From Rigging to Waving: 3D-Guided Diffusion for Natural Animation of Hand-Drawn Characters
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| Format: | Preprint |
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2025
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| _version_ | 1866911142781124608 |
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| author | Zhou, Jie Qu, Linzi Lam, Miu-Ling Fu, Hongbo |
| author_facet | Zhou, Jie Qu, Linzi Lam, Miu-Ling Fu, Hongbo |
| contents | Hand-drawn character animation is a vibrant field in computer graphics, presenting challenges in achieving geometric consistency while conveying expressive motion. Traditional skeletal animation methods maintain geometric consistency but struggle with complex non-rigid elements like flowing hair and skirts, leading to unnatural deformation. Conversely, video diffusion models synthesize realistic dynamics but often create geometric distortions in stylized drawings due to domain gaps. This work proposes a hybrid animation system that combines skeletal animation and video diffusion. Initially, coarse images are generated from characters retargeted with skeletal animations for geometric guidance. These images are then enhanced in texture and secondary dynamics using video diffusion priors, framing this enhancement as an inpainting task. A domain-adapted diffusion model refines user-masked regions needing improvement, especially for secondary dynamics. To enhance motion realism further, we introduce a Secondary Dynamics Injection (SDI) strategy in the denoising process, incorporating features from a pre-trained diffusion model enriched with human motion priors. Additionally, to tackle unnatural deformations from low-poly single-mesh character modeling, we present a Hair Layering Modeling (HLM) technique that uses segmentation maps to separate hair from the body, allowing for more natural animation of long-haired characters. Extensive experiments show that our system outperforms state-of-the-art methods in both quantitative and qualitative evaluations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Rigging to Waving: 3D-Guided Diffusion for Natural Animation of Hand-Drawn Characters Zhou, Jie Qu, Linzi Lam, Miu-Ling Fu, Hongbo Graphics Hand-drawn character animation is a vibrant field in computer graphics, presenting challenges in achieving geometric consistency while conveying expressive motion. Traditional skeletal animation methods maintain geometric consistency but struggle with complex non-rigid elements like flowing hair and skirts, leading to unnatural deformation. Conversely, video diffusion models synthesize realistic dynamics but often create geometric distortions in stylized drawings due to domain gaps. This work proposes a hybrid animation system that combines skeletal animation and video diffusion. Initially, coarse images are generated from characters retargeted with skeletal animations for geometric guidance. These images are then enhanced in texture and secondary dynamics using video diffusion priors, framing this enhancement as an inpainting task. A domain-adapted diffusion model refines user-masked regions needing improvement, especially for secondary dynamics. To enhance motion realism further, we introduce a Secondary Dynamics Injection (SDI) strategy in the denoising process, incorporating features from a pre-trained diffusion model enriched with human motion priors. Additionally, to tackle unnatural deformations from low-poly single-mesh character modeling, we present a Hair Layering Modeling (HLM) technique that uses segmentation maps to separate hair from the body, allowing for more natural animation of long-haired characters. Extensive experiments show that our system outperforms state-of-the-art methods in both quantitative and qualitative evaluations. |
| title | From Rigging to Waving: 3D-Guided Diffusion for Natural Animation of Hand-Drawn Characters |
| topic | Graphics |
| url | https://arxiv.org/abs/2509.06573 |