Bringing Scripts to Acting! Content-Variant Control for performance Emotion-Alignment of Acting generation

We introduce Script-to-Acting, which generates controllable acting from a raw script. Existing text-to-motion and motion-stylization methods typically keep the motion content fixed and only rescale style or intensity, a paradigm we term content-consistent control. Acting is different: when the requested emotional state changes, the content of the performance can change with it. As the intensity along an emotion axis increases, the acting may shift from a submissive plea to more confrontational actions, such as a waving reprimand, a shove-away, or a shoulder shake. We therefore construct TheaterAct, a multimodal dataset for acting generation containing 7.2 hours of professional performance from 59 characters, with aligned scripts, dialogue, motion, and fine-grained emotion annotations. Based on this dataset, we design Acting Mind, Acting Morph, and Acting Creation. Acting Mind uses a dramaturgical context projector to interpret script, character, and dialogue through structured relationship, motivation, and emotion cues. Acting Morph represents dramatic emotion as a token-wise acting-mode matrix whose coordinates are grounded in professional performances indexed by script semantics, emotion axis, and continuous intensity. Rather than broadcasting a global style code, the matrix allocates dramatic control to individual script tokens and produces an emotion-aware motion feature for synthesis. Acting Creation then realizes this content-variant control on top of any pretrained motion generator, upgrading it to support emotion-aligned acting without architectural changes. Experiments show that our method outperforms existing motion-generation models in script alignment, emotion-intensity alignment, physical plausibility, and content-variant acting. Notably, the proposed modules can be integrated with multiple motion-generation backbones as a generic enhancement.