The demand for identity-preserving Facial Aesthetic Enhancement (FAE) has surged in social media and digital entertainment. However, existing methods based on deep generative models encounter difficulties in striking a balance between fine-grained detail enhancement and preserving the unique identities of individuals from diverse ethnic and gender backgrounds. To tackle this issue, this paper proposes a novel tuning-based framework that integrates prototype-based hierarchical prompt learning within a CLIP model and a StyleGAN-based inversion model. Our approach first adapts a pre-trained StyleGAN to the input face via pivotal tuning, optimizing around pivotal latent codes to minimize reconstruction distortion while retaining editability. Then, a prototype-based hierarchical prompt learning module is designed for learning multigrained facial features to achieve comprehensive and fine-grained facial descriptions for FAE. Specifically, we propose a prototypical similarity measure based on a multi-ethnic dataset to select geometrically similar faces with high aesthetic scores as reference faces. This selection is guided by ArcFace regularization within categorized gender and ethnic groups to minimize identity loss. Additionally, we design a novel aesthetic attribute selection algorithm to generate generic fine-grained aesthetic attributes from these reference faces for detailed facial descriptions. These components work synergistically through dynamic weight modulation, prioritizing features with high aesthetic contributions (such as enhancing lip fullness) while ensuring semantic consistency through CLIP-driven optimization for pivotal latent codes. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques in both aesthetic quality and identity preservation, especially for out-of-domain faces.

Abstract Although single‐image 3D human reconstruction has made significant progress in recent years, few of the current state‐of‐the‐art methods can accurately restore the appearance and geometric details of loose clothing. To achieve high‐quality reconstruction of a human body wearing loose clothing, we propose a learnable dynamic adjustment framework that integrates side‐view features and the uncertainty of the parametric human body model to adaptively regulate its reliability based on the clothing type. Specifically, we first adopt the Vision Transformer model as an encoder to capture the image features of the input image, and then employ SMPL‐X to decouple the side‐view body features. Secondly, to reduce the limitations imposed by the regularization of the parametric model, particularly for loose garments, we introduce a learnable coefficient to reduce the reliance on SMPL‐X. This strategy effectively accommodates the large deformations caused by loose clothing, thereby accurately expressing the posture and clothing in the image. To evaluate the effectiveness, we validate our method on the public CLOTH4D and Cape datasets, and the experimental results demonstrate better performance compared to existing approaches. The code is available at https://github.com/yyd0613/CoRe-Human .

Generative Adversarial Networks (GANs) are capable of effectively synthesising new realistic images and estimating the potential distribution of samples utilising adversarial learning. Nevertheless, conventional GANs require a large amount of training data samples to produce plausible results. Inspired by the capacity for humans to quickly learn new concepts from a small number of examples, several meta-learning approaches for the few-shot datasets are presented. However, most of meta-learning algorithms are designed to tackle few-shot classification and reinforcement learning tasks. Moreover, the existing meta-learning models for image generation are complex, thereby affecting the length of training time required. Fast Adaptive Meta-Learning (FAML) based on GAN and the encoder network is proposed in this study for few-shot image generation. This model demonstrates the capability to generate new realistic images from previously unseen target classes with only a small number of examples required. With 10 times faster convergence, FAML requires only one-fourth of the trainable parameters in comparison baseline models by training a simpler network with conditional feature vectors from the encoder, while increasing the number of generator iterations. The visualisation results are demonstrated in the paper. This model is able to improve few-shot image generation with the lowest FID score, highest IS, and comparable LPIPS to MNIST, Omniglot, VGG-Faces, andminiImageNet datasets. The source code is available onhttps://github.com/phaphuang/FAML.

With the exponential growth of video data, video summarization techniques are urgently needed for reducing people’s efforts in the videos' content exploration by generating succinct but informative summaries from original lengthy videos. Though supervised video summarization approaches have demonstrated the state-of-the-art performance, unsupervised methods are still highly demanded due to resourcefully expensive human annotations and the subjectiveness of video summarization tasks. In this paper, a novel unsupervised-based Deep Self-attention Recurrent summarization network with Reinforcement Learning (DSR-RL) for video summarization is proposed. The model can learn the input video sequence and suggest the key-shot summary without additional human annotations by integrating self-attention, BRNN, and reinforcement learning mechanisms. The DSR-RL improves not only importance score through the attention map vector of self-attention network but also the diversity of summaries via the reward function of reinforcement learning. Our method outperforms the state-of-the-art unsupervised video summarization methods on both SumMe and TVSum datasets. The source code is available at https://github.com/phaphuang/DSR-RL.

Saliency detection plays an important role in image segmentation, object detection and retrieval, which attracts more attention in the field of computer vision recently. Most existing saliency detection algorithms have not considered the influence of visual focus (VF) shifting yet. In this study, a novel algorithm named moving region contrast (MRC) is proposed to analyse image saliency. The algorithm MRC is built on a novel concept of moving VF. The initial VF is defined as the geometric centre of the image. Then the VF is calculated iteratively by focus‐moving technique where a saliency gravitation model is employed to determine the moving direction. The salient region is obtained according to the final VF. The experiments are conducted on the dataset with 1000 images released by Achanta. Experimental results show that the proposed algorithm achieves marked improvements in performance and outperforms other 11 popular algorithms.