仅用一张图就能精准重建物体变形，还能保持几何一致性。
Rigidity-Aware 3D Gaussian Deformation from a Single Image
- 通过像素与高斯点匹配，打通2D图像与3D高斯表示的桥梁。
- 引入刚性区域分割，有效识别刚体部分并维持形变连贯性。
- 适合单图形变重建、帧插值等需要高保真动态建模的场景。
从单张图像重建物体形变在计算机视觉与图形学中仍是重大挑战。现有方法通常依赖多视角视频恢复形变，限制了在受限场景下的应用。为此，我们提出 DeformSplat，一种仅需单张图像即可有效引导3D高斯形变的新框架。方法包含两项关键技术：首先，提出高斯到像素匹配（Gaussian-to-Pixel Matching），弥合3D高斯表示与2D像素观测之间的域差距，实现从稀疏视觉线索中稳健地指导形变。其次，设计刚性区域分割（Rigid Part Segmentation），包括初始化与细化阶段，显式识别刚性区域，对维持形变过程中的几何一致性至关重要。结合两项技术，本方法可从单张图像重建一致的形变结果。大量实验表明，该方法显著优于现有方法，并自然扩展至帧插值与交互式物体操作等多种应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。