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climatenerf climatenerf extreme weather synthesis in neural radiance field iccv 2023 yuan li 1 2 zhi hao lin 1 david forsyth 1 jia bin huang 3 shenlong wang 1 1 university of illinois at urbana champaign 2 zhejiang university 3 university of maryland college park equal contribution paper code abstract physical simulations produce excellent predictions of weather effects neural radiance fields produce sota scene models we describe a novel nerf editing procedure that can fuse physical simulations with nerf models of scenes producing realistic movies of physical phenomena in those scenes our application climate nerf allows people to visualize what climate change outcomes will do to them climatenerf allows us to render realistic weather effects including smog snow and flood results can be controlled with physically meaningful variables like water level qualitative and quantitative studies show that our simulated results are significantly more realistic than those from sota 2d image editing and sota 3d nerf stylization weather simulations you can select different weather conditions on different scenes and compare our method with baselines 3d stylization denotes finetuning pre trained ngp model using fastphotostyle weather flood snow smog scene playground family truck train horse garden kitti360_1 kitti360_2 kitti360_3 method ours climate gan 1 stable diffusion 2 swapping autoencoder 3 3d stylization rising sea level simulation controllable rendering our method simulates different densities of smog and distinct heights of flood and accumulated snow rendering procedure of climatenerf we first determine the position of physical entities smog particle snow balls water surface with physical simulation we can then render the scene with desired effects by modeling the light transport between the physical entities and the scene more specifically we follow the volume rendering process and fuse the estimated color and density from 1 the original radiance field by querying the trained instant ngp model and 2 the physical entities by physically based rendering our rendering procedure thus maintain the realism while achieving complex yet physically plausible visual effects user study we perform a user study to validate our approach quantitatively users are asked to watch pairs of synthesized images or videos of the same scene and pick the one with higher realism 37 users participated in the study and in total we collected 2664 pairs of comparisons images videos smog flood snow the length of bars indicates the percentage of users voting for higher realism than the opponents the green bar with the number shows our win rate against each baseline the video quality of our method significantly outperforms all baselines references victor schmidt alexandra sasha luccioni m ́elisande teng tianyu zhang alexia reynaud sunand raghupathi gautier cosne adrien juraver vahe vardanyan alex hernandez garcia yoshua bengio climategan raising climate change awareness by generating images of floods iclr 2022 code robin rombach andreas blattmann dominik lorenz patrick esser and bj ̈orn ommer high resolution image synthesis with latent diffusion models in cvpr 2022 code taesung park jun yan zhu oliver wang jingwan lu eli shechtman alexei efros and richard zhang swapping autoencoder for deep image manipulation neurips 2020 code citation if you find our project useful please consider citing aخa inproceedings li2023climatenerf title climatenerf extreme weather synthesis in neural radiance field author li yuan and lin zhi hao and forsyth david and huang jia bin and wang shenlong booktitle proceedings of the ieee cvf international conference on computer vision iccv year 2023 acknowledgements the website template was borrowed from michaël gharbi refnerf nerfies and semantic view synthesis f