A Horse with no Labels: Self-Supervised Horse Pose Estimation from Unlabelled Images and Synthetic Prior

• URL: http://arxiv.org/abs/2308.03411v1
• Date: Mon, 7 Aug 2023 09:02:26 GMT
• Title: A Horse with no Labels: Self-Supervised Horse Pose Estimation from Unlabelled Images and Synthetic Prior
• Authors: Jose Sosa and David Hogg
• Abstract summary: We propose a simple yet effective self-supervised method for estimating animal pose. We train our method with unlabelled images of horses mainly collected for YouTube videos and a prior consisting of 2D synthetic poses. We demonstrate that it is possible to learn accurate animal poses even with as few assumptions as unlabelled images and a small set of 2D poses generated from synthetic data.
• Score: 1.0878040851638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Obtaining labelled data to train deep learning methods for estimating animal pose is challenging. Recently, synthetic data has been widely used for pose estimation tasks, but most methods still rely on supervised learning paradigms utilising synthetic images and labels. Can training be fully unsupervised? Is a tiny synthetic dataset sufficient? What are the minimum assumptions that we could make for estimating animal pose? Our proposal addresses these questions through a simple yet effective self-supervised method that only assumes the availability of unlabelled images and a small set of synthetic 2D poses. We completely remove the need for any 3D or 2D pose annotations (or complex 3D animal models), and surprisingly our approach can still learn accurate 3D and 2D poses simultaneously. We train our method with unlabelled images of horses mainly collected for YouTube videos and a prior consisting of 2D synthetic poses. The latter is three times smaller than the number of images needed for training. We test our method on a challenging set of horse images and evaluate the predicted 3D and 2D poses. We demonstrate that it is possible to learn accurate animal poses even with as few assumptions as unlabelled images and a small set of 2D poses generated from synthetic data. Given the minimum requirements and the abundance of unlabelled data, our method could be easily deployed to different animals.

Related papers

• Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images [21.718426435322925]
  We introduce the first method using synthetic data generation and disentanglement to learn to regress 3D shape and pose. Our method, Dessie, surpasses existing 3D horse reconstruction methods and generalizes to other large animals like zebras, cows, and deer.
  arXiv  Detail & Related papers  (2024-10-04T13:52:22Z)
• ZebraPose: Zebra Detection and Pose Estimation using only Synthetic Data [0.2302001830524133]
  We use synthetic data generated with a 3D simulator to obtain the first synthetic dataset that can be used for both detection and 2D pose estimation of zebras. We extensively train and benchmark our detection and 2D pose estimation models on multiple real-world and synthetic datasets. These experiments show how the models trained from scratch and only with synthetic data can consistently generalize to real-world images of zebras.
  arXiv  Detail & Related papers  (2024-08-20T13:28:37Z)
• MPL: Lifting 3D Human Pose from Multi-view 2D Poses [75.26416079541723]
  We propose combining 2D pose estimation, for which large and rich training datasets exist, and 2D-to-3D pose lifting, using a transformer-based network. Our experiments demonstrate decreases up to 45% in MPJPE errors compared to the 3D pose obtained by triangulating the 2D poses.
  arXiv  Detail & Related papers  (2024-08-20T12:55:14Z)
• Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape [32.11280929126699]
  We propose Animal3D, the first comprehensive dataset for mammal animal 3D pose and shape estimation. 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. Based on the Animal3D dataset, we benchmark representative shape and pose estimation models at: (1) supervised learning from only the Animal3D data, (2) synthetic to real transfer from synthetically generated images, and (3) fine-tuning human pose and shape estimation models.
  arXiv  Detail & Related papers  (2023-08-22T18:57:07Z)
• Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior [0.7499722271664145]
  We propose an approach for estimating 2D mouse body pose from unlabelled images using a synthetically generated empirical pose prior. We adapt this method to the limb structure of the mouse and generate the empirical prior of 2D poses from a synthetic 3D mouse model. In experiments on a new mouse video dataset, we evaluate the performance of the approach by comparing pose predictions to a manually obtained ground truth.
  arXiv  Detail & Related papers  (2023-07-25T09:31:55Z)
• Learning 3D-aware Image Synthesis with Unknown Pose Distribution [68.62476998646866]
  Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set. This work proposes PoF3D that frees generative radiance fields from the requirements of 3D pose priors.
  arXiv  Detail & Related papers  (2023-01-18T18:47:46Z)
• Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data [18.06492246414256]
  We present a data efficient strategy for pose estimation in quadrupeds that requires only a small amount of real images from the target animal. It is confirmed that fine-tuning a backbone network with pretrained weights on generic image datasets such as ImageNet can mitigate the high demand for target animal pose data. We introduce a prior-aware synthetic animal data generation pipeline called PASyn to augment the animal pose data essential for robust pose estimation.
  arXiv  Detail & Related papers  (2022-08-30T01:17:50Z)
• VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data [69.64723752430244]
  We introduce VirtualPose, a two-stage learning framework to exploit the hidden "free lunch" specific to this task. The first stage transforms images to abstract geometry representations (AGR), and then the second maps them to 3D poses. It addresses the generalization issue from two aspects: (1) the first stage can be trained on diverse 2D datasets to reduce the risk of over-fitting to limited appearance; (2) the second stage can be trained on diverse AGR synthesized from a large number of virtual cameras and poses.
  arXiv  Detail & Related papers  (2022-07-20T14:47:28Z)
• LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery [72.3681707384754]
  We propose a practical problem setting to estimate 3D pose and shape of animals given only a few in-the-wild images of a particular animal species. We do not assume any form of 2D or 3D ground-truth annotations, nor do we leverage any multi-view or temporal information. Following these insights, we propose LASSIE, a novel optimization framework which discovers 3D parts in a self-supervised manner.
  arXiv  Detail & Related papers  (2022-07-07T17:00:07Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)
• Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations [73.11883464562895]
  We propose a new architecture that facilitates unsupervised, or lightly supervised, learning. We demonstrate the method by learning 3D human pose and shape from un-paired and un-annotated images. While we present results for modeling humans, our formulation is general and can be applied to other vision problems.
  arXiv  Detail & Related papers  (2020-01-06T14:54:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.