Controllable Part-wise Motion Generation
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School of Science |
Master's thesis
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Date
2024-12-31
Department
Major/Subject
Computer Science
Mcode
Degree programme
Master's Programme in Computer, Communication and Information Sciences
Language
en
Pages
35
Series
Abstract
Recent advances in generative models show promise in generating diverse and expressive character animations. Most existing models, however, rely on full-body representations, which can restrict the manipulation of character motions when control and adjustments are needed for specific body parts, such as hands, to avoid collisions. Building on existing autoregressive conditional variational autoencoder architectures, this work explores the use of part-wise generative models to address this limitation. By dividing the character into upper and lower body parts and treating them as separate modalities, we introduce more control over specific body parts, enabling finer manipulation of character motions. To handle the interdependencies between body parts, we leverage a multimodal mixture-of-experts prior, allowing information sharing between the different parts. This approach enables the generation of diverse and controllable animations, improving the adaptability of characters in collision avoidance scenarios. We demonstrate the effectiveness of our model in two collision avoidance tasks: dancing in narrow corridors and avoiding collisions between two characters. Our model outperforms the baseline method by minimizing collisions between the character's body parts and obstacles, showcasing its potential for adaptable animation generation in dynamic environments.Description
Supervisor
Hämäläinen, PerttuThesis advisor
Kim, Nam HeeKeywords
generative models, character animation, conditional variational autoencoders, collision avoidance, machine learning, games