The metaverse has gained popularity recently in many areas including social media, healthcare, education and manufacturing. This work explores the use of the metaverse concept for cultural heritage applications. The motivation is to develop a systematic approach for the construction of a cultural heritage metaverse and to offer, potentially, more effective solutions for tourism guidance, site maintenance, heritage object conservation, etc. We propose a framework for this cultural heritage metaverse with an emphasis on fundamental elements and on characterization of the mapping between the physical and virtual cultural heritage worlds. Efforts are made to analyze the dimensional structures of the cultural heritage metaverse. Specifically, five different dimensions, linearity, planarity, space, time and context, are discussed to better understand this metaverse. The proposed framework and methodology are novel and can be applied to the digitalization of cultural heritage via its metaverse development. This is followed by a detailed case study to illustrate the tangible procedure, constructing a cultural heritage metaverse with a complex and dynamic nature which can be used for different applications, including heritage conservation.

Person re-identification is the task of identifying pedestrians across different cameras. Domain adaptation person re-identification involves transferring knowledge from labeled source domains to unlabeled target domains, with applications in security and surveillance. Challenges emerge due to variations in sample quality and disparities in distance distribution between positive and negative sample pairs. To address these challenges, this paper proposes a quality guided metric learning approach for domain adaptation person re-identification. We focus on improving appearance similarity metrics by evaluating sample quality based on local visibility, categorizing images as high or low quality. Besides, we introduce an adaptive weight triplet loss incorporating camera information to optimize triplets. This reduces the effects of invalid triplets and facilitating ongoing target domain learning.We have conducted comprehensive comparative evaluations to showcase the advantages and superiority of our proposed method. Our method has 2.6%, 1.9%, and 6.2% improved on Market-1501, DukeMTMC-reID, and MSMT17 datasets, respectively.

We present a variational method for subdivision surface reconstruction from a noisy dense mesh. A new set of subdivision rules with continuous sharpness control is introduced into Loop subdivision for better modeling subdivision surface features such as semi-sharp creases, creases, and corners. The key idea is to assign a sharpness value to each edge of the control mesh to continuously control the surface features. Based on the new subdivision rules, a variational model with L1 norm is formulated to find the control mesh and the corresponding sharpness values of the subdivision surface that best fits the input mesh. An iterative solver based on the augmented Lagrangian method and particle swarm optimization is used to solve the resulting non-linear, non-differentiable optimization problem. Our experimental results show that our method can handle meshes well with sharp/semi-sharp features and noise.