SCAIL：透過三維一致性姿態表徵的上下文學習實現工作室級角色動畫

摘要

儘管近期取得進展，實現符合工作室級製作標準的角色動畫仍具挑戰性。現有方法能將驅動影片中的動作遷移至參考圖像，但在涉及複雜運動與跨身份動畫的開放場景中，往往難以保持結構保真度與時間一致性。本研究提出SCAIL（基於情境學習的工作室級角色動畫框架），通過兩項關鍵創新應對這些挑戰：首先，我們提出新型3D姿勢表徵法，提供更魯棒且靈活的運動信號；其次，在擴散-轉換器架構中引入全情境姿勢注入機制，實現對完整運動序列的有效時空推理。為契合工作室級需求，我們開發了兼具多樣性與質量的精選數據流水線，並建立系統性評估的綜合基準。實驗表明，SCAIL實現了最先進的性能，將角色動畫向工作室級的可靠性與真實性推進。

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：透過三維一致性姿態表徵的上下文學習實現工作室級角色動畫

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

摘要

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