NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows

Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao, Bowen Wen, Jonathan Tremblay, Stan Birchfield, Alexander Schwing; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 10293-10303

We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene. Our method defines the transformation as a 3D flowspecifically as a weighted linear blending of rigid transformations of 3D anchor points that are defined on the surface of the scene. In order to identify anchor points we introduce a novel correspondence algorithm that first matches RGB-based pairs then leverages multi-view information and 3D reprojection to robustly filter false positives in two steps. We also introduce a new dataset for exploring the problem of modifying a NeRF scene through a single observation. Our dataset contains 113 scenes leveraging 47 3D assets.We show that our proposed method outperforms NeRF editing methods as well as diffusion-based methods and we also explore different methods for filtering correspondences.