High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks
ECCV 2018 workshop paper ##內容： 利用GAN來模擬參數化的人體紋理空間，通過學習每個紋理的最佳3DMM係數來生成相應的面部幾何圖形。生成的圖像被映射到相應的幾何圖形上，以獲得新的高分辨率三維面。 ##3DMM缺陷： 在3D人臉的背景下，最常用的幾何圖形和紋理的表示和合成模型之一是線性表示的3DMM，3DMM能夠提供它所構成的各種數據的新樣本。然而，由於生成模型服從高斯分
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相關文章
- 1. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks論文筆記
- 2. Generative Adversarial Network (GAN) papers (2018.2.4 更新）
- 3. Generative Adversarial Text to Image Synthesis
- 4. [Style Transfer]——Blood Vessel Geometry Synthesis using Generative Adversarial Networks
- 5. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
- 6. Generative Adversarial Nets[CycleGAN]
- 7. Graphical Generative Adversarial Networks
- 8. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 9. Triangle Generative Adversarial Networks
- 10. Contrastive Generative Adversarial Networks