Weakly-supervised 3D Human Pose Estimation
18 papers with code • 2 benchmarks • 2 datasets
This task targets at 3D Human Pose Estimation with fewer 3D annotation.
Benchmarks
These leaderboards are used to track progress in Weakly-supervised 3D Human Pose Estimation
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Libraries
Use these libraries to find Weakly-supervised 3D Human Pose Estimation models and implementations
Human3.6M
MPI-INF-3DHP
Latest papers with no code
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target.
Uncertainty-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.
On 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.
Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network
Instead, exploiting multi-view information is a practical way to achieve absolute 3D human pose estimation.
TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video
Estimating 3D human poses from video is a challenging problem.
Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation
Our paper thus establishes a theoretical baseline that shows the importance of suitable projection models in weakly supervised 3D human pose estimation.
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Camera captured human pose is an outcome of several sources of variation.
Weakly-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.
On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos
As illustrated in experiments, given only a small set of annotations, our method successfully makes the model to learn new poses from unlabelled monocular videos, promoting the accuracies of the baseline model by about 10%.
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning
This alleviates the data bottleneck, which is one of the major concern for supervised methods.
