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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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where is the datasets about mpii? #16

Closed YuQi9797 closed 3 years ago

YuQi9797 commented 3 years ago 
img_path = self.images_dir + variable['img_paths'] 
segmented = cv2.imread(self.labels_dir + "segmented/" + variable['img_paths'][:-4] + '.png')
bbox = np.load(self.labels_dir + "BBOX/" + variable['img_paths'][:-4] + '.npy')

points = torch.Tensor(variable['joint_self'])
center = torch.Tensor(variable['objpos'])  # 594, 257
scale = variable['scale_provided']  # 3.021

Does anyone know where the corresponding files are downloaded? bbox，segmented?

XiyueSun commented 2 years ago

Hi，have you got the bbox files?