论文标题
谁把狗排除在外？循环中的3D动物重建具有期望最大化
Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
论文作者
论文摘要
我们引入了一种自动，端到端的方法，用于从单眼互联网图像中恢复狗的3D姿势和形状。狗品种之间的形状差异很大，遮挡和低质量的互联网图像使这是一个具有挑战性的问题。我们比以前的工作学到了更丰富的先前形状，这有助于对参数估计进行正规化。我们在斯坦福狗数据集上演示了结果，该数据集是20,580个狗图像的“野外”数据集，我们为其收集了2D关节和轮廓注释以进行培训和评估。为了捕获各种各样的狗，我们表明2D数据集中的自然变化足以通过预期最大化（EM）学习详细的3D。作为培训的副产品，我们生成了一个新的参数化模型（包括肢体缩放）SMBLD，我们将与新的注释数据集StanfordExtra一起发布给研究社区。
We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images. The large variation in shape between dog breeds, significant occlusion and low quality of internet images makes this a challenging problem. We learn a richer prior over shapes than previous work, which helps regularize parameter estimation. We demonstrate results on the Stanford Dog dataset, an 'in the wild' dataset of 20,580 dog images for which we have collected 2D joint and silhouette annotations to split for training and evaluation. In order to capture the large shape variety of dogs, we show that the natural variation in the 2D dataset is enough to learn a detailed 3D prior through expectation maximization (EM). As a by-product of training, we generate a new parameterized model (including limb scaling) SMBLD which we release alongside our new annotation dataset StanfordExtra to the research community.
