Step-by-Step: The State of AI Filmmaking Workflows

AI tools continue to reshape how films are made, offering new workflows that integrate text, image, and video generation. Joey breaks down the current landscape of AI filmmaking, exploring practical steps, key tools, and challenges creators face in this evolving domain. This overview highlights how to maintain visual consistency, develop characters, and leverage emerging video-to-video models to streamline production.

The typical AI filmmaking workflow follows a logical progression from ideation to final video output. Creators begin with story development and shot design, move through visual style and character consistency, generate initial frames, and then transform those into moving images. Despite rapid tool evolution, the fundamental workflow remains stable, adaptable across tools and needs.

Joey points out that many creators in competitions like Cinema Synthetica use some variation of this flow: text prompts lead to images, which then become videos. This approach allows filmmakers to create compelling visuals even without cameras or physical resources, relying solely on AI-driven generation.

Every film begins with a solid story. Once the narrative is clear, shot design becomes the next focus. AI language models (LLMs) like Google Gemini 2.5, Claude, or ChatGPT assist by brainstorming shot ideas and generating detailed prompts for image generation.

For example, if you envision an action sequence—a character chased through a mine tunnel on a mine cart—LLMs can help define what shots are needed and provide precise text prompts to feed into image generation tools.

Maintaining a consistent style across shots is crucial to avoid jarring visual shifts. There are several strategies to achieve this:

Joey emphasizes creating original styles ethically by generating images specifically for training, rather than borrowing copyrighted movie imagery. Style transfer, a technique familiar from projects replicating styles like Miyazaki’s Spirited Away, applies consistent filters to maintain a unified look.

Characters must appear consistent across shots, especially when faces and performances are central. AI workflows often combine these approaches:

Joey explains that the LoRA model is trained first to establish the character's base look, followed by a pose sheet that guides the character’s appearance in different positions and angles. This layered approach helps AI maintain the character’s identity through various shots.

He warns about the "uncanny valley" effect: slight imperfections in faces can break audience immersion, so creators must invest time ensuring natural and believable character appearances.

Scene consistency is equally important. One effective method is to use 3D environments (like Unreal Engine rooms) to generate 360-degree panoramas, then apply style transfer. This technique provides consistent backgrounds for conversations or multi-angle shots.

Spatial consistency in AI-generated images remains a frontier challenge, but tools like Flux Kontext and ChatGPT offer some solutions by understanding scene context. Using 3D models to inform AI generation shortcuts many issues, maintaining a believable cinematic world.

With story, style, and characters locked down, creators move to generating first frames for each shot. This process often blends text-to-image generation with reference images and LoRA models for control.

Key tools include:

LLMs help refine and structure detailed prompts, often producing more effective inputs than creators might devise alone. Visual language models (VLMs) add flexibility by converting images back into text prompts for iteration.

Hybrid workflows combining 3D tools like Blender or Unreal Engine with AI style transfer allow creators to block out scenes and generate consistent base images. For example, posing Metahumans in Unreal and capturing stills provides references for AI generation.

Sometimes generated images are nearly perfect but require tweaks. Inpainting tools let creators modify specific areas without regenerating the entire image. For instance, changing a coffee cup to a chalice involves painting over the object and prompting AI to replace it seamlessly.

Photoshop with Adobe Firefly is commonly used alongside AI to polish images, blending traditional and AI tools effectively. This back-and-forth improves quality and control.

For scenes with multiple characters, compositing separate character images in Photoshop before final video generation offers better consistency than generating complex multi-character images directly in AI tools.

After establishing first frames, creators move to video generation. Most AI tools support image-to-video workflows, where the initial frame guides the video output. Some tools even accept multiple frames (first, middle, last) for improved control.

Popular video generation tools include:

Rendering times vary depending on hardware but are comparable to traditional CG render timelines, with 30-second 720p clips taking about an hour on high-end GPUs.

Camera movement control is evolving. Some older models support mapping camera moves, though newer versions may lack these features temporarily. Creators aim to resolve ambiguity by providing clear prompts and camera directions.

Two significant hurdles remain: achieving consistent character performance and syncing lip movements with dialogue. Tools like Hedra, HeyGen, and Runway Act-One offer character animation and speech syncing capabilities, though some are still limited to earlier model versions.

Joey notes that while AI excels at one-shot action sequences, sustained consistency over a feature-length film with dialogue remains complex. Hybrid workflows combining real human performances filmed on green screens with AI-generated backgrounds or facial composites provide practical solutions.

Integrating real actors with AI-generated environments or characters allows filmmakers to preserve authentic performances and direction. Techniques include:

This hybrid approach helps overcome current AI limitations in faces and speech, especially for complex narratives requiring nuanced human expression.

The release of Luma AI’s restyle video feature marks a shift toward video-to-video workflows. Creators can shoot simple object-blocking videos (even with toys or household items) and transform them into rich, cinematic sequences by restyling and iterating through video generation.

John Fingers’ imaginative demos illustrate this potential—turning everyday footage into scenes like astronauts in space or stormy ships. This iterative video-to-video capability enables creators to refine quality and effects continuously.

Such workflows could reduce dependence on traditional multi-step image generation, though solid image references remain important. Camera movements can be sourced from real-world footage, Blender, or Unreal Engine to guide video generation accurately.

While AI-generated filmmaking tools are already usable and affordable, control and consistency challenges persist. However, the pace of development is rapid. According to Joey, once video-to-video capabilities mature further, many current workflow complexities may simplify significantly.

He reflects on how far AI filmmaking has come in just two years—from abstract style transfers to near-commercial-quality video generation—and anticipates a future where creating AI films becomes more effortless.

For now, creators benefit from a mix of AI tools and traditional techniques, adapting workflows as new models and features emerge.

This overview captures the current state of AI filmmaking workflows, highlighting essential tools like Runway References, ChatGPT, Flux Kontext, and emerging video-to-video models from Luma AI and others.

Understanding how to structure your workflow—from story and shot design through style, character development, image generation, and video creation—can empower filmmakers to leverage AI effectively. Balancing creativity with technical control remains key, and hybrid approaches bridging AI and traditional filmmaking show promise for complex projects.

As these tools evolve, staying informed and experimenting will help media professionals integrate AI into their production pipelines with confidence.

For those interested in exploring further, the links below provide detailed resources and tool pages to dive deeper into AI filmmaking technology.