ai-animation-workflow

Generate one reference image of the character and use it as the starting frame for every scene's image-to-video generation. Begin each video prompt with "keeping face, body, color identical to reference" and always include the character-preservation negative prompt.

Plan 12-15 key images, run independent I2V generation per scene with a shared reference image, then stitch clips using ffmpeg concat with crossfade transitions. Frame Chaining, where each clip's last frame starts the next, gives seamless but costlier results.

Runway Gen-4.5 offers the strongest reference-image character retention among Western APIs, while Wan 2.6-R2V leads Asian platforms for reference-to-video consistency. For open-source pipelines, combine IP-Adapter with AnimateDiff in ComfyUI.

Text-only prompts cannot maintain identity because each generation invents a new version of the character. Large motions like running or jumping also drop character retention to 20-40%, so keep motion minimal or add IP-Adapter and ControlNet constraints.

No, pure text prompting is the least reliable consistency method and should be avoided. Every text-to-image generation produces a different interpretation, so always anchor identity with a shared reference image instead.

Use a mandatory negative prompt covering identity drift: different character, face change, identity change, face morphing, appearance drift, character mutation, inconsistent appearance, plus quality terms like deformed, blurry, and distorted.