Unsupervised Human Pose Estimation
13 papers with code • 3 benchmarks • 3 datasets
Most implemented papers
Deep Feature Factorization For Concept Discovery
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images.
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Large intra-class variation is the result of changes in multiple object characteristics.
Motion-supervised Co-Part Segmentation
To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.
Unsupervised Human Pose Estimation through Transforming Shape Templates
Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking.
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an important open problem in computer vision.
Unsupervised Discovery of Object Landmarks as Structural Representations
Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way.
SCOPS: Self-Supervised Co-Part Segmentation
Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations.
LatentKeypointGAN: Controlling GANs via Latent Keypoints
Generative adversarial networks (GANs) have attained photo-realistic quality in image generation.
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing.
Self-Supervised Keypoint Discovery in Behavioral Videos
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos.