Abstract
Recent advances in 3D facial expression reconstruction have demonstrated remarkable performance in capturing macro-expressions, yet the reconstruction of micro-expressions remains unexplored. This novel task is particularly challenging due to the subtle, transient, and low-intensity nature of micro-expressions, which complicate the extraction of stable and discriminative features essential for accurate reconstruction. In this paper, we propose a fine-grained micro-expression reconstruction method that integrates a global dynamic feature capturing stable facial motion patterns with a locally-enriched feature incorporating multiple informative cues from 2D motions, facial priors and 3D facial geometry. Specifically, we devise a plug-and-play dynamic-encoded module to extract micro-expression feature for global facial action, allowing it to leverage prior knowledge from abundant macro-expression data to mitigate the scarcity of micro-expression data. Subsequently, a dynamic-guided mesh deformation module is designed for extracting aggregated local features from dense optical flow, sparse landmark cues and facial mesh geometry, which adaptively refines fine-grained facial micro-expression without compromising global 3D geometry. Extensive experiments on micro-expression datasets demonstrate that our method consistently outperforms state-of-the-art methods in both geometric accuracy and perceptual detail.
Abstract (translated)
最近在3D面部表情重建方面取得的进展展示了捕捉宏观表情方面的出色性能，然而，对微观表情（micro-expressions）的重建研究尚处于探索阶段。这一新颖的任务尤其具有挑战性，因为微表情通常非常微妙、短暂且强度低，这使得提取稳定而有区分度的特征变得复杂，这些特征对于准确地进行微表情重建至关重要。 在本文中，我们提出了一种细粒度的微表情重构方法，该方法结合了用于捕获面部整体运动模式的全局动态特性以及通过2D运动、面部先验知识和3D面部几何结构获取的局部丰富特征。具体而言，我们设计了一个即插即用的动态编码模块来提取面向全球面部动作的微表情特征，并利用宏表情数据中的先前知识以缓解微表情数据稀缺的问题。随后，我们还设计了动态引导网格变形模块，用于从密集光流、稀疏地标线索和面部网格几何结构中提取聚合局部特性，从而在不损害全局3D几何的情况下适应性地细化细微的面部微表情。 通过在多个微表情数据集上进行广泛的实验，我们的方法在几何精度和感知细节方面始终优于现有最先进的技术。
URL
https://arxiv.org/abs/2603.07043
