How to Train Your Dragon: Automatic Diffusion-Based Rigging for Characters with Diverse Topologies

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dc.contributor.authorGu, Zeqien_US
dc.contributor.authorLiu, Difanen_US
dc.contributor.authorLanglois, Timothyen_US
dc.contributor.authorFisher, Matthewen_US
dc.contributor.authorDavis, Abeen_US
dc.contributor.editorBousseau, Adrienen_US
dc.contributor.editorDay, Angelaen_US
dc.date.accessioned2025-05-09T09:10:13Z
dc.date.available2025-05-09T09:10:13Z
dc.date.issued2025
dc.description.abstractRecent diffusion-based methods have achieved impressive results on animating images of human subjects. However, most of that success has built on human-specific body pose representations and extensive training with labeled real videos. In this work, we extend the ability of such models to animate images of characters with more diverse skeletal topologies. Given a small number (3-5) of example frames showing the character in different poses with corresponding skeletal information, our model quickly infers a rig for that character that can generate images corresponding to new skeleton poses. We propose a procedural data generation pipeline that efficiently samples training data with diverse topologies on the fly. We use it, along with a novel skeleton representation, to train our model on articulated shapes spanning a large space of textures and topologies. Then during fine-tuning, our model rapidly adapts to unseen target characters and generalizes well to rendering new poses, both for realistic and more stylized cartoon appearances. To better evaluate performance on this novel and challenging task, we create the first 2D video dataset that contains both humanoid and non-humanoid subjects with per-frame keypoint annotations. With extensive experiments, we demonstrate the superior quality of our results.en_US
dc.description.number2
dc.description.sectionheadersRigged for Success: Character Animation and Retargeting
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70016
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70016
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70016
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Animation; Image manipulation
dc.subjectComputing methodologies → Animation
dc.subjectImage manipulation
dc.titleHow to Train Your Dragon: Automatic Diffusion-Based Rigging for Characters with Diverse Topologiesen_US
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