3D Multi-Person Pose Estimation
32 papers with code • 5 benchmarks • 4 datasets
This task aims to solve root-relative 3D multi-person pose estimation. No human bounding box and root joint coordinate groundtruth are used in testing time.
( Image credit: RootNet )
Benchmarks
These leaderboards are used to track progress in 3D Multi-Person Pose Estimation
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Libraries
Use these libraries to find 3D Multi-Person Pose Estimation models and implementations
Panoptic
MuPoTS-3D
AGORA
Store dataset
Subtasks
Latest papers with no code
Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular Videos in the Wild
3D pose estimation is an invaluable task in computer vision with various practical applications.
A Unified Multi-view Multi-person Tracking Framework
Although there is a significant development in 3D Multi-view Multi-person Tracking (3D MM-Tracking), current 3D MM-Tracking frameworks are designed separately for footprint and pose tracking.
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR
Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light.
IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation
In particular, we firstly formulate video frames as a series of instance-guided tokens and each token is in charge of predicting the 3D pose of a human instance.
Dynamic Graph Reasoning for Multi-person 3D Pose Estimation
Finally, the 3D poses are decoded according to dynamic decoding graphs for each detected person.
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes
The key challenge of this problem is to efficiently match 2D observations across multiple views.
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation
This method first uses 2. 5D pose and geometry information to infer camera-centric root depths in a forward pass, and then exploits the root depths to further improve representation learning of 2. 5D pose estimation in a backward pass.
VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation
The proposed VTP framework integrates the high performance of the transformer with volumetric representations, which can be used as a good alternative to the convolutional backbones.
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i. e., skeleton and mesh structure).
Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation
For this purpose, we build a residual-like permutation-invariant network that successfully refines potentially corrupted initial 3D poses estimated by an off-the-shelf detector.
