Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time presents a significant challenge due to the inherent complexity and temporal dynamics involved. While recent advancements in neural implicit models and dynamic Gaussian Splatting have shown promise, limitations persist, particularly in accurately capturing the underlying geometry of highly dynamic scenes. Some approaches address this by incorporating strong semantic and geometric priors through diffusion models. However, we explore a different avenue by investigating the potential of regularizing the native warp field within the dynamic Gaussian Splatting framework. Our method is grounded on the key intuition that an accurate warp field should produce continuous space-time motions. While enforcing the motion constraints on warp fields is non-trivial, we show that we can exploit knowledge innate to the forward warp field network to derive an analytical velocity field, then time integrate for scene flows to effectively constrain both the 2D motion and 3D positions of the Gaussians. This derived Lucas-Kanade style analytical regularization enables our method to achieve superior performance in reconstructing highly dynamic scenes, even under minimal camera movement, extending the boundaries of what existing dynamic Gaussian Splatting frameworks can achieve.
从2D图像重建动态3D场景并随时间生成多样化视图，由于涉及的固有复杂性和时间动态性，这一任务面临着重大挑战。尽管最近在神经隐式模型和动态高斯喷溅技术方面的进展显示出前景，但在准确捕获高动态场景的底层几何结构方面，仍存在局限性。一些方法通过融入强语义和几何先验的扩散模型来解决这一问题。然而，我们探索了一条不同的道路，即通过调查在动态高斯喷溅框架内规范本机扭曲场的潜力。我们的方法基于这样一个核心直觉：一个准确的扭曲场应该产生连续的时空运动。尽管在扭曲场上执行运动约束并非易事，我们展示了如何利用前向扭曲场网络固有的知识来推导出一个解析速度场，然后进行时间积分以有效地约束高斯的2D运动和3D位置。这种派生的Lucas-Kanade风格解析规范使我们的方法在重建高动态场景方面实现了卓越的性能，即使在相机运动最小的情况下，也扩展了现有动态高斯喷溅框架所能达到的边界。