AHP: Amodal Human Perception dataset

A large-scale integrated human dataset

The AHP dataset consists of 56,599 images in total which are collected from several large-scale instance segmentation and detection datasets, including COCO, VOC (w/ SBD), LIP, Objects365 and OpenImages. Each image is annotated with a pixel-level segmentation mask of a single integrated human.

The dataset is initially proposed to solve the task of human de-occlusion. We believe our dataset can be leveraged in other human associated tasks and inspires more creative ideas.

Data Splits

  • Train: Totally 56,302 images with annotations of integrated humans.
  • Valid: Totally 297 images of synthesized occlusion cases.
  • Test: Totally 56 images of artificial occlusion cases.

Since the AHP dataset mainly targets at human de-occlusion. When training, the occlusion cases will be synthesized by occluding the integrated humans with other instances, e.g. COCO. We have no restrictions on the synthesis algorithms. The validation set contains 891 images with the simplest synthesis technique of pasting instances onto humans. And the validation set is augmented from 297 images each with three different occluders. Moreover, the test set contains 56 artificial occlusion cases which can muddle through human visual systems. For more details, please refer to our paper.


Download the AHP images and annotations from Google Drive or BaiduYun(password: trbr).


Human De-occlusion

Human de-occluson aims at the problem of estimating the invisible masks and content for humans. The synthesized occlusion cases based on the AHP dataset have three advantages:
  • the number of humans in AHP is comparatively larger than other amodal perception datasets.
  • the synthesized occlusion cases own amodal segmentation and visual content ground-truths from real-world scenes;
  • the occlusion cases with expected occlusion distribution can be readily obtained.



Human De-occlusion: Invisible Perception and Recovery for Humans
Qiang Zhou, Shiyin Wang, Yitong Wang, Zilong Huang, and Xinggang Wang
Computer Vision and Pattern Recognition (CVPR) 2021
[PDF] [Supp] [BibTex]

author = { Q. Zhou and S. Wang and Y. Wang and Z. Huang and X. Wang},
title = {Human De-occlusion: Invisible Perception and Recovery for Humans},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
Please cite the relevant papers in your publications if AHP helps your research.


The annotations in this dataset belong to the ByteDance Ltd. are licensed under a Creative Commons Attribution 4.0 License. The data is released for non-commercial research purpose only.

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