Roar & transform to bear

Cited / reported data

Emotional performance is improved for more expressive and nuanced character delivery. How it works: the model generates characters whose facial micro-expressions (eye movement, mouth shape, brow position) and body language (gesture, posture, movement intensity) respond to the emotional context described in your prompt. Instead of flat, neutral faces, characters can express joy, sadness, surprise, anger, or subtler states like hesitation or curiosity. When to use this: virtual-human and VTuber content where emotional connection drives audience engagement; brand storytelling where character emotion carries the narrative; educational content where an instructor character needs to appear warm and approachable; social-media shorts where expressive characters boost watch-time and shares. Tips and practical notes: be specific about the emotion and its intensity in your prompt — 'gentle smile with a slight head tilt' produces more nuanced results than just 'happy.' For dialogue scenes, pair emotion expression with native audio to get synchronized vocal tone and facial expression. The model handles transitions between emotions within a single shot (e.g., 'starts surprised then breaks into laughter'), which is useful for short narrative content. Combine with character consistency to ensure the same character maintains its identity while expressing different emotions across shots.

AI characters often look flat or fake—emotion doesn’t land. Seedance 2.0 improves micro-expressions and body language so characters can show joy, sadness, surprise, or anger in a more natural way; eyes, mouth, and gesture all read better. If you want AI characters with real “acting,” 2.0 gets you there.