img2vid-character

Generates image-to-video prompts that preserve character identity across animation frames.

Updated Jul 6, 2026
One-click install
npx skills add https://github.com/Lucien-1127/strata-skill --skill img2vid-character-lucien-1127
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: img2vid-character
Source: https://github.com/Lucien-1127/strata-skill/tree/main/prompt-engineering/img2vid-character
Command: npx skills add https://github.com/Lucien-1127/strata-skill --skill img2vid-character-lucien-1127

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Image-to-video generation with Agnes Video V2.0 (ti2vid mode) often causes the character to drift — hairstyle, facial features, or outfit change mid-animation. This Skill provides a structured prompt architecture and negative prompt vocabulary to keep the character consistent with the reference image. ## Core Features & Use Cases - Three-Layer Prompt Structure: Separates prompts into a Preserve layer (features that must stay identical), an Animate layer (minimal necessary motion), and a Scene layer (background, lighting, camera). - Character-Retention Negative Prompts: Ready-to-copy negative prompt word groups targeting face change, identity drift, and appearance mutation, plus general quality terms. - API Parameter Guidance: Covers Agnes Video V2.0 specifics such as the 8n+1 frame rule, public-URL-only image input, fixed seeds, and aspect-ratio locking to avoid automatic normalization. - Use Case: When animating an idol character image into a 5-second stage performance clip, apply the three-layer template with the character-retention negative prompt so the face and outfit remain identical to the reference image. ## Quick Start Use the img2vid-character skill to write a character-preserving ti2vid prompt for animating my reference image of a singer on stage.

Frequently Asked Questions about img2vid-character

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I keep a character consistent in image-to-video generation?▼

Use a three-layer prompt structure: a Preserve layer stating which features must stay identical to the reference image, an Animate layer describing only minimal necessary motion, and a Scene layer for background and camera. Combine this with character-retention negative prompts like 'face change' and 'identity change'.

What negative prompts prevent face change in AI video generation?▼

Use character-retention terms such as 'different character, face change, identity change, face morphing, different hairstyle, appearance drift, character mutation'. Add general quality terms like 'ugly, deformed, blurry, distorted' for overall output quality.

Why does my image-to-video character drift during animation?▼

Character drift increases with the amount of motion requested. Large movements like dancing or spinning carry high drift risk, while micro-motions like hair swaying or breathing preserve identity best. Missing character-retention negative prompts also cause severe drift.

Does Agnes Video V2.0 accept Base64 images for ti2vid mode?▼

No. The image parameter in ti2vid mode is a single string that must be a public URL. Base64 Data URIs are only supported in image-to-image mode via the extra_body.image array, which is a different parameter.

What frame count values are valid for Agnes Video V2.0?▼

The num_frames parameter must follow the 8n+1 rule, such as 81, 121, 241, or 441. At 24 fps, 121 frames produces roughly 5 seconds and 441 frames is the upper limit at about 18 seconds.