Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view

TL;DR

This paper introduces ImpHMR, a novel method for 3D human mesh recovery that leverages consistency across unseen views using neural feature fields and self-supervised learning, improving inference accuracy from single images.

Contribution

ImpHMR is the first to implicitly model 3D human meshes at the feature level with view consistency, enabling better generalization to unseen viewpoints.

Findings

Achieves state-of-the-art accuracy in 3D human mesh recovery.

Effectively utilizes self-supervised learning for view-invariant pose and shape estimation.

Demonstrates robustness in scenarios with only 2D labels.

Abstract

From an image of a person, we can easily infer the natural 3D pose and shape of the person even if ambiguity exists. This is because we have a mental model that allows us to imagine a person's appearance at different viewing directions from a given image and utilize the consistency between them for inference. However, existing human mesh recovery methods only consider the direction in which the image was taken due to their structural limitations. Hence, we propose "Implicit 3D Human Mesh Recovery (ImpHMR)" that can implicitly imagine a person in 3D space at the feature-level via Neural Feature Fields. In ImpHMR, feature fields are generated by CNN-based image encoder for a given image. Then, the 2D feature map is volume-rendered from the feature field for a given viewing direction, and the pose and shape parameters are regressed from the feature. To utilize consistency with pose and shape from unseen-view, if there are 3D labels, the model predicts results including the silhouette from an arbitrary direction and makes it equal to the rotated ground-truth. In the case of only 2D labels, we perform self-supervised learning through the constraint that the pose and shape parameters inferred from different directions should be the same. Extensive evaluations show the efficacy of the proposed method.