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Interpreting and Generating Artistic Depictions: Applications to Sketch-Based Modeling and Video Stylization. (Interprétation et Génération de Représentations Artistiques: Applications à la Modélisation par le Dessin et à la Stylisation de Vidéos)

Digital tools brings new ways of creation, for accomplished artists as well as for any individual willing to create. In this thesis, I am interested in two different aspects in helping artists: interpreting their creation and generating new content. I first study how to interpret a sketch as a 3D object. We propose a data-driven approach that tackles this challenge by training deep convolutional neural networks (CNN) to predict occupancy of a voxel grid from a line drawing. We integrate our CNNs in an interactive modeling system that allows users to seamlessly draw an object, rotate it to see its 3D reconstruction, and refine it by re-drawing from another vantage point using the 3D reconstruction as guidance. We then complement this technique with a geometric method that allows to refine the quality of the final object. To do so, we train an additional CNN to predict higher resolution normal maps from each input view. We then fuse these normal maps with the voxel grid prediction by optimizing for the final surface. We train all of these networks by rendering synthetic contour drawings from procedurally generated abstract shapes. In a second part, I present a method to generate stylized videos with a look reminiscent of traditional 2D animation. Existing stylization methods often retain the 3D motion of the original video, making the result look like a 3D scene covered in paint rather than a 2D painting of a scene. Inspired by cut-out animation, we propose to modify the motion of the sequence so that it is composed of 2D rigid motions. To achieve this goal, our approach applies motion segmentation and optimization to best approximate the input optical flow with piecewise-rigid transforms, and re-renders the video such that its content follows the simplified motion. Applying existing stylization algorithm to the new sequence produce a stylized video more similar to 2D animation. Although the two parts of my thesis lean on different methods, they both rely on traditional techniques used by artists: either by understanding how they draw objects or by taking inspiration from how they simplify the motion in 2D animation.