SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations
Achieving character animation that meets studio-grade production standards remains challenging despite recent progress. Existing approaches can transfer motion from a driving video to a reference image, but often fail to preserve structural fidelity and temporal consistency in wild scenarios involving complex motion and cross-identity animations. In this work, we present SCAIL (Studio-grade Character Animation via In-context Learning), a framework designed to address these challenges from two key innovations. First, we propose a novel 3D pose representation, providing a more robust and flexible motion signal. Second, we introduce a full-context pose injection mechanism within a diffusion-transformer architecture, enabling effective spatio-temporal reasoning over full motion sequences. To align with studio-level requirements, we develop a curated data pipeline ensuring both diversity and quality, and establish a comprehensive benchmark for systematic evaluation. Experiments show that SCAIL achieves state-of-the-art performance and advances character animation toward studio-grade reliability and realism.
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Wenhao Yan
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2025-12-12T09:17:52.579Z
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wenhao
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Sheng Ye
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Zhuoyi Yang
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Jiayan Teng
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2025-12-08T09:01:04.763Z
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tengjiayan
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ZhenHui Dong
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Kairui Wen
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2025-12-08T09:00:44.740Z
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Wen Kairui
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Xiaotao Gu
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2025-12-08T09:00:34.434Z
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Xiaotao Gu
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Yong-Jin Liu
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Jie Tang
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2025-12-08T09:00:18.245Z
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Jie Tang
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SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations
Achieving character animation that meets studio-grade production standards remains challenging despite recent progress. Existing approaches can transfer motion from a driving video to a reference image, but often fail to preserve structural fidelity and temporal consistency in wild scenarios involving complex motion and cross-identity animations. In this work, we present SCAIL (Studio-grade Character Animation via In-context Learning), a framework designed to address these challenges from two key innovations. First, we propose a novel 3D pose representation, providing a more robust and flexible motion signal. Second, we introduce a full-context pose injection mechanism within a diffusion-transformer architecture, enabling effective spatio-temporal reasoning over full motion sequences. To align with studio-level requirements, we develop a curated data pipeline ensuring both diversity and quality, and establish a comprehensive benchmark for systematic evaluation. Experiments show that SCAIL achieves state-of-the-art performance and advances character animation toward studio-grade reliability and realism.
SCAIL framework improves character animation by using a novel 3D pose representation and a diffusion-transformer architecture with full-context pose injection, achieving studio-grade quality and realism.
3D pose representation diffusion-transformer architecture full-context pose injection studio-grade animation spatio-temporal reasoning motion sequences