Integrating runway into creative workflow: ai for video and image
Runway (often Runway ML) is a creative AI (Generative AI tools) platform focused on image and video generation and manipulation. It offers a suite of “AI Magic Tools” enabling creators to perform tasks like generating video from text or images (Runway Gen-2 explanation), removing objects from videos (inpainting), smooth slow-motion (motion interpolation), training custom models, and much more. Integrating Runway into creative workflow can unlock new possibilities but requires understanding its capabilities and how to fit it in effectively.
The challenge: ai video generation quality and control
Video generation from text or images (Gen-1, Gen-2) is one of Runway’s most exciting features. However, like any cutting-edge Generative AI technology, the quality, consistency, and control over the output can be variable. Achieving precise motion, maintaining object consistency across frames, or generating long, complex videos remains challenging. Users often need to experiment with prompts (prompt engineering), reference images, and settings, potentially generating multiple clips to get the desired result.
Learning curve and complexity of ‘magic tools’
Runway offers a wide array of specialized AI tools. Learning how to effectively use each tool (inpainting, frame interpolation, text-to-color-grade, etc.) takes time and experimentation. While the interface aims for user-friendliness, mastering the parameters and nuances of each tool to achieve professional results requires a learning curve.
Integration with traditional video editing software
Runway is often used to generate specific clips or effects that are then incorporated into a larger video editing project using software like Adobe Premiere Pro or Final Cut Pro. The challenge is ensuring a smooth workflow between Runway and these non-linear editors (NLEs). This involves managing file exports and imports effectively and ensuring format and color consistency across platforms.
Cost and credit management
Runway typically operates on a credit-based model, where different AI operations consume credits at varying rates. Video generation, in particular, can be credit-intensive. Teams need to manage their credit usage and budget to avoid unexpected costs, especially when experimenting or generating many iterations.
Ethical and copyright considerations
As with all generative AI tools (AI ethics for businesses), using Runway raises questions about the copyright of training data (AI Training Data) and generated outputs, as well as the potential for creating deepfakes or misleading content.
Brandeploy: managing assets and brand around runway
Runway is a *production* tool. Brandeploy can support this process by:
- Managing Input Assets: Storing and providing approved brand images, logos, or video clips (centralization and control of brand assets) that might be used as references or inputs in Runway.
- Managing Output Assets: Once a video clip or image is finalized in Runway, it can be uploaded and managed within Brandeploy as an approved asset.
- Ensuring Overall Consistency: Embedding Runway-generated clips into broader marketing templates (e.g., a presentation, webpage) managed in Brandeploy ensures the surrounding context adheres to the brand governance platform.
Brandeploy provides the asset management and governance *around* Runway’s creative production capabilities.
Explore the creative possibilities of AI video and image with Runway. Integrate its tools into your workflow to generate unique effects and clips. Manage your assets and ensure overall brand consistency with Brandeploy. Schedule a demo.
