2 research outputs found
Photo2Relief: Let Human in the Photograph Stand Out
In this paper, we propose a technique for making humans in photographs
protrude like reliefs. Unlike previous methods which mostly focus on the face
and head, our method aims to generate art works that describe the whole body
activity of the character. One challenge is that there is no ground-truth for
supervised deep learning. We introduce a sigmoid variant function to manipulate
gradients tactfully and train our neural networks by equipping with a loss
function defined in gradient domain. The second challenge is that actual
photographs often across different light conditions. We used image-based
rendering technique to address this challenge and acquire rendering images and
depth data under different lighting conditions. To make a clear division of
labor in network modules, a two-scale architecture is proposed to create
high-quality relief from a single photograph. Extensive experimental results on
a variety of scenes show that our method is a highly effective solution for
generating digital 2.5D artwork from photographs.Comment: 10 pages, 11 figure
Bas-relief modelling from enriched detail and geometry with deep normal transfer
Detail-and-geometry richness is essential to bas-relief modelling. However, existing image-based and model-based bas-relief modelling techniques commonly suffer from detail monotony or geometry loss. In this paper, we introduce a new bas-relief modelling framework for detail abundance with visual attention based mask generation and geometry preservation, which benefits from our two key contributions. For detail richness, we propose a novel semantic neural network of normal transfer to enrich the texture styles on bas-reliefs. For geometry preservation, we introduce a normal decomposition scheme based on Domain Transfer Recursive Filter (DTRF). Experimental results demonstrate that our approach is advantageous on producing bas-relief modellings with both fine details and geometry preservation