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Unpaired image translation algorithms can be used for sim2real tasks, butmany fail to generate temporally consistent results. We present a new approachthat combines differentiable rendering with image translation to achievetemporal consistency over indefinite timescales, using surface consistencylosses and \emph{neural neural textures}. We call this algorithm TRITON(Texture Recovering Image Translation Network): an unsupervised, end-to-end,stateless sim2real algorithm that leverages the underlying 3D geometry of inputscenes by generating realistic-looking learnable neural textures. By settlingon a particular texture for the objects in a scene, we ensure consistencybetween frames statelessly. Unlike previous algorithms, TRITON is not limitedto camera movements -- it can handle the movement of objects as well, making ituseful for downstream tasks such as robotic manipulation.

Neural Neural Textures Make Sim2Real Consistent