NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
Dakshit Agrawal Jiajie Xu Siva Karthik Mustikovela Ioannis Gkioulekas Ashish Shrivastava Yuning Chai
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.