Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation

TL;DR

This paper introduces FECO, a novel framework for dense foot contact estimation from a single RGB image, which is invariant to shoe styles and aware of ground features, advancing human movement understanding.

Contribution

The paper proposes a shoe style-invariant and ground-aware learning framework for dense foot contact estimation, addressing generalization and feature extraction challenges.

Findings

Achieves robust contact estimation across diverse shoe styles.

Effectively leverages ground information for improved accuracy.

Outperforms existing methods in dense foot contact prediction.

Abstract

Foot contact plays a critical role in human interaction with the world, and thus exploring foot contact can advance our understanding of human movement and physical interaction. Despite its importance, existing methods often approximate foot contact using a zero-velocity constraint and focus on joint-level contact, failing to capture the detailed interaction between the foot and the world. Dense estimation of foot contact is crucial for accurately modeling this interaction, yet predicting dense foot contact from a single RGB image remains largely underexplored. There are two main challenges for learning dense foot contact estimation. First, shoes exhibit highly diverse appearances, making it difficult for models to generalize across different styles. Second, ground often has a monotonous appearance, making it difficult to extract informative features. To tackle these issues, we present a FEet COntact estimation (FECO) framework that learns dense foot contact with shoe style-invariant and ground-aware learning. To overcome the challenge of shoe appearance diversity, our approach incorporates shoe style adversarial training that enforces shoe style-invariant features for contact estimation. To effectively utilize ground information, we introduce a ground feature extractor that captures ground properties based on spatial context. As a result, our proposed method achieves robust foot contact estimation regardless of shoe appearance and effectively leverages ground information. The codes are available at https://github.com/dqj5182/FECO_RELEASE.