We will release our Neurips 2020 ChaLearn LaP 3D+Texture Garment Reconstruction Challenge soon (13th May). Check it here!
5th April: We have just released our ECCV 2020 ChaLearn Looking at People Fair Face Recognition Challenge webpage. Check it out!
Looking at People (LAP) is a challenging area of research that deals with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others. Any scenario where the visual or multi-modal analysis of people takes a is of interest within the field of Looking at People.
Because of huge space of human configurations, human analysis is a difficult Computer Vision and Machine Learning problem that involves dealing with several distortions: illumination changes, partial occlusions, changes in the point of view, rigid and elastic deformations, or high inter and intra-class variability, just to mention a few. Even with the high difficulty of the problem, modern Computer Vision and Machine Learning techniques deserve further attention.
Several subareas of LAP have been recently defined, such as Affective Computing, Social Signal Processing, Human Behavior Analysis, or Social Robotics. The effort involved in this area of research will be compensated by its potential applications: TV production, home entertainment (multimedia content analysis), education purposes, sociology research, surveillance and security, improved quality life by means of monitoring or automatic artificial assistance, etc.
This page summarizes all previous, current, and upcoming events organized by the ChaLearn LAP team, including international workshops, competitions, special issues, books and other publications related to ChaLearn LAP events. Associated databases and their state-of-the-art results are also available.
We would like to thank all the hundreds of participants that joined our events and that considerably help to advance in both theory and practice of LAP. We thank our sponsors, including Microsoft Research, Google, NVIDIA Corportation, Disney Research, Facebook, and Amazon, among others.
ChaLearn LAP datasets license
The ownership of ChaLearn LAP datasets, if no other information is provided at each specific dataset webpage, belongs to their authors. They are licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
ChaLearn LAP datasets, if no other information is provided at each specific dataset webpage, are presented "AS IS". By downloading and using them, you acknowledge they may contain errors, and take full responsibility on any potential risk or damage. To the fullest extent provided by law, in no event will we, our affiliates, or our licensors, service providers, employees, agents, officers, or directors be liable for damages of any kind, under any legal theory, arising out of or in connection with the developer’s use, or inability to use, the services, datasets, any content on the services or such other services, including any direct, indirect, special, incidental, consequential, or punitive damages, including but not limited to, personal injury, pain and suffering, emotional distress, loss of revenue, loss of profits, loss of business or anticipated savings, loss of use, loss of goodwill, loss of data, and whether caused by tort (including negligence), breach of contract, or otherwise, even if foreseeable.