鞋款无关与地面感知的密集足部接触估计学习

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

November 27, 2025

作者: Daniel Sungho Jung, Kyoung Mu Lee

cs.AI

摘要

脚部接触在人类与世界的互动中起着关键作用，因此探索脚部接触能够深化我们对人体运动与物理交互的理解。尽管其重要性不言而喻，现有方法通常采用零速度约束来近似模拟脚部接触，并聚焦于关节层面的接触分析，未能捕捉脚部与地面之间精细的交互细节。密集脚部接触估计对于精确建模这种交互至关重要，然而从单张RGB图像预测密集脚部接触的研究仍处于探索不足的状态。学习密集脚部接触估计主要面临两大挑战：首先，鞋类外观差异巨大，导致模型难以适应不同鞋型；其次，地面通常呈现单调外观，使得特征提取困难。为解决这些问题，我们提出了一种脚部接触估计框架FECO，通过鞋型不变性与地面感知学习来实现密集脚部接触估计。针对鞋类外观多样性挑战，本方法引入鞋型对抗训练机制，强制模型提取与鞋型无关的特征进行接触估计。为有效利用地面信息，我们设计了基于空间上下文的地面特征提取器来捕捉地面属性。实验表明，所提方法能够不受鞋类外观影响实现鲁棒的脚部接触估计，并有效利用地面信息。代码将公开发布。

English

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. Code will be released.