鞋风格不变与地面感知学习用于密集足部接触估计
Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation
针对鞋外观多样性，引入鞋风格对抗训练以学习风格不变特征；针对地面外观单调，设计基于空间上下文的地面特征提取器。这两点可迁移到其他需要跨外观泛化的密集预测任务（如人体解析、交互区域分割）。
原文依据 shoe style adversarial training that enforces shoe style-invariant features for contact estimation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论足部接触在人类与世界的交互中起着关键作用，因此探索足部接触可以增进我们对人体运动和物理交互的理解。尽管其重要性，现有方法通常使用零速度约束来近似足部接触，并关注关节级接触，未能捕捉足部与世界之间的详细交互。密集估计足部接触对于准确建模这种交互至关重要，然而从单张RGB图像预测密集足部接触在很大程度上仍未被充分探索。学习密集足部接触估计存在两个主要挑战。首先，鞋子外观高度多样，使得模型难以在不同风格间泛化。其次，地面通常外观单调，难以提取有信息量的特征。为解决这些问题，我们提出了一个足部接触估计（FECO）框架，通过鞋风格不变和地面感知学习来学习密集足部接触。为克服鞋外观多样性的挑战，我们的方法结合了鞋风格对抗训练，强制学习用于接触估计的鞋风格不变特征。为有效利用地面信息，我们引入了一个基于空间上下文捕捉地面属性的地面特征提取器。因此，我们提出的方法无论鞋外观如何都能实现稳健的足部接触估计，并有效利用地面信息。代码可在 https://github.com/dqj5182/FECO_RELEASE 获取。
英文原文摘要 1565 字符
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.