3D Pose Estimation
132 papers with code • 6 benchmarks • 29 datasets
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Use these libraries to find 3D Pose Estimation models and implementationsLatest papers
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding
We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors.
EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams
In response to the existing limitations, this paper 1) introduces a new problem, i. e., 3D human motion capture from an egocentric monocular event camera with a fisheye lens, and 2) proposes the first approach to it called EventEgo3D (EE3D).
SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation
Unlike current state-of-the-art fully-supervised methods, our approach does not require any 2d or 3d ground-truth poses and uses only the multi-view input images from a calibrated camera setup and 2d pseudo poses generated from an off-the-shelf 2d human pose estimator.
Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation
In this study we focus on the new task of multi-hypothesis motion estimation.
Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting
We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network.
3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera
3D hand tracking from a monocular video is a very challenging problem due to hand interactions, occlusions, left-right hand ambiguity, and fast motion.
Mask as Supervision: Leveraging Unified Mask Information for Unsupervised 3D Pose Estimation
Automatic estimation of 3D human pose from monocular RGB images is a challenging and unsolved problem in computer vision.
MoEmo Vision Transformer: Integrating Cross-Attention and Movement Vectors in 3D Pose Estimation for HRI Emotion Detection
In the current effort, we introduce 1) MoEmo (Motion to Emotion), a cross-attention vision transformer (ViT) for human emotion detection within robotics systems based on 3D human pose estimations across various contexts, and 2) a data set that offers full-body videos of human movement and corresponding emotion labels based on human gestures and environmental contexts.
DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation
Specifically, for an initial hand-object pose estimated by a base network, we forward it to a physics simulator to evaluate its stability.
FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions
To facilitate the development of 3D pose estimation, we present FreeMan, the first large-scale, multi-view dataset collected under the real-world conditions.