CoNeRF: Controllable Neural Radiance Fields

Example novel attributes and view synthesis generated by our CoNeRF


We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attributes as latent variables that are regressed by the neural network given the scene encoding. This leads to a few-shot learning framework, where attributes are discovered automatically by the framework, when annotations are not provided. We apply our method to various scenes with different types of controllable attributes (e.g. expression control on human faces, or state control in movement of inanimate objects). Overall, we demonstrate, to the best of our knowledge, for the first time novel view and novel attribute re-rendering of scenes from a single video.

In 2022 Conference on Computer Vision and Pattern Recognition Proceedings
Kacper Kania
Kacper Kania
PhD Student

4th year PhD Student in Neural Rendering, supported by Microsoft Research PhD funding programme

Tomasz Trzciński
Tomasz Trzciński
Principal Investigator