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bmartacho / UniPose

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.
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I trained to get the results in MPII #28

Open jinchengll opened 3 years ago

jinchengll commented 3 years ago

Hi, thank you for doing this great work. I clone the project and trained on the MPII dataset and obtained the following results： image

It did not reach the level you described in your paper, may I ask what caused it?

JackZh4n9 commented 3 years ago

Hi, thank you for doing this great work. I clone the project and trained on the MPII dataset and obtained the following results： image

It did not reach the level you described in your paper, may I ask what caused it?

你这是没有bbox直接跑的结果，和我的差不多，都是70左右的精度，需要输入bbox

XiyueSun commented 2 years ago

Hi, thank you for doing this great work. I clone the project and trained on the MPII dataset and obtained the following results： image It did not reach the level you described in your paper, may I ask what caused it?

你这是没有bbox直接跑的结果，和我的差不多，都是70左右的精度，需要输入bbox

您好，请问你有复现论文的结果吗？我下载了训练的模型，但是测试结果和论文中不一样。并且BBox是怎么获得呢？论文中提到Detectron，那我是需要在MPII数据集上进行检测得到检测结果BBox吗？非常感谢。

bibibabibo26 commented 2 years ago

您好！请问可以分享一下这个项目适配的MPII数据集文件吗，尤其是mpii_annotation.json，我无法调试通mpii_data.py，不太明白作者用的数据是什么样子的。拜托了！

susanxyh commented 1 year ago

Hi, thank you for doing this great work. I clone the project and trained on the MPII dataset and obtained the following results： image It did not reach the level you described in your paper, may I ask what caused it?

你这是没有bbox直接跑的结果，和我的差不多，都是70左右的精度，需要输入bbox

您好，请问你有复现论文的结果吗？我下载了训练的模型，但是测试结果和论文中不一样。并且BBox是怎么获得呢？论文中提到Detectron，那我是需要在MPII数据集上进行检测得到检测结果BBox吗？非常感谢。

您好，请问解决了吗，我也遇到了同样的问题，不太清楚bbox是怎么获得的，求回复，感谢！！