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LIP (Look into Person)

Semantic Understanding of People from Images

LIP (Look into Person)

We present a new large-scale dataset focusing on semantic understanding of person. The dataset is an order of magnitude larger and more challenge than similar previous attempts that contains 50,000 images with elaborated pixel-wise annotations with 19 semantic human part labels and 2D human poses with 16 key points. The images collected from the real-world scenarios contain human appearing with challenging poses and views, heavily occlusions, various appearances and low-resolutions. This challenge and benchmark are fully supported by the Human-Cyber-Physical Intelligence Integration Lab of Sun Yat-sen University.

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Sun Yat-sen University
http://www.sysu.edu.cn/en/index.htm
Task
3D Semantic Segmentation
Annotation Types
Semantic Segmentation
50000
Items
35
Classes
50000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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