论文标题
利用可学习的顶点 - 佛特克斯关系来概括人类的姿势和网状重建
Leveraging the Learnable Vertex-Vertex Relationship to Generalize Human Pose and Mesh Reconstruction for In-the-Wild Scenes
论文作者
论文摘要
我们提出了Meshletemp，这是一种从单个图像中的3D人姿势和网格重建的强大方法。就人体先验编码而言，我们建议使用可学习的模板人网格，而不是先前最新方法所使用的恒定模板。所提出的可学习模板不仅反映了顶点 - vertex相互作用，还反映了人体和身体形状，能够适应各种图像。我们进行了广泛的实验，以显示我们在看不见方案的方法的普遍性。
We present MeshLeTemp, a powerful method for 3D human pose and mesh reconstruction from a single image. In terms of human body priors encoding, we propose using a learnable template human mesh instead of a constant template as utilized by previous state-of-the-art methods. The proposed learnable template reflects not only vertex-vertex interactions but also the human pose and body shape, being able to adapt to diverse images. We conduct extensive experiments to show the generalizability of our method on unseen scenarios.
