CharacterShot: Controllable and Consistent 4D Character Animation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909731179724800 |
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| author | Gao, Junyao Li, Jiaxing Liu, Wenran Zeng, Yanhong Shen, Fei Chen, Kai Sun, Yanan Zhao, Cairong |
| author_facet | Gao, Junyao Li, Jiaxing Liu, Wenran Zeng, Yanhong Shen, Fei Chen, Kai Sun, Yanan Zhao, Cairong |
| contents | In this paper, we propose \textbf{CharacterShot}, a controllable and consistent 4D character animation framework that enables any individual designer to create dynamic 3D characters (i.e., 4D character animation) from a single reference character image and a 2D pose sequence. We begin by pretraining a powerful 2D character animation model based on a cutting-edge DiT-based image-to-video model, which allows for any 2D pose sequnce as controllable signal. We then lift the animation model from 2D to 3D through introducing dual-attention module together with camera prior to generate multi-view videos with spatial-temporal and spatial-view consistency. Finally, we employ a novel neighbor-constrained 4D gaussian splatting optimization on these multi-view videos, resulting in continuous and stable 4D character representations. Moreover, to improve character-centric performance, we construct a large-scale dataset Character4D, containing 13,115 unique characters with diverse appearances and motions, rendered from multiple viewpoints. Extensive experiments on our newly constructed benchmark, CharacterBench, demonstrate that our approach outperforms current state-of-the-art methods. Code, models, and datasets will be publicly available at https://github.com/Jeoyal/CharacterShot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CharacterShot: Controllable and Consistent 4D Character Animation Gao, Junyao Li, Jiaxing Liu, Wenran Zeng, Yanhong Shen, Fei Chen, Kai Sun, Yanan Zhao, Cairong Computer Vision and Pattern Recognition In this paper, we propose \textbf{CharacterShot}, a controllable and consistent 4D character animation framework that enables any individual designer to create dynamic 3D characters (i.e., 4D character animation) from a single reference character image and a 2D pose sequence. We begin by pretraining a powerful 2D character animation model based on a cutting-edge DiT-based image-to-video model, which allows for any 2D pose sequnce as controllable signal. We then lift the animation model from 2D to 3D through introducing dual-attention module together with camera prior to generate multi-view videos with spatial-temporal and spatial-view consistency. Finally, we employ a novel neighbor-constrained 4D gaussian splatting optimization on these multi-view videos, resulting in continuous and stable 4D character representations. Moreover, to improve character-centric performance, we construct a large-scale dataset Character4D, containing 13,115 unique characters with diverse appearances and motions, rendered from multiple viewpoints. Extensive experiments on our newly constructed benchmark, CharacterBench, demonstrate that our approach outperforms current state-of-the-art methods. Code, models, and datasets will be publicly available at https://github.com/Jeoyal/CharacterShot. |
| title | CharacterShot: Controllable and Consistent 4D Character Animation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.07409 |