We introduce a novel method for dynamic free-view synthesis of an ambient scenes from a monocular capture bringing a immersive quality to the viewing experience. Our method builds upon the recent advancements in 3D Gaussian Splatting (3DGS) that can faithfully reconstruct complex static scenes. Previous attempts to extend 3DGS to represent dynamics have been confined to bounded scenes or require multi-camera captures, and often fail to generalize to unseen motions, limiting their practical application. Our approach overcomes these constraints by leveraging the periodicity of ambient motions to learn the motion trajectory model, coupled with careful regularization. We also propose important practical strategies to improve the visual quality of the baseline 3DGS static reconstructions and to improve memory efficiency critical for GPU-memory intensive learning. We demonstrate high-quality photorealistic novel view synthesis of several ambient natural scenes with intricate textures and fine structural elements.

翻译：我们提出了一种新颖的方法，用于从单目拍摄中实现环境场景的动态自由视角合成，从而为观看体验带来沉浸式品质。我们的方法建立在3D高斯泼溅（3DGS）的最新进展之上，该技术能够忠实地重建复杂的静态场景。先前将3DGS扩展以表示动态场景的尝试局限于有界场景或需要多相机拍摄，并且通常难以泛化到未见过的运动，限制了其实际应用。我们的方法通过利用环境运动的周期性来学习运动轨迹模型，并结合仔细的正则化，克服了这些限制。我们还提出了重要的实用策略，以提高基线3DGS静态重建的视觉质量，并改进对GPU内存密集型学习至关重要的内存效率。我们展示了多个具有复杂纹理和精细结构元素的环境自然场景的高质量、逼真的新视角合成。