Generalizable 3D Gaussian Splatting (G-3DGS) has recently emerged as a promising solution for efficient 3D scene representation and novel view synthesis. However, sparse-view scenarios pose a critical challenge for accurate depth estimation. In such cases, viewpoint overlaps are minimal, and many regions are visible from only a single view. As a result, reliable multi-view matching is unavailable in these areas, leading to significant reconstruction quality degradation. To tackle this bottleneck, we propose GraphSplat, a feed-forward framework for novel view synthesis that dynamically incorporates both cross-view and monocular cues through a graph-based feature aggregation strategy. Central to our approach is a Multi-view Aggregate Graph Attention (MAGA) mechanism, which adaptively reweights intra-view and inter-view node connections to compensate for unreliable multi-view correspondences with robust single-view depth priors. In addition, we design a Hierarchical Depth Fusion Estimator (HDFE) module to integrate monocular and multi-view depth cues, effectively reducing ghosting artifacts and improving geometric consistency. Extensive evaluations on RealEstate10K and ACID benchmarks show that GraphSplat achieves competitive performance against prior SOTA methods, with improvements in appearance fidelity and cross-dataset generalization particularly under challenging sparse-view conditions.

Abstract Chinese stroke segmentation is a crucial and challenging task for various downstream applications such as font generation, aesthetic evaluation etc. Conventional semantic segmentation techniques typically face difficulties in accurately segmenting strokes, as intersection regions in Chinese characters can belong to multiple strokes simultaneously, and these approaches often lack a holistic understanding of character composition. This paper proposes a character stroke segmentation framework named Stroke‐Seg that integrates with various semantic segmentation architectures, demonstrating adaptability to different backbone networks to tackle the above tasks. A multi‐label output strategy is proposed to effectively classify strokes in intersection areas, overcoming the limitations of traditional semantic segmentation approaches. Additionally, a prior knowledge vector is incorporated into the input layer to provide character‐specific information on stroke composition, enhancing the ability of the framework to precisely identify and segment strokes. The effectiveness of the proposed framework is demonstrated through evaluations of a comprehensive dataset (brush calligraphy stroke segmentation dataset). Evaluations show that the proposed framework significantly improves the capability of semantic segmentation networks, achieving a remarkable improvement of up to 19.9% in the true stroke rate, compared to traditional stroke segmentation techniques. The Stroke‐Seg framework integrated with TransUNet demonstrates its high performance with an impressive 98.2% true stroke rate. Furthermore, the proposed framework combined with FCN still achieves good performance while consuming the least amount of computational resources and memory, demonstrating its potential for lightweight design.