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In this work, we aim to address the 3D scene stylization problem - generatingstylized images of the scene at arbitrary novel view angles. A straightforwardsolution is to combine existing novel view synthesis and image/video styletransfer approaches, which often leads to blurry results or inconsistentappearance. Inspired by the high-quality results of the neural radiance fields(NeRF) method, we propose a joint framework to directly render novel views withthe desired style. Our framework consists of two components: an implicitrepresentation of the 3D scene with the neural radiance fields model, and ahypernetwork to transfer the style information into the scene representation.In particular, our implicit representation model disentangles the scene intothe geometry and appearance branches, and the hypernetwork learns to predictthe parameters of the appearance branch from the reference style image. Toalleviate the training difficulties and memory burden, we propose a two-stagetraining procedure and a patch sub-sampling approach to optimize the style andcontent losses with the neural radiance fields model. After optimization, ourmodel is able to render consistent novel views at arbitrary view angles witharbitrary style. Both quantitative evaluation and human subject study havedemonstrated that the proposed method generates faithful stylization resultswith consistent appearance across different views.

Stylizing 3D Scene via Implicit Representation and HyperNetwork