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.

我们介绍了一种新颖的方法，可以从单目捕捉中进行动态自由视图合成，为观看体验带来沉浸感。我们的方法基于对复杂静态场景进行忠实重建的 3D 高斯点插值（3DGS）的最新进展。通过利用环境动力学的周期性来学习运动轨迹模型，并结合谨慎的正则化来克服以往将 3DGS 扩展到表示动态时遇到的局限性，这些局限性包括仅适用于有界场景或需要多摄像机捕捉，并且通常无法推广到未见过的运动，限制了它们的实际应用。我们还提出了一些重要的实用策略，以提高基线 3DGS 静态重建的视觉质量，并提高 GPU 内存密集学习的内存效率，展示了多个环境自然场景的高质量照片逼真的新视图合成，其中包含复杂的纹理和精细的结构元素。