Papers Weakly-supervised 3D Human Pose Estimation
“Weakly-supervised 3D Human Pose Estimation” 태그가 달린 논문 29편 · 필터 해제
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation
3D human pose data collected in controlled laboratory settings present challenges for pose estimators that generalize across diverse scenarios. To address this, domain generalization is employed. Current methodologies in…
3D Human Pose EstimationDomain GeneralizationPose EstimationWeakly-supervised 3D Human Pose EstimationAutomatic Labeling of Parkinson’s Disease Gait Videos with Weak Supervision
Motor dysfunction in Parkinson’s Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical asses…
3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose EstimationNon-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each …
3D Human Pose EstimationDecoderPose EstimationRelation+2Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard …
3D Human Pose EstimationDomain AdaptationMonocular 3D Human Pose EstimationPose Estimation+3On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation
Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. However, as the acquisition of ground-truth 3D labels is labor intensive and time consuming, recent a…
3D Human Pose Estimation3D Pose EstimationFormPose Estimation+2AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation
This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mai…
3D Human Pose EstimationDiversityMotion GenerationPose Estimation+1Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition
Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dance genre). We propose a Hierarchical Dan…
3D Pose EstimationPose EstimationUnsupervised 3D Human Pose EstimationVideo Recognition+1Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry
We present a self-supervised learning algorithm for 3D human pose estimation of a single person based on a multiple-view camera system and 2D body pose estimates for each view. To train our model, represented by a deep n…
3D Human Pose EstimationPose EstimationSelf-Supervised LearningWeakly-supervised 3D Human Pose Estimation+1MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perform this task with high precision and mi…
3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose EstimationHeuristic Weakly Supervised 3D Human Pose Estimation
Monocular 3D human pose estimation from RGB images has attracted significant attention in recent years. However, recent models depend on supervised training with 3D pose ground truth data or known pose priors for their t…
3D Human Pose Estimation3D Pose EstimationMonocular 3D Human Pose EstimationPose Estimation+1Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network
Although monocular 3D human pose estimation methods have made significant progress, it is far from being solved due to the inherent depth ambiguity. Instead, exploiting multi-view information is a practical way to achiev…
3D Human Pose EstimationMonocular 3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose Estimation+1TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video
Estimating 3D human poses from video is a challenging problem. The lack of 3D human pose annotations is a major obstacle for supervised training and for generalization to unseen datasets. In this work, we address this pr…
3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose EstimationPoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation
Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a new …
3D Human Pose EstimationData AugmentationDiversityMonocular 3D Human Pose Estimation+2CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild
Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately. Unfortunately, for many human activities (\eg outdoor spo…
3D Human Pose EstimationMonocular 3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose EstimationError Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation
The current state-of-the-art in monocular 3D human pose estimation is heavily influenced by weakly supervised methods. These allow 2D labels to be used to learn effective 3D human pose recovery either directly from image…
3D Human Pose EstimationMonocular 3D Human Pose EstimationPose EstimationPosition+1Cascaded deep monocular 3D human pose estimation with evolutionary training data
End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data. This paper proposes a nove…
3D Human Pose EstimationData AugmentationMonocular 3D Human Pose EstimationPose Estimation+3Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, such as shape and appearance, that may be…
3D Human Pose Estimation3D Pose EstimationDisentanglementImage Generation+4Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild
One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses. In this paper, we address this challenge by …
3D Human Pose EstimationMonocular 3D Human Pose EstimationPose EstimationWeakly-superavised 3D Human Pose Estimation+1TexturePose: Supervising Human Mesh Estimation with Texture Consistency
This work addresses the problem of model-based human pose estimation. Recent approaches have made significant progress towards regressing the parameters of parametric human body models directly from images. Because of th…
Pose EstimationWeakly-supervised 3D Human Pose EstimationOn Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos
The premise of training an accurate 3D human pose estimation network is the possession of huge amount of richly annotated training data. Nonetheless, manually obtaining rich and accurate annotations is, even not impossib…
3D Human Pose EstimationPose EstimationWeakly-supervised 3D Human Pose Estimation