Skeleton-based hand gesture recognition plays a crucial role in enabling intuitive human–computer interaction. Traditional methods have primarily relied on hand-crafted features—such as distances between joints or positional changes across frames—to alleviate issues from viewpoint variation or body proportion differences. However, these hand-crafted features often fail to capture the full spatio-temporal information in raw skeleton data, exhibit poor interpretability, and depend heavily on dataset-specific preprocessing, limiting generalization. In addition, normalization strategies in traditional methods, which rely on training data, can introduce domain gaps between training and testing environments, further hindering robustness in diverse real-world settings. To overcome these challenges, we exclude traditional hand-crafted features and propose Skeleton Kinematics Extraction Through Coordinated grapH (SKETCH), a novel framework that directly utilizes raw four-dimensional (time, x, y, and z) skeleton sequences and transforms them into intuitive visual graph representations. The proposed framework incorporates a novel learnable Dynamic Range Embedding (DRE) to preserve axis-wise motion magnitudes lost during normalization and visual graph representations, enabling richer and more discriminative feature learning. This approach produces a graph image that richly captures the raw data’s inherent information and provides interpretable visual attention cues. Furthermore, SKETCH applies independent min–max normalization on fixed-length temporal windows in real time, mitigating degradation from absolute coordinate fluctuations caused by varying sensor viewpoints or differences in individual body proportions. Through these designs, our approach becomes inherently topology-agnostic, avoiding fragile dependencies on dataset- or sensor-specific skeleton definitions. By leveraging pre-trained vision backbones, SKETCH achieves efficient convergence and superior recognition accuracy. Experimental results on SHREC’19 and SHREC’22 benchmarks show that it outperforms state-of-the-art methods in both robustness and generalization, establishing a new paradigm for skeleton-based hand gesture recognition. The code is available at https://github.com/capableofanything/SKETCH.

In this work, we observed that a nonlinear line could be expressed with a set of linear lines. We propose a novel lane detection method with polyline-based reconstruction based on this hypothesis. We define the optimal principal points with a new metric, the principal score, to generate the polyline. According to the principal score, we select principal points having a high influence on lane reconstruction and simply reproduce the target lane by connecting them. Additionally, conventional methods predict a fixed number of parameters to express each lane. However, this can limit an ability to represent a lane curvature and cause inaccurate detection results. Therefore, we set the number of principal points to be dynamically changed depending on the lane curvature to solve this problem. This allows the model to make flexible detection results reflecting the characteristics of each lane. We also propose a training strategy with a new piece-wise linear equation-based loss function. With this strategy, the model is fine-tuned to predict the principal points representing the curved parts of the lane well. Last, we propose a spatial context-aware feature flip fusion module to exploit the symmetric property of road images. This module helps the model selectively utilize the spatial context in the flipped feature map based on the lane density. We effectively reduce the adverse effects, especially the false positives of the existing feature flip fusion module misaligned on asymmetrical images. The experiments show that the proposed method provides competitive lane detection results compared to state-of-the-art methods.