Novel Toolset for 2D Drawing and Animation
Gioacchino Noris (Disney Research Zurich)
Markus Gross (Disney Research Zurich)
Bob Sumner (Disney Research Zurich)
Alexander Sorkine-Hornung (Disney Research Zurich)
Stelian Coros (Disney Research Zurich)
Daniel Sykora (Czech Technical University in Prague)
Brian Whited (Walt Disney Animation Studios)
Maryann Simmons (Walt Disney Animation Studios)
This project investigates a set of novel digital tools for 2D Animation addressing the shortcomings of current digital support, representations and algorithms. Our goal is to produce animation tools that are intuitive to use, allow full control over the resulting drawing when desired, and provide the artist with immediate visual feedback of the animation as it progresses.
In contrast to previous work, where automation has been often been the goal, we focus on building tools that keep the artist as a central agent. We target automation of the most tedious tasks where the need for artistic interpretation is minimal, and otherwise aim for computer-assisted solutions geared to provide a similar experience as with traditional workflows augmented with algorithmic computation.
We start by investigating the problem of representing drawings digitally, analyzing existing representations, and highlighting their major shortcomings. We then present a hybrid representation that combines the advantages of vector and raster images, and propose the use of a novel vector description for lines and areas.
We then address the problem of vectorization of line drawings. This problem is challenging due to ambiguities in regions where lines are drawn close to each other or intersect. We propose a two-step, topology-driven approach that first exploits the pixel gradient information in a clustering process to generate an initial stroke graph from which the topology of the drawing is learned, and then applies a “reverse drawing” procedure where plausible junction configurations are considered and a heuristic optimum is selected.
Segmentation is a key step in organizing digital drawings into semantic groups ready for editing and animation. Done manually, this can be a very labor intensive task. We propose a scribble-based interface that guides a novel energy minimization resulting in the labeling of the drawing strokes. In contrast to previous methods, we exploit both geometric and temporal information available with modern drawing devices.
In the realm of applications, we address the task of inbetweening, which is the creation of animation frames between pairs of key frames in order to create the illusion of a continuous animation. Drawings are represented as stroke graphs. Given two input key frames, a mapping between the graphs is derived, and spiral trajectories for graph nodes and additional salient points are computed. Strokes are then interpolated, leading to an initial set of inbetween frames. We propose a set of tools to modify the mapping, deal with simple topological mismatches, and redraw animation trajectories.
Finally, we propose a technique to control temporal noise in sketchy animation. Sequences of sketches typically present notable temporal artifacts in the form of visual flickering due to the lack of temporal consistency in the way sketched lines vary from the visually perceived boundaries and interior lines. We propose a two-step method that applies a temporal filter bi-linearly. By combining motion extraction, stroke correspondence, and inbetweening, temporal consistency can be enforced at the stroke level. We first apply this to selected key frames in the input animation to generate a so-called “noise free” sequence, and then to pairs of frames from the input sequence and the noise free sequence to obtain the desired temporal noise level specified by the user.