Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction

03/07/2019
by   Shafeeq Elanattil, et al.
0

We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. The dataset consist of seven motion sequences of a single human model. For each motion sequence per-frame ground truth geometry and ground truth skeleton are given. The dataset also contains skinning weights of the human model. More information about the dataset can be found at: https://research.csiro.au/robotics/our-work/databases/synthetic-human-model-dataset/

READ FULL TEXT

page 7

page 8

research
10/09/2018

Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction

This paper presents a method which can track and 3D reconstruct the non-...
research
11/29/2017

Real-Time System for Human Activity Analysis

We propose a real-time human activity analysis system, where a user's ac...
research
06/22/2020

MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency

We introduce MotioNet, a deep neural network that directly reconstructs ...
research
06/17/2018

The RBO Dataset of Articulated Objects and Interactions

We present a dataset with models of 14 articulated objects commonly foun...
research
01/07/2022

A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

Markerless motion capture has become an active field of research in comp...
research
03/07/2018

A Neural Network Approach to Missing Marker Reconstruction

Optical motion capture systems have become a widely used technology in v...
research
09/10/2019

THÖR: Human-Robot Indoor Navigation Experiment and Accurate Motion Trajectories Dataset

Understanding human behavior is key for robots and intelligent systems t...

Please sign up or login with your details

Forgot password? Click here to reset