Recent advances in 3D Gaussian Splatting have shown promising results. Existing methods typically assume static scenes and/or multiple images with prior poses. Dynamics, sparse views, and unknown poses significantly increase the problem complexity due to insufficient geometric constraints. To overcome this challenge, we propose a method that can use only two images without prior poses to fit Gaussians in dynamic environments. To achieve this, we introduce two technical contributions. First, we propose an object-level two-view bundle adjustment. This strategy decomposes dynamic scenes into piece-wise rigid components, and jointly estimates the camera pose and motions of dynamic objects. Second, we design an SE(3) field-driven Gaussian training method. It enables fine-grained motion modeling through learnable per-Gaussian transformations. Our method leads to high-fidelity novel view synthesis of dynamic scenes while accurately preserving temporal consistency and object motion. Experiments on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-art approaches designed for the cases of static environments, multiple images, and/or known poses. Our project page is available at https://colin-de.github.io/DynSUP/.

本研究解决了在动态环境中使用仅有的两张无先验姿态图像进行高斯拟合这一问题。通过提出物体层级的双视图束调整和基于SE(3)场驱动的高斯训练方法，研究展示了在合成和真实场景上的高保真新视角合成，显著提高了动态场景的时间一致性和对象运动建模能力，对现有静态和已知姿态方法形成了有力的挑战。

DynSUP：来自非姿态图像对的动态高斯点云