Age significantly influences human motor patterns, yet existing virtual reality (VR) systems lack dynamic modelling of these variations. This paper introduces AgeStyle, a versatile framework that integrates age-guided style selection with motion style transfer to convert user-uploaded videos into interactive 3D motion models. Utilizing 2D joint detection and 3D pose estimation, AgeStyle constructs motion representations enhanced by a CLIP-driven Cross-Attention module, capturing the distinct traits of different age groups—child flexibility, adult efficiency, and elderly stability. Our system enables real-time switching between motion styles and perspectives through voice commands, offering an immersive exploration of age-related movements. Quantitative experiments on the XIA dataset demonstrate AgeStyle’s competitive performance in both content preservation and style consistency, achieving average CC and SC++ scores of 7.4 and 14.8, respectively. AgeStyle represents a meaningful advancement in VR character design, with broad potential applications in education, healthcare, rehabilitation, and interactive entertainment. For the demo, please refer to https://youtu.be/eo7Shy0Ukps. The source code of AgeStyle is available at https://github.com/codeozzz/ageStyle.
Large-area semi-closed holes on the lateral surfaces of tooth models can cause difficulties in subsequent orthodontic tasks, such as collision detection and finite element analysis, and may affect the accuracy and stability of further processing. To address this issue, this paper proposes a three-dimensional tooth semi-closed hole repair method based on the moving least squares technique. First, key points were manually selected to transform semi-closed holes into closed ones, and new vertices were uniformly distributed within the hole region to construct an initial triangular mesh. Subsequently, a mesh homogenization algorithm was applied to improve the quality of the generated triangular patches. Then, a moving least-squares surface-fitting approach was employed. By incorporating the point cloud density and Gaussian curvature, an adaptive vertex influence region was designed to iteratively adjust the vertex positions and better fit the target surface. Finally, the generated mesh was smoothed using the Laplacian-Beltrami operator to obtain a repaired surface consistent with the original geometry. The experimental results demonstrate that the proposed method achieves superior efficiency and repair quality compared to existing approaches.
Cognitive decline (CD) is a hallmark symptom of many neurodegenerative diseases, and in severe cases, it progresses to cognitive dysfunction and a loss of independent daily functioning. Eye movement interaction (EMI) has shown promise in detecting early signs of CD, but its applications remain largely confined to clinical settings, limiting integration into everyday life. In response, this paper proposes a novel method based on naturalistic EMI for the prediagnosis of CD, leveraging sports streaming to induce saccades and using ball movements as dynamic stimuli to uncover early neural dysfunction. Through a pilot and empirical study, we demonstrate that ball games can effectively elicit saccades, and our algorithm can accurately extract batting saccades to distinguish between the healthy control group (HCG) and the cognitive decline group (CDG). In this study with 60 participants, the accuracy of recognizing CDG in natural, unmarked game viewing was 0.7333 at best, and reached 0.8833 when semi-natural random flashing cues were introduced on balls. These findings illustrate the potential for seamlessly integrating naturalistic EMI-based prediagnosis into everyday interactions.
Craniofacial reconstruction aims to estimate a person’s facial appearance from the skull and has applications in forensic investigation, reconstructive surgery, archaeology, and anthropology. The skull and face are typically represented as triangular meshes, on which geodesics provide intrinsic surface structures. Accordingly, we propose a craniofacial reconstruction framework that represents facial geometry using geodesics and learns a geodesic-based statistical model. First, we extract a set of geodesic curves for each registered face mesh, originating from the nose tip. Next, paired skull meshes and their corresponding facial geodesics are used to train a principal component analysis (PCA)-based statistical model that predicts the facial geodesics for an unseen skull. Finally, we reconstruct the full-face mesh by fitting a facial statistical shape model to the predicted geodesics. Because the intrinsic properties of the geodesics and geodesic distances are preserved under isometric deformations, the accuracy of the corresponding feature points automatically established by the geodesics and exact correspondence between the feature points are ensured, thereby improving the accuracy of the craniofacial reconstruction results. Experiments demonstrate improved reconstruction accuracy and reduced runtime compared with baseline methods.
This survey reviews feed-forward, point-cloud 3D reconstruction methods from DUSt3R to VGGT and their recent variants. Here, feed-forward primarily refers to predicting dense geometry and, when applicable, camera poses through learned network inference, without relying on classical per-scene SfM + MVS optimization as the main inference mechanism. We first formalize the reconstruction task in pose-aware and pose-free settings, and contrast feed-forward point-map regression with classical Structure-from-Motion and Multi-View Stereo pipelines. Building on this, we organize existing methods into three stages: early pairwise models typified by DUSt3R, DUSt3R-style extensions that enhance multi-view consistency, streaming, efficiency, and dynamic-scene handling, and large unified transformers such as VGGT that process tens to hundreds of views jointly, while noting differences in their inference paradigms. We analyze these models along shared axes, including scene representation, correspondence reasoning, pose regression, fusion strategies, and the role of large-scale training data. We summarize widely used 3D datasets and evaluation metrics, and provide a case study on the DTU benchmark for multi-view depth and point map estimation, highlighting accuracy-efficiency trade-offs between optimization-based and feed-forward approaches. Finally, we discuss open challenges in data scarcity, sparse-view reconstruction, non-Lambertian structures, dynamic scenes, long-context processing, and resource-efficient deployment, and outline future directions that combine feed-forward architectures with differentiable rendering, generative priors, and safety mechanisms to enable scalable and trustworthy 3D reconstruction systems.
