Skeleton-Driven Inbetweening of Bitmap Character Drawings

One of the primary reasons for the high cost of traditional animation is the inbetweening process, where artists manually draw each intermediate frame necessary for smooth motion. Making this process more efficient has been at the core of computer graphics research for years, yet the industry has adopted very few solutions. Most existing solutions either require vector input or resort to tight inbetweening; often, they attempt to fully automate the process. In industry, however, keyframes are often spaced far apart, drawn in raster format, and contain occlusions. Moreover, inbetweening is fundamentally an artistic process, so the artist should maintain high-level control over it. We address these issues by proposing a novel inbetweening system for bitmap character drawings, supporting both tight and far inbetweening. In our setup, the artist can control motion by animating a skeleton between the keyframe poses. Our system then performs skeleton-based deformation of the bitmap drawings into the same pose and employs discrete optimization and deep learning to blend the deformed images. Besides the skeleton and the two drawn bitmap keyframes, we require very little annotation. However, deforming drawings with occlusions is complex, as it requires a piecewise smooth deformation field. To address this, we observe that this deformation field is smooth when the drawing is lifted into 3D. Our system therefore optimizes topology of a 2.5D partially layered template that we use to lift the drawing into 3D and get the final piecewise-smooth deformaton, effectively resolving occlusions. We validate our system through a series of animations, qualitative and quantitative comparisons, and user studies, demonstrating that our approach consistently outperforms the state of the art and our results are consistent with the viewers' perception. Code and data for our paper are available at http://www-labs.iro.umontreal.ca/~bmpix/inbetweening/.

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Publication Details

Journal
ACM Transactions on Graphics
Published
2024-11-19
DOI
https://doi.org/10.1145/3687955
Citations
3
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.75

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article

Skeleton-Driven Inbetweening of Bitmap Character Drawings

Mikhail Bessmeltsev, Kirill Brodt
3 citations
ACM Transactions on Graphics
Handwritten Text Recognition Techniques
0.75
article

Skeleton-Driven Inbetweening of Bitmap Character Drawings

Mikhail Bessmeltsev, Kirill Brodt
article en
3 citations

Abstract

One of the primary reasons for the high cost of traditional animation is the inbetweening process, where artists manually draw each intermediate frame necessary for smooth motion. Making this process more efficient has been at the core of computer graphics research for years, yet the industry has adopted very few solutions. Most existing solutions either require vector input or resort to tight inbetweening; often, they attempt to fully automate the process. In industry, however, keyframes are often spaced far apart, drawn in raster format, and contain occlusions. Moreover, inbetweening is fundamentally an artistic process, so the artist should maintain high-level control over it. We address these issues by proposing a novel inbetweening system for bitmap character drawings, supporting both tight and far inbetweening. In our setup, the artist can control motion by animating a skeleton between the keyframe poses. Our system then performs skeleton-based deformation of the bitmap drawings into the same pose and employs discrete optimization and deep learning to blend the deformed images. Besides the skeleton and the two drawn bitmap keyframes, we require very little annotation. However, deforming drawings with occlusions is complex, as it requires a piecewise smooth deformation field. To address this, we observe that this deformation field is smooth when the drawing is lifted into 3D. Our system therefore optimizes topology of a 2.5D partially layered template that we use to lift the drawing into 3D and get the final piecewise-smooth deformaton, effectively resolving occlusions. We validate our system through a series of animations, qualitative and quantitative comparisons, and user studies, demonstrating that our approach consistently outperforms the state of the art and our results are consistent with the viewers' perception. Code and data for our paper are available at http://www-labs.iro.umontreal.ca/~bmpix/inbetweening/.

ACM Transactions on GraphicsVol. 43(6)
Université de Montréal (CA)
Natural Sciences and Engineering Research Council of Canada
Industry, innovation and infrastructure
Openalex Percentile: Top 27%
Handwritten Text Recognition Techniques
0.75
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