Character Consistency

Maintain character identity across multiple shots using Identity Grounding 2.0

Introduction

One of the biggest challenges in AI-generated video is maintaining consistent character appearance across shots. MiniStudio solves this with Identity Grounding 2.0, a powerful system that ensures your characters look the same every time they appear.

Identity Grounding works by analyzing the initial character appearance and creating a consistent representation that's referenced throughout the video generation process.

How It Works

The character consistency system uses multiple techniques:

  • 1.Initial Character Extraction: From the first shot, the system extracts and encodes the character's identity features.
  • 2.Identity Embedding: Creates a compact representation of the character that can be referenced across shots.
  • 3.Consistency Checks: Validates that generated frames match the character identity within acceptable thresholds.
  • 4.Adaptive Refinement: Adjusts the generation parameters if consistency falls below targets.

Using Character Consistency

Basic Usage

from ministudio import VideoOrchestrator, VideoGenerationRequest

orchestrator = VideoOrchestrator(provider)

# First shot - establishes character identity
request1 = VideoGenerationRequest(
    prompt="A scientist in a white coat works at a laboratory bench",
    duration_seconds=8,
    aspect_ratio="16:9"
)

result1 = await orchestrator.generate_shot(request1)

# Second shot - maintains character consistency
request2 = VideoGenerationRequest(
    prompt="The same scientist looks at test results on a screen",
    duration_seconds=8,
    aspect_ratio="16:9",
    character_identity=result1.character_identity  # Reference first shot's character
)

result2 = await orchestrator.generate_shot(request2)

Advanced Configuration

from ministudio import VideoGenerationRequest, CharacterConsistencyConfig

# Fine-tune consistency behavior
consistency_config = CharacterConsistencyConfig(
    enabled=True,
    confidence_threshold=0.85,  # How strict consistency checks are (0-1)
    allow_pose_variation=True,  # Allow different poses for same character
    allow_lighting_variation=False  # Keep lighting consistent
)

request = VideoGenerationRequest(
    prompt="The scientist walks across the laboratory",
    duration_seconds=8,
    aspect_ratio="16:9",
    character_consistency=consistency_config,
    character_identity=result1.character_identity
)

result = await orchestrator.generate_shot(request)

Best Practices

  • ✓Start with a clear, well-lit shot of your character for the initial frame.
  • ✓Use detailed character descriptions in your prompts to reinforce identity.
  • ✓Keep the confidence threshold high (0.8+) for strict consistency.
  • ✗Avoid drastically changing the character's appearance between shots.
  • ✗Don't use completely different camera angles without context.

Troubleshooting

Character appearance changes between shots

Increase the confidence_threshold value and ensure your prompts consistently describe the character.

Consistency errors blocking generation

Lower the confidence_threshold slightly or enable pose/lighting variation to allow more flexibility.

Character identity not extracting properly

Ensure the initial shot has good lighting and a clear view of the character's face.