💡 The paper presents SCAIL-2, a framework for controlled character animation that enables end-to-end motion transfer from driving videos to reference characters without using intermediate representations. Prior methods relied on intermediate representations such as pose skeletons or masked backgrounds, which led to information loss. SCAIL-2 addresses this issue by directly concatenating driving videos to the sequence, allowing the model to obtain all required visual information from the input video.

To overcome the lack of end-to-end data, the authors unify sub-tasks of character animation with decoupled conditions and create a pipeline to synthesize a large dataset called MotionPair-60K, which contains heterogeneous tasks of character animation. The framework utilizes in-context mask conditioning and mode-specific RoPE as soft guidance beyond textual instructions and raw visual information.

The authors also propose Bias-Aware DPO to mitigate errors caused by synthetic discrepancies in detailed regions. This approach constructs preference items to address the issue. Extensive experiments demonstrate that SCAIL-2 substantially outperforms existing state-of-the-art approaches in various character animation tasks.

The key contributions of the paper are the development of an end-to-end character animation framework that bypasses intermediate representations, the creation of a large synthetic dataset for motion transfer, and the proposal of a novel method to address synthetic discrepancies. The results show that SCAIL-2 achieves superior performance compared to existing methods, and the authors plan to release a large subset of synthetic data and model weights to facilitate further research.