Fine-Grained 3D Facial Reconstruction for Micro-Expressions

• URL: http://arxiv.org/abs/2603.07043v1
• Date: Sat, 07 Mar 2026 05:34:57 GMT
• Title: Fine-Grained 3D Facial Reconstruction for Micro-Expressions
• Abstract summary: We propose a fine-grained micro-expression reconstruction method that integrates a global dynamic feature capturing stable facial motion patterns.<n>Our method consistently outperforms state-of-the-art methods in both geometric accuracy and perceptual detail.
• Score: 37.37024356725285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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