Sue Hyun Park, August 24.
<aside> 🔗 We have reposted this blog on our Medium publication. Read this on Medium.
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In the Expressive 3D Human Pose and Shape Estimation series, we introduce our novel methods to compose 3D human pose and shape information from a single RGB image.
In part 1, we describe our work on 3D human pose estimation. We focus on localizing joints of human bodies and hands in the 3D space in order to lay the cornerstone for vital 2D-to-3D conversion techniques. These include accurately measuring a subject's relative distance from the camera for multi-person scenarios and depicting the complex sequence of interacting hands.
In part 2, we describe how our work extends to estimating the human mesh, a widely used data format for 3D human shape representation. In the end, we simultaneously localize joints and mesh vertices of all human parts, including body, hands, and face, for more rich and comprehensive 3D figures. Reaching this state, we discuss how advanced 3D human pose and shape estimation methods can be applied to industries to lead the advent of new communication technologies.
<aside> ⬅️ If you would like to review the start of our discussion, read part 1.
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Among point cloud, voxel, and mesh, mesh is the most popular 3D data format for 3D human shape representation. This is because connecting the mesh vertices represents the surface as a set of polygons and is adequate for the human body frame. Above all, the mesh format is known for its compactness and usability.
Different representations of 3D data: point cloud, voxel, and mesh in order (Source: Nabil MADALI)
Using the mesh format, this blog, part 2, describes our 3D human pose and shape estimation methods that focus on delivering:
3D human pose and mesh estimation
Expressive 3D human pose and mesh estimation
We also share prospects for advanced 3D human pose and shape estimation applications, centered on future communication channels and human behavior understanding.