We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene. Compared to prior work, our framework significantly reduces blending artifacts when inserting multiple dynamic objects into a 3D scene at novel views and times; achieves comparable PSNR without the need for additional ground truth modalities like optical flow; and overall provides ease, flexibility, and scalability in neural composition. Our codebase is on GitHub.

翻译：我们提出了一种新颖视角增强（NOVA）策略，用于训练NeRF实现对静态场景中动态对象的照片级真实感三维组合。与先前工作相比，我们的框架在将多个动态对象插入到三维场景的新颖视角和不同时间点时，显著减少了混合伪影；在无需光流等额外真实模态信息的情况下，实现了可比的峰值信噪比；总体上为神经组合提供了简便性、灵活性和可扩展性。我们的代码库已在GitHub上公开。