SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning

Hugging Face Daily Papers Papers
character-animation motion-transfer end-to-end synthetic-data in-context-conditioning bias-aware-dpo

Summary

SCAIL-2 is a framework that achieves end-to-end controlled character animation by directly transferring motion from driving videos without intermediate representations, using unified task decomposition, synthetic data (MotionPair-60K), and novel conditioning techniques like in-context mask conditioning and Bias-Aware DPO.

Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate representations, including pose skeletons to represent motion or masked background to represent environment, which inevitably leads to information loss. To address this, we present SCAIL-2, an framework that bypasses those intermediates and achieves end-to-end character animation. By directly concatenating driving videos to the sequence, the model can obtain all the required visual information from the input video. To address lack of end-to-end data, we unify sub-tasks of character animation with decoupled conditions and then curate a pipeline to synthesize MotionPair-60K, an end-to-end motion transfer dataset containing heterogeneous tasks of character animation. To archive the unification, we utilize in-context mask conditioning and mode-specific RoPE as soft guidance beyond textual instructions and raw visual information. To address synthetic discrepancy in detailed regions, we propose Bias-Aware DPO to construct preference items to mitigate the errors. Extensive experiments demonstrate that our method substantially outperforms existing state-of-the-art approaches in various character animation tasks. A large subset of synthetic data as well as model weights will be released at our project page: https://teal024.github.io/SCAIL-2/.
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Paper page - SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning

Source: https://huggingface.co/papers/2606.10804

Abstract

SCAIL-2 enables end-to-end character animation by directly transferring motion from driving videos without intermediate representations, using unified task decomposition and synthetic data generation.

Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate representations, including pose skeletons to represent motion or masked background to represent environment, which inevitably leads to information loss. To address this, we present SCAIL-2, an framework that bypasses those intermediates and achievesend-to-end character animation. By directly concatenatingdriving videosto the sequence, the model can obtain all the required visual information from the input video. To address lack of end-to-end data, we unify sub-tasks of character animation withdecoupled conditionsand then curate a pipeline to synthesizeMotionPair-60K, an end-to-endmotion transferdataset containing heterogeneous tasks of character animation. To archive the unification, we utilizein-context mask conditioningandmode-specific RoPEas soft guidance beyond textual instructions and raw visual information. To address synthetic discrepancy in detailed regions, we proposeBias-Aware DPOto construct preference items to mitigate the errors. Extensive experiments demonstrate that our method substantially outperforms existing state-of-the-art approaches in various character animation tasks. A large subset of synthetic data as well as model weights will be released at our project page: https://teal024.github.io/SCAIL-2/.

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