The number of research papers on 3D pose estimation, and in particular in
un-controlled settings has dramatically increased recently. Most papers demonstrate
performance qualitatively, showing images and the corresponding 3D
body model or stick gure side by side. Quantitative evaluation is limited
to indoor datasets such as H3.6M or Human-Eva, or outdoor datasets with
recording volume like MuPoTs-3D. This changed with 3DPW (https://virtualhumans.mpi-inf.mpg.de/3DPW/), which constitutes the
only dataset with accurate reference 3D poses in natural scenes (e.g., people
shopping in the city, having coffee, or doing sports recorded with a moving
hand-held camera). While researchers started using this dataset, the evaluation,
protocols and sequences differ from paper to paper. This makes comparison of
methods difficult. The purpose of this workshop and challenge is to standardize
protocols and metrics so that researchers compare their methods in a consistent
manner in future publications, ultimately to advance the state of the art in 3D
human pose estimation in the wild. The tentative program consists in invited
talks by Jitendra Malik and Kostas Daniilidis and the 3 top
performing methods of the 3DPW-Challenge, and a poster session for workshop
papers and invited relevant papers from the main conference.
Notification of Acceptance
Camera Ready Submission
All deadlines are 5 PM Pacific time.
Paper submissions should follow the exact same guidelines of ECCV, 14 pages plus references.
Submissions can be uploaded to the CMT: https://cmt3.research.microsoft.com/3DPW/
If you do not have one already, create an account for cmt3 and login.
In case you are not directed to 3DPW submission, type 3DPW in the search box to find it.
Accepted papers will be published in the proceedings of ECCV workshops.
Welcome and introduction
Winner of Challenge 1
Winner of Challenge 2
Invited Speaker 3
Winner of Challenge 3
Information about the challenge and evaluation protocols will be posted here. Below, you can find the video of the ECCV'18 paper associated with the 3DPW dataset: