Artificial intelligence and deep learning techniques are revolutionizing the film production pipeline. The majority of the current screenplay-to-animation pipelines focus on understanding the screenplay through natural language processing techniques, and on the generation of the animation through custom engines, missing the possibility to customize the characters. To address these issues, we propose a high-level pipeline for generating 2D characters and animations starting from screenplays, through a combination of Latent Diffusion Models and Large Language Models. Our approach uses ChatGPT to generate character descriptions starting from the screenplay. Then, using that data, it generates images of custom characters with Stable Diffusion and animates them according to their actions in different scenes. The proposed approach avoids well-known problems in generative AI tools such as temporal inconsistency and lack of control on the outcome. The results suggest that the pipeline is consistent and reliable, benefiting industries ranging from film production to virtual, augmented and extended reality content creation.

Character Animation Pipeline based on Latent Diffusion and Large Language Models

Clocchiatti A.
First
;
Fumero N.;Soccini A. M.
Last
2024-01-01

Abstract

Artificial intelligence and deep learning techniques are revolutionizing the film production pipeline. The majority of the current screenplay-to-animation pipelines focus on understanding the screenplay through natural language processing techniques, and on the generation of the animation through custom engines, missing the possibility to customize the characters. To address these issues, we propose a high-level pipeline for generating 2D characters and animations starting from screenplays, through a combination of Latent Diffusion Models and Large Language Models. Our approach uses ChatGPT to generate character descriptions starting from the screenplay. Then, using that data, it generates images of custom characters with Stable Diffusion and animates them according to their actions in different scenes. The proposed approach avoids well-known problems in generative AI tools such as temporal inconsistency and lack of control on the outcome. The results suggest that the pipeline is consistent and reliable, benefiting industries ranging from film production to virtual, augmented and extended reality content creation.
2024
XRiM Workshop in IEEE AIxVR 2024
Los Angeles (USA)
17-19 January 2024
Proceedings of IEEE AIxVR 2024
IEEE
398
405
https://ieeexplore.ieee.org/abstract/document/10445544
artificial intelligence, deep learning, generative art, virtual reality, extended reality, computer animation
Clocchiatti A.; Fumero N.; Soccini A.M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2037375
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