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Dense pose

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Dense pose

  1. 1. Dense Pose: Dense Human Pose Estimation In The Wild Pre by Guangrui Li
  2. 2. 目标 • 1,配合新颖的标注策略,建立了一个庞大的,RGB 图像和 surface-based representation对应的人体的数据集; • 2,基于上一条提到的数据集,分别用FCN,region-based system 做了实验,实验证明,后者更棒; • 3,在尝试了多种利用数据集的方式,并提出了最行之有效的利 用方式。
  3. 3. COCO-DensePose Dataset task 1: 分割为头、躯干等几个部位 task 2:对于每一个部位,进行点的标注,在点的标注上,针对不同复杂程度的部位有着不同的 策略,详情应该还需要翻阅其引用的SMPL模型
  4. 4. Novel annotation method • 标注准确率: • 由于其可以渲染图像,因此直 接选取标注点的一部分,与渲 染模型中坐标真实值计算 geodesic distance。
  5. 5. Evaluation Method • 1, pointwise the Ratio of Correct Point (RCP) correspondences, where a correspondence is declared correct if the geodesic distance is below a certain threshold 2, Per-Instance geodesic point similarity:
  6. 6. Learning Dense Human Pose Estimation Fully-convolutional dense pose regression 1,classifier:分类为某个部位 [cross-entropy loss] 2,regressor:定位point坐标 [smooth L1 loss] 弊端: 这样一个网络承担了这么多任务(一个分类器和24和 regressor)的情况下,很难再保证scale-invariance
  7. 7. Learning Dense Human Pose Estimation • Region based system we use a cascade of region proposal generation and feature pooling, followed by a fully-convolutional network that densely predicts discrete part labels and continuous surface coordinates
  8. 8. Distillation-based ground-truth interpolation • 对于每个训练样本,只提供标注点新的的一部分。 • 除此之外,利用了蒸馏的思路 • 首先,训练teacher net,这个net的目标是来重建没有提供标注的 点, • 然后将该网络与student net一起训练,这样最终获得了更好的结 果。

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