Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry.
Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain; we showcase a semantic segmentation, a normal map, and a stylization.
Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape.
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion.
We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments.
The estimation of the optical properties of a material from RGB-images is an important but extremely ill-posed problem in Computer Graphics.
Video capture is limited by the trade-off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system.
In this paper, we use transfer learning techniques to propose a new classification network with better performance in illustration images.