Reconstructing photo-realistic and animatable human avatars from monocular RGB videos is a long-standing and challenging problem. Existing methods based on Neural Radiance Field (NeRF) or 3D Gaussian Splatting (3DGS) can model animatable avatars, but often face difficulties in capturing high-frequency dynamic appearance details under monocular settings. To address this issue, we propose SubGAva, an avatar modeling approach based on Gaussian primitive subdivision. Our method employs an efficient geometry–appearance fusion strategy to characterize the coupled variations of geometry and appearance induced by pose changes. In addition, we introduce a controllable Gaussian subdivision mechanism together with a high-frequency guided loss, which improves the reconstruction of fine-grained surface details. Experimental results on People-Snapshot and DynVideo datasets show that our method yields superior rendering quality and more realistic dynamic appearance compared to existing approaches.

The historical atlases provide a wealth of information about the evolution of geography over time and space. The alignment of geographical entities across varying time periods is a crucial aspect of extracting meaningful insights into the spatio-temporal dynamics of geography. This paper proposes a geographic entity alignment approach coupling Natural Gradient Boosting (NGBoost) with SHapley Additive exPlanation (SHAP). Taking the historical atlas of China as a case study, a geographic entity alignment model based on NGBoost is constructed considering the different kinds of similarity features of geographic entities, including semantic, distance, shape, size and topology. The contribution of similarity features in the NGBoost model is analyzed using the SHAP framework so as to improve the explanatory capacity of the model. The spatio-temporal evolution relationships of geographic entities are generated by association rules depending on alignment types and represented as quadruples, for constructing geographic knowledge graphs. The proposed NGBoost method was found a superior accuracy by comparing with BP neural networks, random forests, and other alternative methods for aligning geographic entities. The constructed geographic spatio-temporal evolution knowledge graphs offer valuable support for the queries of evolutionary knowledge.

Zero-shot learning (ZSL) aims to recognize images from unseen (novel) classes with the training images from seen classes. The attributes of each class is exploited as auxiliary semantic information. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the seen classes to the unseen classes. However, few works study whether the auxiliary semantic information in the class-level is extensive enough or not for the ZSL task. To tackle such problem, we propose a hierarchical coupled dictionary learning (HCDL) approach to hierarchically align the visual-semantic structures in both the class-level and the image-level. Firstly, the class-level coupled dictionary is trained to establish a basic connection between visual space and semantic space. Then, the image attributes are generated based on the basic connection. Finally, the fine-grained information can be embedded by training the image-level coupled dictionary. Zero-shot recognition is performed in multiple spaces by searching the nearest neighbor class of the unseen image. Experiments on two widely used benchmark datasets show the effectiveness of the proposed approach.

Existing non-rigid shape matching methods mainly involve two disadvantages. (a) Local details and global features of shapes can not be carefully explored. (b) A satisfactory trade-off between the matching accuracy and computational efficiency can be hardly achieved. To address these issues, we propose a local-global commutative preserving functional map (LGCP) for shape correspondence. The core of LGCP involves an intra-segment geometric submodel and a local-global commutative preserving submodel, which accomplishes the segment-to-segment matching and the point-to-point matching tasks, respectively. The first submodel consists of an ICP similarity term and two geometric similarity terms which guarantee the correct correspondence of segments of two shapes, while the second submodel guarantees the bijectivity of the correspondence on both the shape level and the segment level. Experimental results on both segment-to-segment matching and point-to-point matching show that, LGCP not only generate quite accurate matching results, but also exhibit a satisfactory portability and a high efficiency.

Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4 2 , and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.

A new facial feature extraction method is proposed. Based on linear combination model, the method locates feature points in facial images precisely. The model uses the knowledge of prototypic faces to interpret novel faces. To get the knowledge, the prototypes are labeled manually on the feature points. Generally, the construction of the linear combination model depends on pixel-wise alignments of prototypes, and the alignments are computed by an optical flow algorithm or bootstrapping algorithm which is a full-scale optimization and not includes local information such as facial feature points. To combine local facial feature with the linear combination model, a restrained optical flow algorithm is proposed to compute the pixel-wise alignments. With the information of labeled feature points, the model matches the input facial images and extracts the feature points automatically. Implementing the feature extraction method on the MPI face database, the experimental results show that the method has good performance.