Controllable Shadow Generation Using Pixel Height Maps

Shadows are essential for realistic image compositing. Physics-based shadow rendering methods require 3D geometries, which are not always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to an object's shadow without explicitly modeling the shadow geometry. Still, they lack control and are prone to visual artifacts. We introduce pixel heigh, a novel geometry representation that encodes the correlations between objects, ground, and camera pose. The pixel height can be calculated from 3D geometries, manually annotated on 2D images, and can also be predicted from a single-view RGB image by a supervised approach. It can be used to calculate hard shadows in a 2D image based on the projective geometry, providing precise control of the shadows' direction and shape. Furthermore, we propose a data-driven soft shadow generator to apply softness to a hard shadow based on a softness input parameter. Qualitative and quantitative evaluations demonstrate that the proposed pixel height significantly improves the quality of the shadow generation while allowing for controllability.Comment: 15 pages, 11 figure

Positioning articulated figures

Many animation systems rely on key-frames or poses to produce animated sequences of figures we interpret as articulated, e.g. the skeleton of a character. The production of poses is a difficult problem which can be solved by using techniques such as forward and inverse kinematics. However, animators often find these techniques difficult to work with. The work, presented in this thesis, proposes an innovative technique which approaches this problem from a totally different direction from conventional techniques, and is based on Interactive Genetic Algorithms (IGAs). IGAs are evolutionary tools based on the theory of evolution which was first described by Darwin in 1859. They are derived from Genetic Algorithms (GAs) themselves based on the theory of evolution. IGAs have been successfully used to produce abstract pictures, sculptures and abstract animation sequences. Conventional techniques assist the animator in producing poses. On the contrary, when working with IGAs, users assist the computer in its search for a good solution. Unfortunately, this concept is too weak to allow for an efficient exploration of the space of poses as the user requires more control over the evolutionary process. So, a new concept was introduced to let the user specify directly what is of interest, that is a limb or a set of limbs. This information is efficiently used by the computer to greatly enhance the search. Users build a pose by selecting limbs which are of interest. That pose is provided to the computer as a seed to produce a new generation of poses. The degree of similarity is specified directly by the user. Typically, it is small at the beginning and increases as the process reaches convergences. The power of this new technique is demonstrated by two evaluations, one which uses a set of non expert users and another one which uses myself as the sole but expert user. The first evaluation highlighted the high cognitive requirement of the new technique whereas the second evaluation showed that given sufficient training, the new technique becomes much faster than the other two conventional techniques