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We present a neural rendering framework that maps a voxelized scene into ahigh quality image. Highly-textured objects and scene element interactions arerealistically rendered by our method, despite having a rough representation asan input. Moreover, our approach allows controllable rendering: geometric andappearance modifications in the input are accurately propagated to the output.The user can move, rotate and scale an object, change its appearance andtexture or modify the position of the light and all these edits are representedin the final rendering. We demonstrate the effectiveness of our approach byrendering scenes with varying appearance, from single color per object tocomplex, high-frequency textures. We show that our rerendering network cangenerate very detailed images that represent precisely the appearance of theinput scene. Our experiments illustrate that our approach achieves moreaccurate image synthesis results compared to alternatives and can also handlelow voxel grid resolutions. Finally, we show how our neural rendering frameworkcan capture and faithfully render objects from real images and from a diverseset of classes.

Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool