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
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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%.
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