Computer-assisted cel animation: post-processing after inbetweening

Although 2D animation production today has become more efficient with the help of improved computer technology, it is still very labor intensive. The less creative inking/painting and inbetweening processes take a significant portion of the total work. Our Computer-assisted Cel Animation (CACAni) research aims at automating the above two processes to assist animators in producing their work faster and with better quality. In this paper, a post-processing algorithm for generating inbetween frames is proposed. Algorithms for generating inbetweenings, particularly, the Feature-based Matching (MFBA) of keyframes and linear inbetween frame generation are also introduced. Images constructed with MFBA may have gaps (artefacts) due to the lack of smoothness among adjacent displacement vectors as well as the discrete feature of rasterization. With the guidance of displacement vectors, our algorithm classifies the gaps into several categories and handles them accordingly. The results are compared with those processed by filtering method to show the superiority of the new algorithm.

[1]  J Jobson Daniel,et al.  Retinex Image Processing: Improved Fidelity to Direct Visual Observation , 1996 .

[2]  Jean-Daniel Fekete,et al.  TicTacToon: a paperless system for professional 2D animation , 1995, SIGGRAPH.

[3]  Keith Waters,et al.  Computer facial animation , 1996 .

[4]  Bernd Jähne,et al.  Digital Image Processing: Concepts, Algorithms, and Scientific Applications , 1991 .

[5]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[6]  Donald P. Greenberg,et al.  Efficient Rendering and Compression for Full-Parallex Computer-Generated Holographic Stereograms , 2000 .

[7]  Philip J. Willis,et al.  Computer Assisted Animation: 2D or not 2D? , 1994, Comput. J..

[8]  Loren C. Carpenter,et al.  The A -buffer, an antialiased hidden surface method , 1984, SIGGRAPH.

[9]  Markus H. Gross,et al.  An approach to computer-supported cartooning , 2005, The Visual Computer.

[10]  Ronald Fedkiw,et al.  Visual simulation of smoke , 2001, SIGGRAPH.

[11]  Zia-ur Rahman,et al.  Retinex Image Processing: Improved Fidelity To Direct Visual Observation , 1996, CIC.

[12]  N. Burtnyk,et al.  Computer-Generated Key-Frame Animation , 1971 .

[13]  Charles X. Durand,et al.  The "TOON" project: Requirements for a computerized 2D animation system , 1991, Comput. Graph..

[14]  Seah Hock Soon,et al.  Computer-assisted coloring by matching line drawings , 2000, The Visual Computer.

[15]  Marc Levoy,et al.  The digital Michelangelo project: 3D scanning of large statues , 2000, SIGGRAPH.

[16]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..

[17]  Kikuo Fujimura,et al.  Shape similarity by homotopic deformation , 2000, The Visual Computer.

[18]  Marc Levoy The Digital Michelangelo Project , 1999, Comput. Graph. Forum.

[19]  Bernd Jähne,et al.  Digital image processing (3rd ed.): concepts, algorithms, and scientific applications , 1995 .

[20]  Thomas S. Huang,et al.  Motion and Structure from Image Sequences , 1992 .

[21]  Marc Levoy A color animation system: based on the multiplane technique , 1977, SIGGRAPH '77.

[22]  Donald P. Greenberg,et al.  Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments , 2001, TOGS.

[23]  David J. Heeger,et al.  Optical flow using spatiotemporal filters , 2004, International Journal of Computer Vision.

[24]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[25]  Ronald Baecker,et al.  Picture-driven animation , 1899, AFIPS '69 (Spring).