Motion inbetweening with score-based diffusion models
Title: |
Motion inbetweening with score-based diffusion models |
DNr: |
Berzelius-2022-160 |
Project Type: |
LiU Berzelius |
Principal Investigator: |
Rajmund Nagy <rajmundn@kth.se> |
Affiliation: |
Kungliga Tekniska högskolan |
Duration: |
2022-08-18 – 2023-03-01 |
Classification: |
10201 |
Keywords: |
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Abstract
In recent years, score-based diffusion models have proven to be very strong generative models in several domains, for example text-based image generation. However, they have not been applied to character-animation problems yet. With this project, we aim to develop the first score-based diffusion model that can generate the missing frames of a character animation sequence, a problem known as "inbetweening" in animation. Based on the success of diffusion models in other domains, we expect to significantly push the state-of-the-art forward, in terms of motion quality and temporal coherence, seeing that most current inbetweening models can only generate 1 second of animation. Furthermore, we plan to support fine-grained control over the generated motion by allowing editing and re-generation of the motion sequence. With these benefits, the developed model can rapidly integrated into the workflow of animators in the film and gaming industries.