We introduce an unsupervised GANbased model for shading photorealistic hair animations.Our model is much faster than previous rendering algorithms and produces fewer artifacts than other neural image translation metho...We introduce an unsupervised GANbased model for shading photorealistic hair animations.Our model is much faster than previous rendering algorithms and produces fewer artifacts than other neural image translation methods.The main idea is to extend the Cycle-GAN structure to avoid semitransparent hair appearance and to exactly reproduce the interaction of the lights with the scene.We use two constraints to ensure temporal coherence and highlight stability.Our approach outperforms and is computationally more efficient than previous methods.展开更多
基金partially supported by JSPS KAKENHI,Grant Number JP19K11990。
文摘We introduce an unsupervised GANbased model for shading photorealistic hair animations.Our model is much faster than previous rendering algorithms and produces fewer artifacts than other neural image translation methods.The main idea is to extend the Cycle-GAN structure to avoid semitransparent hair appearance and to exactly reproduce the interaction of the lights with the scene.We use two constraints to ensure temporal coherence and highlight stability.Our approach outperforms and is computationally more efficient than previous methods.
