High Frame Rate Photorealistic Flame Rendering via Generative Adversarial Networks

Publication Type:
Conference Proceeding
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 2391-2396
Issue Date:
Filename Description Size
08914043.pdfPublished version288.5 kB
Adobe PDF
Full metadata record
In this paper we propose accelerating live rendering of flame using generative adversarial neural networks. The proposed method targets entertainment and simulation-based training industries whose demands for high fidelity and high frame rate increases steadily. The proposed approach takes image frames rendered with low voxel resolution (8 × 8 × 8 voxels at 90 FPS) and produces image frames equivalent to imagery produced from high voxel resolution (64 × 64 × 64 voxels) typically rendered at 3 FPS. The error was evaluated using the structural similarity image metric (SSIM). The average error between generated image frames and the ground truth recorded 92:7%±4:6%.
Please use this identifier to cite or link to this item: