Veo 3.1 Character Consistency SOLVED | Workflow That Actually Works + Prompts

How to Create Consistent AI-Generated Characters

Introduction to the Workflow

• The video introduces a method for creating consistent AI-generated characters across various scenes in just 10 minutes. The main challenge addressed is the inconsistency often found in AI-generated videos, where characters can appear different from one scene to another.

• The presenter promises to share their exact workflow using Nano Banana and Google VO 3.1, ensuring viewers will receive specific prompts and understand the underlying system by the end of the video.

Step-by-Step Pipeline for Character Creation

Step One: Foundation Character

• To achieve Hollywood-level consistency, it's essential to provide visual anchors rather than relying solely on text prompts. A high-resolution image of your character serves as this anchor. This image should be distinctive enough to hold up under various angles and lighting conditions.

• Multiple variations of a character should be generated, allowing selection of the best representation that will serve as a reference image throughout the process. Save this image at the highest quality possible for future use.

Step Two: Character Storyboard

• Uploading the reference image allows for generating a nine-panel grid (character storyboard) that showcases the character from multiple critical angles (e.g., front view, side profile). This step teaches the AI about your character's anatomy and features comprehensively.

• The storyboard acts as an engine for consistency, ensuring that all subsequent scenes maintain uniformity in character design across different perspectives and scenarios.

Step Three: Scene Generation

• Both the original reference image and character storyboard are uploaded together when generating scenes; this dual reference is crucial for maintaining consistency during scene creation. A specialized prompt generates a 3x3 grid of shots depicting various camera angles and compositions based on described scenes (e.g., motorcycle race).

• Each new scene retains consistent character features despite changes in environment or action, demonstrating how effectively this workflow captures character DNA across diverse settings (e.g., skydiving over a city).

Step Four: From Grid to Animation

• After generating scenes, favorite shots must be extracted from grids at full resolution before animation can occur; two methods are available for extraction—cropping directly if resolution permits or using a specialized prompt designed for extraction purposes.

• Once images are prepared at full resolution, they can be animated using tools like Google Flow’s imagetovideo feature with motion prompts describing desired movements within each shot (e.g., dynamic camera movement during sequences). This results in cinematic animations while maintaining consistent character portrayal throughout all frames created within ten minutes.

Conclusion of Workflow Steps

• The entire process consists of four key steps:

• Generate your foundation character.

• Create a nine-panel storyboard.

• Generate unlimited scenes with both references.

• Extract preferred shots and animate them.

This systematic approach revolutionizes how characters are developed in projects driven by AI technology while ensuring visual coherence across varying narratives and actions presented within those projects. All necessary prompts are provided in the description section of the video for viewer access and application in their own work processes.

Creating with AI: From Random Images to Movie Direction

Overview of Creative Control

• The speaker discusses the extensive creative control available when using AI tools, highlighting capabilities ranging from generating random images to directing a movie.

• Emphasis is placed on the ability to customize various elements such as actors, costumes, and cinematography in the creative process.

• Viewers are encouraged to engage with the content by copying and tweaking prompts provided in the video description for their own creations.

• A call-to-action is made for viewers to like and subscribe, noting that this support helps algorithms share content with more creators.