While 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 graph-cut 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.

By extending from monocular displays to binocular displays, one additional image domain is introduced. Existing binocular display systems only utilize this additional image domain for stereopsis. Our human vision is not only able to fuse two displaced images, but also two images with difference in detail, contrast and luminance, up to a certain limit. This phenomenon is known as binocular single vision . Humans can perceive more visual content via binocular fusion than just a linear blending of two views. In this paper, we make a first attempt in computer graphics to utilize this human vision phenomenon, and propose a binocular tone mapping framework. The proposed framework generates a binocular low-dynamic range (LDR) image pair that preserves more human-perceivable visual content than a single LDR image using the additional image domain. Given a tone-mapped LDR image (left, without loss of generality), our framework optimally synthesizes its counterpart (right) in the image pair from the same source HDR image. The two LDR images are different, so that they can aggregately present more human-perceivable visual richness than a single arbitrary LDR image, without triggering visual discomfort . To achieve this goal, a novel binocular viewing comfort predictor (BVCP) is also proposed to prevent such visual discomfort. The design of BVCP is based on the findings in vision science. Through our user studies, we demonstrate the increase of human-perceivable visual richness and the effectiveness of the proposed BVCP in conservatively predicting the visual discomfort threshold of human observers.

The wide availability and popularity of text-based communication channels encourage the usage of ASCII art in representing images. Existing tone-based ASCII art generation methods lead to halftone-like results and require high text resolution for display, as higher text resolution offers more tone variety. This paper presents a novel method to generate structure-based ASCII art that is currently mostly created by hand. It approximates the major line structure of the reference image content with the shape of characters. Representing the unlimited image content with the extremely limited shapes and restrictive placement of characters makes this problem challenging. Most existing shape similarity metrics either fail to address the misalignment in real-world scenarios, or are unable to account for the differences in position, orientation and scaling. Our key contribution is a novel alignment-insensitive shape similarity (AISS) metric that tolerates misalignment of shapes while accounting for the differences in position, orientation and scaling. Together with the constrained deformation approach, we formulate the ASCII art generation as an optimization that minimizes shape dissimilarity and deformation . Convincing results and user study are shown to demonstrate its effectiveness.