# Human Pose Estimation With Deep Learning | Two Minute Papers #106 ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=NnzzSkKKoa8 - **Дата:** 16.11.2016 - **Длительность:** 4:02 - **Просмотры:** 45,484 - **Источник:** https://ekstraktznaniy.ru/video/14752 ## Описание The paper "Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image" is available here: http://files.is.tue.mpg.de/black/papers/BogoECCV2016.pdf Welch Labs: Neural Networks Demystified - https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU Learning to See - https://www.youtube.com/playlist?list=PLiaHhY2iBX9ihLasvE8BKnS2Xg8AhY6iV Our earlier episode on optimization - https://www.youtube.com/watch?v=1ypV5ZiIbdA WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Julian Josephs, Daniel John Benton, Dave Rushton-Smith, Benjamin Kang. https://www.patreon.com/TwoMinutePapers Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_center?add_user=keeroyz Music: Dat Groove by Audionautix is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Artist: http://audionautix.com/ Thumbnail image background credits: http ## Транскрипт ### [] dear fellow Scholars this is 2minute papers with K POS estimation is an ### 3D Shape and Pose from a Single Image [0:06] interesting area of research where we typically have a few images or video footage of humans and we try to automatically extract the pose this person was taking in short the input is ### 3D Pose Estimation [0:18] mostly a 2D image and the output is typically a skeleton of the person applications of pose estimation include automatic creation of assets for computer games and digital media analyze Ing and coaching the techniques of athletes or helping computers understand what they see for the betterment of Robotics and machine learning techniques and this is just a taste the list was by no means exhaustive beyond the obvious challenge of trying to reconstruct 3D information from a simple 2D image the problem is fraught with difficulties as one has to be able to overcome the ### Our Results [0:54] ambiguity of lighting occlusions and clothing covering the body a tough problem no question about that an ideal technique would do this automatically without any user Intervention which sounds like wishful thinking or does it in this paper a previously proposed ### Overview [1:11] convolutional neural network is used to predict the position of the individual joints and curiously it turns out that we can create a faithful representation of the 3D human body from that by means of optimization we have had a previous episode on mathematical optimization you know the drill the link is available in the video description box what is remarkable here is that not only the pose but the body type is also inferred therefore the output of the process is not just a skeleton but full 3d geometry ### 2D Joint Detection [1:44] it is course geometry so don't expect a ton of details but it's 3D geometry more than what most other competing techniques can offer to ease the computational burden of this problem in ### SMPL body model [1:57] this optimization formulation healthy straints are assumed that apply to the human body such as avoiding unnatural knee and elbow bends and self intersections if we use these constraints the space in which we have to look for possible solutions shrinks considerably the results show that this ### SMPLify Objective Function [2:15] algorithm outperforms several other state-of-the-art techniques by a significant margin it is an auspicious opportunity to preserve and recreate a ### Data Term: Joint Projection Error [2:24] lot of historic events in digital form maybe even use them in computer games and I'm sure that artists will make great use of such techniques really well done the paper is extremely well written the mathematics and the optimization formulations are beautiful it was such a joy to read regarding the future I am pretty sure we are soon going to see some pose and skeleton transfer applications via machine learning the ### Depth Ambiguity [2:49] input would be a real world video with a person doing something and we could essentially edit the video and bend these characters to our will there are some exploratory Works in this area already the Disney guys for instance are doing quite well there will be lots of fun to be had indeed also make sure to check out the YouTube channel of Welch Labs who has a great introductory series for neural networks which is in my opinion Second To None he also has a new series called learning to see where he codes up a machine learning technique for a computer vision application it is about counting the number of fingers on an image really cool right the quality of these videos is through the roof the link for both of these series are available in the description box make sure to check them out thanks for watching and for your generous support and I'll see you next time oh