Cart
Create Account
Sign In
Collection:
Accurate reconstruction of real-world materials' appearance from a very limited number of samples is still a huge challenge in computer vision and graphics. In this paper, we present a novel deep architecture, Disentangled Generative Adversarial Network (
- IEEE MemberUS $11.00
- Society MemberUS $0.00
- IEEE Student MemberUS $11.00
- Non-IEEE MemberUS $15.00
Videos in this product
Dgan: Disentangled Representation Learning For Anisotropic Brdf Reconstruction
Accurate reconstruction of real-world materials' appearance from a very limited number of samples is still a huge challenge in computer vision and graphics. In this paper, we present a novel deep architecture, Disentangled Generative Adversarial Network (