised embedding.

[1] Thewlis, James, Hakan Bilen, and Andrea Vedaldi. "Unsupervised learning of object landmarks by factorized spatial embeddings." Proceedings of the IEEE International Conference on Computer Vision. 2017.

( Image credit: Unsupervised learning of object landmarks by factorized spatial embeddings )

Most implemented papers

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

zhixinshu/DeformingAutoencoders-pytorch ECCV 2018

In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner.

Unsupervised Learning of Object Landmarks through Conditional Image Generation

tomasjakab/imm NeurIPS 2018

We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision.

Self-supervised learning of a facial attribute embedding from video

oawiles/FAb-Net 21 Aug 2018

We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time.

Unsupervised learning of object landmarks by factorized spatial embeddings

alldbi/Factorized-Spatial-Embeddings ICCV 2017

Learning automatically the structure of object categories remains an important open problem in computer vision.

Unsupervised Discovery of Object Landmarks as Structural Representations

YutingZhang/lmdis-rep CVPR 2018

Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way.

Unsupervised Learning of Landmarks by Descriptor Vector Exchange

jamt9000/DVE ICCV 2019

Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.

LatentKeypointGAN: Controlling GANs via Latent Keypoints

DELTA37/LatentKeypointGAN 29 Mar 2021

Generative adversarial networks (GANs) have attained photo-realistic quality in image generation.

Unsupervised Image Representation Learning with Deep Latent Particles

taldatech/deep-latent-particles-pytorch 31 May 2022

We propose a new representation of visual data that disentangles object position from appearance.