NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

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...

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Hauptverfasser: Agrawal, Dakshit, Xu, Jiajie, Mustikovela, Siva Karthik, Gkioulekas, Ioannis, Shrivastava, Ashish, Chai, Yuning
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Computer Vision and Pattern Recognition
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Beschreibung
Zusammenfassung: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.
DOI:10.48550/arxiv.2308.12560