Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos

TL;DR

This paper introduces a self-supervised learning approach for high-fidelity 3D human reconstruction from social media videos, overcoming the lack of ground truth data by enforcing temporal coherence and geometric consistency.

Contribution

It presents a novel self-supervised method leveraging local transformations and temporal coherence to learn detailed 3D human geometry without ground truth labels.

Findings

Outperforms state-of-the-art depth estimation methods

Achieves high-fidelity depth and surface normal predictions

Provides theoretical bounds for self-supervised learning performance

Abstract

A key challenge of learning a visual representation for the 3D high fidelity geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real-world imagery. We address this challenge by leveraging a new data resource: a number of social media dance videos that span diverse appearance, clothing styles, performances, and identities. Each video depicts dynamic movements of the body and clothes of a single person while lacking the 3D ground truth geometry. To learn a visual representation from these videos, we present a new self-supervised learning method to use the local transformation that warps the predicted local geometry of the person from an image to that of another image at a different time instant. This allows self-supervision by enforcing a temporal coherence over the predictions. In addition, we jointly learn the depths along with the surface normals that are highly responsive to local texture, wrinkle, and shade by maximizing their geometric consistency. Our method is end-to-end trainable, resulting in high fidelity depth estimation that predicts fine geometry faithful to the input real image. We further provide a theoretical bound of self-supervised learning via an uncertainty analysis that characterizes the performance of the self-supervised learning without training. We demonstrate that our method outperforms the state-of-the-art human depth estimation and human shape recovery approaches on both real and rendered images.