Title: Rigidity-Aware 3D Gaussian Deformation from a Single Image Show Chinese title

Title: 从单张图像中感知刚度的3D高斯变形

Abstract: Reconstructing object deformation from a single image remains a significant challenge in computer vision and graphics. Existing methods typically rely on multi-view video to recover deformation, limiting their applicability under constrained scenarios. To address this, we propose DeformSplat, a novel framework that effectively guides 3D Gaussian deformation from only a single image. Our method introduces two main technical contributions. First, we present Gaussian-to-Pixel Matching which bridges the domain gap between 3D Gaussian representations and 2D pixel observations. This enables robust deformation guidance from sparse visual cues. Second, we propose Rigid Part Segmentation consisting of initialization and refinement. This segmentation explicitly identifies rigid regions, crucial for maintaining geometric coherence during deformation. By combining these two techniques, our approach can reconstruct consistent deformations from a single image. Extensive experiments demonstrate that our approach significantly outperforms existing methods and naturally extends to various applications,such as frame interpolation and interactive object manipulation. Show Chinese abstract

Abstract: 从单张图像重建物体变形仍然是计算机视觉和图形学中的一个重要挑战。 现有方法通常依赖多视角视频来恢复变形，在受限场景下应用受到限制。 为了解决这个问题，我们提出了 DeformSplat，一种新颖的框架，能够仅从单张图像有效引导 3D 高斯变形。 我们的方法引入了两项主要技术贡献。 首先，我们提出了高斯到像素匹配，弥合了 3D 高斯表示与 2D 像素观测之间的领域差距。 这使得可以从稀疏视觉线索中进行鲁棒的变形引导。 其次，我们提出了刚性部件分割，包括初始化和细化。 这种分割显式地识别刚性区域，这对于在变形过程中保持几何一致性至关重要。 通过结合这两种技术，我们的方法可以从单张图像中重建一致的变形。 大量实验表明，我们的方法显著优于现有方法，并自然扩展到各种应用，如帧插值和交互式物体操作。