Approximately 250 million people suffer from color vision deficiency (CVD). They can hardly share the same visual content with normal-vision audiences. In this paper, we propose the first system that allows CVD and normal-vision audiences to share the same visual content simultaneously. The key that we can achieve this is because the ordinary stereoscopic display (non-autostereoscopic ones) offers users two visual experiences (with and without wearing stereoscopic glasses). By allocating one experience to CVD audiences and one to normal-vision audiences, we allow them to share. The core problem is to synthesize an image pair, that when they are presented binocularly, CVD audiences can distinguish the originally indistinguishable colors; and when it is in monocular presentation, normal-vision audiences cannot distinguish its difference from the original image. We solve the image-pair recoloring problem by optimizing an objective function that minimizes the color deviation for normal-vision audiences, and maximizes the color distinguishability and binocular fusibility for CVD audiences. Our method is extensively evaluated via multiple quantitative experiments and user studies. Convincing results are obtained in all our test cases.

Figure 1: Stereoscopization of a cel animation. Our method takes an ordinary 2D cel animation (top row) as input, infers the temporalconsistent ordering, and synthesizes the per-frame depth maps (middle row), in order to generate a stereoscopic cel animation (bottom row, presented in the form of anaglyphs). This sequence has 12 frames (1920 × 1080). The frame containing the maximal number of regions has 82 regions. In our experiment, depth ordering takes 12 minutes, and depth synthesis takes 9.6 minutes. AbstractWhile hand-drawn cel animation is a world-wide popular form of art and entertainment, introducing stereoscopic effect into it remains difficult and costly, due to the lack of physical clues. In this paper, we propose a method to synthesize convincing stereoscopic cel animations from ordinary 2D inputs, without labor-intensive manual depth assignment nor 3D geometry reconstruction. It is mainly automatic due to the need of producing lengthy animation sequences, but with the option of allowing users to adjust or constrain all intermediate results. The system fits nicely into the existing production flow of cel animation. By utilizing the T-junction cue available in cartoons, we first infer the initial, but not reliable, ordering of regions. One of our major contributions is to resolve the temporal inconsistency of ordering by formulating it as a graphcut problem. However, the resultant ordering remains insufficient for generating convincing stereoscopic effect, as ordering cannot be directly used for depth assignment due to its discontinuous nature. We further propose to synthesize the depth through an optimization process with the ordering formulated as constraints. This is our second major contribution. The optimized result is the spatiotemporally smooth depth for synthesizing stereoscopic effect. Our method has been evaluated on a wide range of cel animations and convincing stereoscopic effect is obtained in all cases.

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Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays, with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editing, including deformation, colorization, etc. To perform such editing, it is essential to extract the structure lines within cartoon images. Traditional edge detection methods are mainly based on gradients. These methods perform poorly in the face of compression artifacts and spatially-varying line colors, which cause gradient values to become unreliable. This paper presents the first approach to extract structure lines in cartoons based on regions. Our method starts by segmenting an image into regions, and then classifies them as edge regions and non-edge regions. Our second main contribution comprises three measures to estimate the likelihood of a region being a non-edge region. These measure darkness, local contrast, and shape. Since the likelihoods become unreliable as regions become smaller, we further classify regions using both likelihoods and the relationships to neighboring regions via a graph-cut formulation. Our method has been evaluated on a wide variety of cartoon images, and convincing results are obtained in all cases.