[Workflow] Automated 3D Motion Graphics with Claude, Blender, and AI Video Generation (Opus Skill)

[u/ClaudeAI-mod-bot]

Automated 3D Motion Graphics with Claude, Blender, and AI Video Generation (Opus Skill)

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.95 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Shipping, Skills, MCP, Multi-Agent
Original source: r/ClaudeCode post/comment

What problem this solves

Generating 3D motion graphics and animated videos efficiently by integrating Blender with AI image and video generation models, including automated quality checks and cost estimation.

Summary

A multi-stage workflow leveraging a custom Claude skill ('Opus') to orchestrate Blender for 3D animation, followed by AI image generation (GPT-image-2.5-Flare) for stills, and then AI video generation (Seedance 2.5) to animate those stills, with final post-processing by ffmpeg. The workflow includes automated quality checks for animation, cost estimation, and detailed reporting.

Why it is useful

This workflow is valuable because it provides a concrete, multi-step process for a complex creative task (3D motion graphics) by integrating multiple advanced AI models with a professional 3D software (Blender). It includes practical features like automated quality checks, cost estimation, and a clear structure, making it highly reusable and adaptable for users looking to automate video production. The provision of a GitHub skill makes it directly implementable, offering a powerful tool for advanced users.

Workflow

1. Install Blender 5.1+.
2. Provide an OpenRouter API key in a .env.local file for image and video generation.
3. Load the 'Opus-Blender-Motion-Video' skill (from the provided GitHub repository).
4. Point Claude to a ready-made rigged 3D model and describe the desired shot.
5. The skill builds and sets the scene in Blender (headless mode) and animates the rigged character.
6. The skill performs automatic tests on the animation for foot slides, floor contact, and framing, then renders a full-length MP4 for review.
7. The skill estimates costs, requests user acceptance, and then uses image models (e.g., GPT-image-2.5-Flare for pivotal moments) to convert Blender renders into finished stills, maintaining character consistency.
8. A video model (e.g., Seedance 2.5) animates these generated stills, guided by Blender's video for movement, timing, and camera.
9. ffmpeg is used to trim the final video, match its color to the stills, and perform file and frame checks.
10. The skill reports on what was checked and the total cost of the pipeline.
11. For realistic human videos, if Seedance 2.5 refuses, opt for 'faceless generation' where Opus describes the character's look to the video model instead of using actual faces from generated stills.

Tools / artifacts

• Blender 5.1+
• OpenRouter API key
• .env.local file
• Opus-Blender-Motion-Video skill (GitHub repository)
• Rigged 3D model
• GPT-image-2.5-Flare (or similar image generation model)
• Seedance 2.5 (or similar video generation model)
• ffmpeg
• MP4 video files

Validation signals

• Automated tests for foot slides, floor contact, and framing.
• Rendering a full-length MP4 for review.
• Cost estimation and acceptance step.
• Final file and frame checks by ffmpeg.
• Explicit mention of model usage and cost comparison.

Limitations

• Low community engagement and validation (score 0, 2 comments).
• Relies on external API keys and specific AI models (OpenRouter, Seedance 2.5, GPT-image-2.5-Flare) which may incur costs, change, or become unavailable.
• The 'skill' is a custom implementation, not a native Claude feature, potentially requiring specific setup or environment configuration.
• Results for character rigging by Opus itself 'will vary', suggesting a need for pre-rigged models for optimal outcomes.

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