Animation production workflows often involve digital colorization of line art, where small unpainted regions (“gaps”) frequently occur and remain an underexplored challenge. We conducted a formative study in Japanese animation (anime) pipelines and found that while the paint bucket tool is widely used for base coloring, tiny enclosed areas are frequently overlooked, resulting in time-consuming manual detection and filling. We introduce GapFill, a tool grounded in professional practices that reduces the effort of gap detection, zooming, and color selection. Our deep-learning method suggests appropriate fill colors by referencing surrounding regions, leveraging the flat-color nature of anime-style images. In a user study with 13 professional colorists, our system improved performance and usability in gap-filling tasks over conventional methods. The study also suggested that prediction accuracy alone is not the primary factor for usability, that appropriate colors can be contextually ambiguous, and that GapFill can complement existing tools depending on users’ trust in new AI-powered assistance.

Abstract This paper proposes t‐Regina, a novel iterative scheme that automates the manipulation of grid handles in free‐form deformation (FFD) for deformable registrations of cartoon character sprites. First, we build a data‐driven FFD (dFFD) that enables users to handle locations of FFD handles from lower‐dimensional parameters. To prepare training data, we implement an FFD‐based puppet tool and recorded the user‐designed locations of grid handles. Second, we iteratively optimize the parameters of dFFD by using a locally optimal block matching algorithm with almost‐uniform sampling strategies. t‐Regina is effective and easy to integrate into existing drawing systems. This paper shows some examples of deformation results to demonstrate the robustness of t‐Regina.

Abstract This paper proposes a method to estimate the locations of grid handles in free-form deformation (FFD) while preserving the local shape characteristics of the 2D/3D input model embedded into the grid, named locality-preserving FFD (lp-FFD). Users first specify some vertex locations in the input model and grid handle locations. The system then optimizes all locations of grid handles by minimizing the distortion of the input model’s mesh elements. The proposed method is fast and stable, allowing the user to directly and indirectly make the deformed shape of the mesh model and grid. This paper shows some examples of deformation results to demonstrate the robustness of our lp-FFD. In addition, we conducted a user study and confirm our lp-FFD’s efficiency and effectiveness in shape deformation is higher than those of existing methods used in commercial software.

The automatic colorization of anime line drawings is a challenging problem in production pipelines. Recent advances in deep neural networks have addressed this problem; however, collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines. To overcome this obstacle, we propose a new patch-based learning method for few-shot anime-style colorization. The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings. We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists. The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily incorporated within existing production pipelines. We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.

We introduce an interactive method to sequentially find viewpoints for constructing view-dependent models which represent view-specific deformations in classic 2D cartoons [Chaudhuri et al. 2004, 2007; Koyama and Igarashi 2013; Rademacher 1999]. As users design one view-specific model from a single-fixed viewpoint, the system searches successive viewpoints for subsequent modeling and instantly jumps to the next viewpoints. Thereby, the users can efficiently repeat the design process of view-specific deformations until they are satisfied. This method is simple enough to easily implement in an existing modeling system. We conduct a user study with novice and amateur users and confirm that the proposed system is effective for designing view-specific models envisioned by the users.

We propose a new automatic head modeling system to generate individualized head models which can express person-specific facial expressions. The head modeling system consists of two core processes. The head modeling process with the proposed automatic mesh completion generates a whole head model only from facial range scan data. The key shape generation process generates key shapes for the generated head model based on physics-based facial muscle simulation with an individual muscle layout estimated from subject's facial expression videos. Facial animations considering personal characteristics can be synthesized using the individualized head model and key shapes. Experimental results show that the proposed system can generate head models where 84% of subjects can identify themselves. Therefore, we conclude that our head modeling system is effective to games and entertainment systems like a Future Cast System.

In this paper, we first present a new audience-participating movie form in which 3D virtual characters of audiences are constructed by computer graphics (CG) technologies and are embedded into a in a pre-rendered movie as different roles. Then, we investigate how the audiences respond to these virtual characters using physiological and subjective evaluation methods. To facilitate the investigation, we present three versions of a movie to an audience—a Traditional version, its SDIM version with the participation of the audience’s virtual character, and its SFDIM version with the co-participation of the audience and her/his friends’ virtual characters. The subjective evaluation results show that the participation of virtual characters indeed causes increased subjective sense of spatial presence and engagement, and emotional reaction; moreover, SFDIM performs significantly better than SDIM, due to the co-participation of friends’ virtual characters. Also, we find that the audiences experience not only significantly different galvanic skin response (GSR) changes on average—changing trend over time and number of fluctuations—but they also show the increased phasic GSR responses to the appearance of their own or friends’ virtual 3D characters on the screen. The evaluation results demonstrate the success of the new audience-participating movie form and contribute to understanding how people respond to virtual characters in a role-playing entertainment interface.

In this paper we subjectively and physiologically investigate the effects of the audiences' 3D virtual actor in a movie on their movie experience, using the audience-participating movie DIM as the object of study. In DIM, the photo-realistic 3D virtual actors of audience are constructed by combining current computer graphics (CG) technologies and can act different roles in a pre-rendered CG movie. To facilitate the investigation, we presented three versions of a CG movie to an audience---a Traditional version, its Self-DIM (SDIM) version with the participation of the audience's virtual actor, and its Self-Friend-DIM (SFDIM) version with the co-participation of the audience and his friends' virtual actors. The results show that the participation of audience's 3D virtual actors indeed cause increased subjective sense of presence and engagement, and emotional reaction; moreover, SFDIM performs significantly better than SDIM, due to increased social presence. Interestingly, when watching the three movie versions, subjects experienced not only significantly different galvanic skin response (GSR) changes on average---changing trend over time, and number of fluctuations---but they also experienced phasic GSR increase when watching their own and friends' virtual 3D actors appearing on the movie screen. These results suggest that the participation of the 3D virtual actors in a movie can improve interaction and communication between audience and the movie.