鞋款不變與地面感知學習：密集足部接觸估計

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

腳部接觸在人與世界的互動中扮演著關鍵角色，因此探索腳部接觸能增進我們對人類運動與物理互動的理解。儘管其重要性不言而喻，現有方法常採用零速度約束來近似模擬腳部接觸，且多聚焦於關節層面的接觸，未能捕捉腳部與環境間的細緻互動。密集腳部接觸估計對於精確建模此類互動至關重要，然而從單張RGB影像預測密集腳部接觸的研究仍相當匱乏。學習密集腳部接觸估計主要面臨兩大挑戰：首先，鞋具外觀高度多樣化，使模型難以跨風格泛化；其次，地面通常呈現單調外觀，導致難以提取具信息量的特徵。為解決這些問題，我們提出FECO框架，透過鞋款不變性學習與地面感知學習來實現密集腳部接觸估計。為克服鞋具外觀多樣性挑戰，我們引入鞋款對抗訓練機制，強制模型提取與鞋款無關的特徵進行接觸估計。為有效利用地面信息，我們設計了基於空間上下文捕捉地面屬性的地面特徵提取器。實驗結果表明，無論鞋具外觀如何變化，我們的方法都能實現穩健的腳部接觸估計，並有效利用地面信息。程式碼將公開釋出。

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.