Animal Pose Estimation
25 papers with code • 8 benchmarks • 17 datasets
Animal pose estimation is the task of identifying the pose of an animal.
( Image credit: Using DeepLabCut for 3D markerless pose estimation across species and behaviors )
Benchmarks
These leaderboards are used to track progress in Animal Pose Estimation
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Libraries
Use these libraries to find Animal Pose Estimation models and implementationsDatasets
AP-10K
Animal-Pose Dataset
Animal Kingdom
StanfordExtra
ATRW
Horse-10
AwA Pose
TriMouse-161
MBW - Zoo Dataset
Latest papers with no code
Domain adaptive pose estimation via multi-level alignment
Specifically, we first utilize image style transer to ensure that images from the source and target domains have a similar distribution.
Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
We present a method to build animatable dog avatars from monocular videos.
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and Beyond
Animal Pose Estimation and Tracking (APT) is a critical task in detecting and monitoring the keypoints of animals across a series of video frames, which is essential for understanding animal behavior.
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior
To address this, we propose an approach for estimating 2D mouse body pose from unlabelled images using a synthetically generated empirical pose prior.
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework
Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology.
Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning
Given the fact that the human brain is able to recognize animal pose without requiring large amounts of labeled data, it is only reasonable that we exploit unsupervised learning to tackle the problem of animal pose recognition from the available, unlabelled data.
Incremental Learning for Animal Pose Estimation using RBF k-DPP
Thus, in this work, we propose a novel problem of "Incremental Learning for Animal Pose Estimation".
SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation
Estimating the pose of animals can facilitate the understanding of animal motion which is fundamental in disciplines such as biomechanics, neuroscience, ethology, robotics and the entertainment industry.
Cross-Domain Adaptation for Animal Pose Estimation
Therefore, the easily available human pose dataset, which is of a much larger scale than our labeled animal dataset, provides important prior knowledge to boost up the performance on animal pose estimation.
