Face editing involves modifying facial attributes like expression, head pose, or lighting, with the goal of preserving the subject’s unique identity features. Diffusion models have recently emerged as the dominant approach in visual generation, driven by their strong generative power. However, challenges persist in the realm of face editing, where independently and correctly editing target attributes while preserving high-fidelity identity information remains a formidable problem. In this paper, we present RigFace, a novel framework that combines controllable signals derived from a 3D Morphable Model (3DMM) with a fine-tuned Stable Diffusion (SD) model. Our basic idea to achieve by leveraging disentangled facial attributes provided by 3DMM and harnessing the strong generative capacity of Stable Diffusion. Specifically, our method contains: 1) A Spatial Attribute Encoder that provides robust and decoupled conditions of background, pose, expression and lighting; 2) A FaceFusion module that transfers identity information at different resolutions from the Identity Encoder to the Denoising UNet of a pre-trained SD model through self-attention, which facilitates detailed identity preservation. Our model achieves superior performance in both identity preservation and photorealism compared to existing face editing models.

Micro-expressions (MEs) are subtle and brief facial movements that reveal genuine emotional states but are often imperceptible due to their low intensity. While motion magnification has proven effective for enhancing ME visibility in 2D settings, its extension to 3D remains largely unexplored. In this work, we presentLatentMag, the first controllable 3D micro-expression magnification framework. Unlike traditional editing methods that rely on fixed labels or expression targets, our approach models expression intensity as a relative, input-dependent signal. We adopt registered 3D meshes as our representation, enabling vertex-level correspondence and interpretable displacement analysis. To guide magnification, we introduce a geometric prior that models amplification as a spatially adaptive transformation, where the change in pairwise distance between points on the output mesh scales with that observed between the input shapes, ensuring natural, localized deformation. We operationalize this prior in a generative framework by disentangling a latent intensity code, whose extrapolation drives controllable shape amplification. Trained in a self-supervised manner using unlabeled mesh sequences, LatentMag generalizes well to unseen identities and expressions, offering a novel solution that bridges geometric interpretability with realistic 3D expression modeling.

Micro-expression (ME) recognition is challenging due to the low-intensity facial motions. An idea to overcome this is learning assisted by macro-expressions (MaEs). However, the intensity gap between MaE and ME is so huge that related works fail to effectively leverage MaE’s assistance in overcoming low-intensity interference, which attempt to directly force ME knowledge to mimic MaE knowledge. In this paper, we propose that the knowledge transfer from MaE to ME can be converted into a progressive process for better implementation. Thus, we construct a progressive multi-step learning framework, which accomplishes two tasks: first, we dissect the huge intensity gap into multiple segments that are easier to bridge by constructing multiple learning steps, each corresponding to various intensity levels of expression recognition tasks. Second, through a designed self-knowledge distillation (self-KD) model, each dissected gap can be bridged, enabling the MaE knowledge to progressively transfer to guide the ME learning. Experiments carried out on three widely used databases demonstrated that the proposed PLMaM achieves state-of-the-art results.

Reconstructing missing modality based on available modalities is widely used to address inevitable modality-missing for Multimodal Emotion Recognition (MER). However, due to explicit distribution gap across heterogeneous modalities, they fail to guarantee the consistency between the reconstructed data and the ground truth. To mitigate this problem, we propose a novel method to restore the missing modality using its weighted prototypes rather than other modalities. Specifically, prototypes of different classes of missing modality are used to encapsulate its representative knowledge. Then sample-to-prototype affinity measuring class similarity is used as weights to combine these prototypes for reconstruction, thereby effectively restoring the distribution-consistent modality. Furthermore, to improve the efficacy of prototype-based completion under seriously missing, we devise an adaptive knowledge distillation from the strong modality to the weaker ones. This reinforces the representation ability of weak modality features. Extensive experiments on CMU-MOSI and IEMOCAP datasets demonstrate the superiority of our method.

Micro-Expression Recognition (MER) is challenging because the Micro-Expressions' (ME) motion is too weak to distinguish. This hurdle can be tackled by enhancing intensity for a more accurate acquisition of movements. However, existing magnification strategies tend to use the features of facial images that include not only intensity clues as intensity features, leading to the intensity representation deficient of credibility. In addition, the intensity variation over time, which is crucial for encoding movements, is also neglected. To this end, we provide a reliable scheme to extract intensity clues while considering their variation on the time scale. First, we devise an Intensity Distillation (ID) loss to acquire the intensity clues by contrasting the difference between frames, given that the difference in the same video lies only in the intensity. Then, the intensity clues are calibrated to follow the trend of the original video. Specifically, due to the lack of truth intensity annotation of the original video, we build the intensity tendency by setting each intensity vacancy an uncertain value, which guides the extracted intensity clues to converge towards this trend rather some fixed values. A Wilcoxon rank sum test (Wrst) method is enforced to implement the calibration. Experimental results on three public ME databases i.e. CASME II, SAMM, and SMIC-HS validate the superiority against state-of-the-art methods.

Micro-Expression recognition (MER) is a challenging task due to the short duration and low intensity of Micro-Expressions. A popular method to tackle this is magnifying MEs so as to enlarge the expression intensity to make recognition easier. However, the single fixed magnification strategy, widely used in existing works of MER, is not appropriate for different subjects, because each subject has specific expression intensity corresponding to different MEs. To cope with this issue, we propose a novel Attention-based Magnification-Adaptive Network (AMAN) to learn adaptive magnification levels for the ME representation. The network consists of two modules: magnification attention (MA module) to adaptively focus on appropriate magnification levels of different MEs, and frame attention (FA module) to focus on discriminative aggregated frames in a ME video. Extensive experiments on three widely used databases manifest that our method yields state-of-art results compared with other methods.

Cross-Database Micro-Expression Recognition (CD-MER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER.

Existing works for spontaneous Micro-Expression Recognition (MER) tend to encode Micro-Expression (ME) movements to get more discriminative features. However, MEs’ low intensity makes the capture for motion extremely difficult, and the widely adopted unified-magnification strategy is prone to noise and lacks flexibility. To this end, this paper provides a new insight to encode ME motion and tackle magnification noise. Specifically, we reconstruct a new sequence via magnification techniques to make subtle ME movements more distinguishable. Afterward, Sparse Self-Attention (SSA) rectifies self-attention with Locality Sensitive Hashing (LSH), cutting the space into several hush buckets of related features. Only keys in the same bucket are operated in the attention term for every query feature. The resulting sparsity in the attention matrix prevents the network from attending features stemming from less-informative magnification degrees which could be regarded as noise, while retains the sequence modelling capability of standard self-attention. Extensive experiments on three public MER databases demonstrate our superiority against the state-of-the-art methods.