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There were twelve competitions in 2004, 58 more from 2005 to 2006, over 100 in 2008, and over 1150 in 2018. This revival of competition sparked a new wave of organized speedcubing events, which include regular national and international competitions. So twenty years after the first world championship, they orchestrated a second championship in Toronto in 2003 and another smaller competition in the Netherlands later that same year. People prominent in this online community, such as Ron van Bruchem, Tyson Mao, Chris Hardwick, and Ton Dennenbroek, eventually wanted to meet in person and compete. Simultaneously spreading effective speedsolving methods and teaching people new to the cube to solve it for the first time, these sites brought in a new generation of cubers, created a growing international online community, and raised the profile of the art. The height of the Rubik's Cube craze began to fade away after 1983, but with the advent of the Internet, sites relating to speedcubing began to surface. Other notable attendees include Jessica Fridrich and Lars Petrus, two people who would later be influential in the development of solving methods and the speedcubing community. 19 people competed in the event and the American Minh Thai won with a single solve time of 22.95 seconds and was considered as the First World Record of the Rubik's Cube. On June 5, 1982, the first world championship was held in Budapest, Hungary. Later, Ernő Rubik partnered with Ideal Toy company to widespread the international interest in the cube which began in 1979, which soon developed into a global craze. The Rubik's Cube was invented in 1974 by Hungarian professor of architecture, Ernő Rubik (Born 13 July 1944). 2.5 Fewest Moves Challenge (FMC) methods.To evaluate camera-centric (i.e., camera coordinates) 3D multi-person pose estimation: python eval_mupots_pck_abs.pyĪfter running the above code, the following PCK_abs (camera-centric) value is expected, which matches the number reported in Table 3, PCK_abs = 48 (percentage) in the paper. To evaluate the person-centric 3D multi-person pose estimation: python eval_mupots_pck.pyĪfter running the above code, the following PCK (person-centric, pelvis-based origin) value is expected, which matches the number reported in Table 3, PCK = 89 (percentage) in the paper. Please note that python calculate_mupots_btmup.py is going to take a while (30-40 minutes depending on your machine). We split the whole pipeline into several separate steps to make it more clear for the users. Run evaluation on MuPoTS dataset with estimated 2D joints as input Evaluation instructions to reproduce the results (PCK and PCK_abs) are provided in the next section.
The following table is similar to Table 3 in the main paper, where the quantitative evaluations on MuPoTS-3D dataset are provided (best performance in bold). Usage MuPoTS dataset evaluation 3D Multi-Person Pose Estimation Evaluation on MuPoTS Dataset |- other python code, LICENSE, and README files |- mupots <- the downloaded processed human keypoint files |- MultiPersonTestSet <- the newly added MuPoTS eval set |- ckpts <- the downloaded pre-trained Models Now you should see the following directory structure. Unzip it and move the folder MultiPersonTestSet to the root directory of the project to perform evaluation on MuPoTS test set.
#Multiperson rubiks cube timer download
After the download is complete, a MultiPersonTestSet.zip is avaiable, ~5.6 GB. You need to download the mupots-3d-eval.zip file, unzip it, and run get_mupots-3d.sh to download the dataset. MuPoTS eval set is needed to perform evaluation as the results reported in Table 3 in the main paper, which is available on the MuPoTS dataset website.
#Multiperson rubiks cube timer zip file
Models and Testing Data Pre-trained Modelsĭownload the pre-trained model and processed human keypoint files here, and unzip the downloaded zip file to this project's root directory, two folders are expected to see after doing that (i.e.
#Multiperson rubiks cube timer install
Install dependencies pip install - r requirements.txtīuild the Fast Gaussian Map tool: cd lib/fastgaus For example, command to use on Linux with CUDA 11.0 is like: conda install pytorch torchvision cudatoolkit=11.0 -c pytorch
#Multiperson rubiks cube timer driver
Install the latest version of pytorch (tested on pytorch 1.5 - 1.7) based on your OS and GPU driver installed following install pytorch.
#Multiperson rubiks cube timer code
evaluation code of PCK (person-centric) and PCK_abs (camera-centric), and pre-trained model for MuPoTS dataset tested and releasedĬreate an enviroment.Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up NetworksĬomputer Vision and Pattern Recognition, CVPR 2021.