Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals

• URL: http://arxiv.org/abs/2001.08601v1
• Date: Thu, 23 Jan 2020 15:34:11 GMT
• Title: Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals
• Authors: Siyuan Li, Semih G\"unel, Mirela Ostrek, Pavan Ramdya, Pascal Fua, and Helge Rhodin
• Abstract summary: We aim to capture the pose of neuroscience model organisms, without using any manual supervision, to study how neural circuits orchestrate behaviour. Our key contribution is the explicit and independent modeling of appearance, shape and poses in an unpaired image translation framework. We demonstrate improved pose estimation accuracy on Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm) and Danio rerio (zebrafish)
• Score: 56.65062746564091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our goal is to capture the pose of neuroscience model organisms, without using any manual supervision, to be able to study how neural circuits orchestrate behaviour. Human pose estimation attains remarkable accuracy when trained on real or simulated datasets consisting of millions of frames. However, for many applications simulated models are unrealistic and real training datasets with comprehensive annotations do not exist. We address this problem with a new sim2real domain transfer method. Our key contribution is the explicit and independent modeling of appearance, shape and poses in an unpaired image translation framework. Our model lets us train a pose estimator on the target domain by transferring readily available body keypoint locations from the source domain to generated target images. We compare our approach with existing domain transfer methods and demonstrate improved pose estimation accuracy on Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm) and Danio rerio (zebrafish), without requiring any manual annotation on the target domain and despite using simplistic off-the-shelf animal characters for simulation, or simple geometric shapes as models. Our new datasets, code, and trained models will be published to support future neuroscientific studies.

Related papers

• Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition [0.19183348587701113]
  Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning. Our experiments will demonstrate the usefulness of in-domain models and datasets for bird species recognition.
  arXiv  Detail & Related papers  (2024-04-26T08:47:28Z)
• Neuromorphic Valence and Arousal Estimation [28.793519320598865]
  We use neuromorphic data to predict emotional states from faces. We demonstrate that our trained models can still yield state-of-the-art results. In the paper we propose several alternative models to solve the task, both frame-based and video-based.
  arXiv  Detail & Related papers  (2024-01-29T11:13:18Z)
• 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)
• MagicPony: Learning Articulated 3D Animals in the Wild [81.63322697335228]
  We present a new method, dubbed MagicPony, that learns this predictor purely from in-the-wild single-view images of the object category. At its core is an implicit-explicit representation of articulated shape and appearance, combining the strengths of neural fields and meshes.
  arXiv  Detail & Related papers  (2022-11-22T18:59:31Z)
• SuperAnimal pretrained pose estimation models for behavioral analysis [42.206265576708255]
  Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. We present a series of technical innovations that enable a new method, collectively called SuperAnimal, to develop unified foundation models.
  arXiv  Detail & Related papers  (2022-03-14T18:46:57Z)
• LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies [78.17425779503047]
  We propose a novel neural implicit representation for the human body. It is fully differentiable and optimizable with disentangled shape and pose latent spaces. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
  arXiv  Detail & Related papers  (2021-11-30T04:10:57Z)
• Coarse-to-fine Animal Pose and Shape Estimation [67.39635503744395]
  We propose a coarse-to-fine approach to reconstruct 3D animal mesh from a single image. The coarse estimation stage first estimates the pose, shape and translation parameters of the SMAL model. The estimated meshes are then used as a starting point by a graph convolutional network (GCN) to predict a per-vertex deformation in the refinement stage.
  arXiv  Detail & Related papers  (2021-11-16T01:27:20Z)
• FixMyPose: Pose Correctional Captioning and Retrieval [67.20888060019028]
  We introduce a new captioning dataset named FixMyPose to address automated pose correction systems. We collect descriptions of correcting a "current" pose to look like a "target" pose. To avoid ML biases, we maintain a balance across characters with diverse demographics.
  arXiv  Detail & Related papers  (2021-04-04T21:45:44Z)
• Style-transfer GANs for bridging the domain gap in synthetic pose estimator training [8.508403388002133]
  We propose to adopt general-purpose GAN models for pixel-level image translation. The obtained models are then used either during training or inference to bridge the domain gap. Our evaluation shows a considerable improvement in model performance when compared to a model trained with the same degree of domain randomization.
  arXiv  Detail & Related papers  (2020-04-28T17:35:03Z)
• 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)

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.