Interactive landscape–scale cloud animation using DCGAN

Abstract

This article presents an interactive method for 3D cloud animation at the landscape scale by employing machine learning. To this end, we utilize deep convolutional generative adversarial network (DCGAN) on GPU for training on home-captured cloud videos and producing coherent animation frames. We limit the size of input images provided to DCGAN, thereby reducing the training time and yet producing detailed 3D animation frames. This is made possible through our preprocessing of the source videos, wherein several corrections are applied to the extracted frames to provide an adequate input training data set to DCGAN. A significant advantage of the presented cloud animation is that it does not require any underlying physics simulation. We present detailed results of our approach and verify its effectiveness using human perceptual evaluation. Our results indicate that the proposed method is capable of convincingly realistic 3D cloud animation, as perceived by the participants, without introducing too much computational overhead.

Deep Synthesis of Cloud Lighting

“…A different approach using transformers is developed by Chen et al [32] to generate HDR panoramas from a given text description. Goswami et al [33] produced a method that synthesized cloud animations in world space but relied on simplified volumetric cloud and lighting models.…”