SCAIL：通过三维一致性姿态表征的上下文学习实现影视级角色动画

摘要

尽管近期取得进展，实现符合影视级制作标准的角色动画仍具挑战。现有方法可将驱动视频中的动作迁移至参考图像，但在涉及复杂运动和跨身份动画的开放场景中，往往难以保持结构保真度与时间一致性。本研究提出SCAIL（基于情境学习的影视级角色动画框架），通过两项关键创新应对这些挑战：首先，我们提出一种新型3D姿态表示法，提供更鲁棒灵活的运动信号；其次，在扩散-变换器架构中引入全上下文姿态注入机制，实现对完整运动序列的有效时空推理。为契合影视级要求，我们开发了兼顾多样性与质量的精选数据流水线，并建立了系统性评估的综合基准。实验表明，SCAIL实现了最先进的性能，将角色动画向影视级可靠性与真实感推进。

English

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：通过三维一致性姿态表征的上下文学习实现影视级角色动画

SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations

摘要

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