3d-pose
Datasets
All datasets matching “3d-pose”synthetic-human-expressions-poses-3d
3D Synthetic Human Poses and FACS Expressions Dataset
This is a high-fidelity synthetic dataset consisting of 10,075 pairs of 3D human character renders and detailed natural language annotations.
Dataset Structure & Generation
To ensure consistency, the dataset is generated using a single base 3D human model. The diversity of the dataset is achieved through a wide range of body poses, facial expressions, and camera angles:
Character: 1 base human model.
Camera… See the full description on the dataset page: https://huggingface.co/datasets/nadizik/synthetic-human-expressions-poses-3d.FineVideo-Phase2-3DPose
FineVideo-Phase2-3DPose — 3D Human Pose from MotionBERT
Overview
This dataset contains 3D human pose data lifted from 2D detections using MotionBERT, extracted from ~40K YouTube videos in the FineVideo dataset.
This is the output of Phase 2 (+ Phase 2.5 resampling) in the FineVideo-VLA pipeline. It contains raw 3D joint positions as NumPy arrays at 30fps, before any filtering, normalisation, or tokenisation.
Statistics
Metric
Value
Source… See the full description on the dataset page: https://huggingface.co/datasets/EmpathicRobotics/FineVideo-Phase2-3DPose.116048-Sets-3D-Hand-Pose-Gesture-Recognition-Data-Sample
116048-Sets-3D-Handpose-Dataset
Description
This dataset contains 116,048 sets of 3D handpose data, each set includes hand mask image(RGB, 24-bit), depth image(16-bit), camera intrinsic parameter file(TXT), 3D keypoints file(OBJ), mesh file(OBJ), gesture type file(TXT), keypoints demo image(JPG), and mesh demo image(JPG). The data is collected indoors, with the right hand (no handheld objects), covering both first-person and third-person perspectives, multiple… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-AI/116048-Sets-3D-Hand-Pose-Gesture-Recognition-Data-Sample.VLM-3DPOSE-DATA3d-scene-poses3d-hand-pose-generation
3D-Hand-Pose-Generation Dataset
This dataset provides a number of 3D objects and corresponding segmentation for grasps. It furthermore includes generated hand poses for the given object and segmentations,
generated using semantic-hand-pose-synthesis and GrainGrasp.
