Learning to Reconstruct People in Clothing from a Single RGB Camera

Understand

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm.

• Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape.
• The model achieves fast and accurate predictions based on two key design choices.
• First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused.