Monocular facial performance capture in-the-wild is challenging due to varied capture conditions, face shapes, and expressions. Most current methods rely on linear 3D Morphable Models, which represent facial expressions independently of identity at the vertex displacement level. We propose SEREP (Semantic Expression Representation), a model that disentangles expression from identity at the semantic level. We start by learning an expression representation from high-quality 3D data of unpaired facial expressions. Then, we train a model to predict expression from monocular images relying on a novel semi-supervised scheme using low quality synthetic data. In addition, we introduce MultiREX, a benchmark addressing the lack of evaluation resources for the expression capture task. Our experiments show that SEREP outperforms state-of-the-art methods, capturing challenging expressions and transferring them to new identities.

Visible-infrared person re-identification (V-I ReID) seeks to retrieve images of the same individual captured over a distributed network of RGB and IR sensors. Several V-I ReID approaches directly integrate the V and I modalities to represent images within a shared space. However, given the significant gap in the data distributions between V and I modalities, cross-modal V-I ReID remains challenging. A solution is to involve a privileged intermediate space to bridge between modalities, but in practice, such data is not available and requires selecting or creating effective mechanisms for informative intermediate domains. This paper introduces the Adaptive Generation of Privileged Intermediate Information (AGPI2) training approach to adapt and generate a virtual domain that bridges discriminative information between the V and I modalities. AGPI2enhances the training of a deep V-I ReID backbone by generating and then leveraging bridging privileged information without modifying the model in the inference phase. This information captures shared discriminative attributes that are not easily ascertainable for the model within individual V or I modalities. Towards this goal, a non-linear generative module is trained with adversarial objectives, transforming V attributes into intermediate spaces that also contain I features. This domain exhibits less domain shift relative to the I domain compared to the V domain. Meanwhile, the embedding module within AGPI2aims to extract discriminative modality-invariant features for both modalities by leveraging modality-free descriptors from generated images, making them a bridge between the main modalities. Experiments conducted on challenging V-I ReID datasets indicate that AGPI2consistently increases matching accuracy without additional computational resources during inference.