Media creation with artificial intelligence

Copyright and fair use

To understand the full picture of copyright, it is necessary to consider its real-world implementation.

Game companies have a strong interest in not upsetting their fan base, especially organized structures such as gaming communities.

Game communities function as voluntary support structures that provide services companies would otherwise need to fund themselves. Many of these contributions can be understood as direct or indirect sales promotion. These communities typically offer:

• First contact and general help for newbies.
• The creation of guides, tips and tricks, and even complete playthroughs.
• Support in organizing competitions and other promotional events, including creative activities such as fan art. Cosplay contributions can increase a company's visibility at events like Gamescom.
• An additional channel for updates (information and content).
• Bug reporting and, in some cases, bug fixing. In rare instances, community members may even contribute to maintaining source code.
• Mods that improve replay value and thus increase overall customer satisfaction.
• Increased likelihood that fans will purchase other games and products (merchandise) from the company.
• A pool of trusted and engaged players who can be recruited as beta testers for new releases.

As a result, most companies employ community managers. In practice, companies often tolerate limited uses of their intellectual property because they benefit from these activities. A strictly enforced copyright regime could suppress creative community contributions, reduce engagement, and ultimately harm the company itself.

However, this does not mean that copyright is overridden. In some cases, fan works may fall under fair use (depending on jurisdiction), but more often they exist within a space of informal tolerance or explicit licensing policies.

Modifications (mods) often remain short of becoming independent games:

• Typically, they add optional 2D and 3D content, sometimes in large quantities. However, more fundamental changes - such as new game mechanics - often require access to or modification of the game engine and are therefore limited.

Generative AI (GenAI) has the potential to disrupt this cost–benefit balance:

• The large-scale or automated production of new content based on existing assets may conflict with the company’s interests.

Considering the artists and technicians involved in creating the original work, mass-produced fan content generated at little or no cost could threaten established creative professions.

• An overabundance of derivative content may dilute attention and reduce consumer motivation to engage with official products.

The goal should be a form of symbiotic coexistence:

• Game communities should avoid creating direct competition with official products.
• In contrast to official expansion packs and DLCs, mods should generally not be placed behind paywalls.

As of 2026, conflicts arising from GenAI-driven mods remain largely hypothetical, but their relevance is likely to increase. In the long term, cooperative development models between companies and communities are conceivable, though this remains uncharted territory.

Possibilities and limitations

Generative artificial intelligence (GenAI) can ease and accelerate content creation. The difficulty of using or creating your own setups will continue to decrease with each newly released commercial forerunner model.

You can use GenAI via websites, desktop clients, or dedicated programs that may run fully locally. To build your own programmatic solutions, you will either need downloadable AI models or API keys to access cloud services that perform the heavy computation remotely. With sufficient expertise, you can even build agentic AIs (such as Open Claw) that use your existing tools and carry out tasks automatically. However, caution is advised: probabilistic AIs can hallucinate and may pose a risk to your system. As a mitigation, MCPs should be used. Sandboxes offer an additional layer of safety, but they can limit the usefulness of agents and introduce extra complexity, which may offset the time savings you intended to achieve.

GenAI systems operate probabilistically. Do not expect identical results when repeating prompts with the same inputs. The same text prompts may produce similar, but not identical, outputs. Therefore, in some scenarios, it can be beneficial to generate multiple results and select the most suitable candidate for your intermediate or final goal.

Sounds, voice acting and music

• Voice-cloning of existing or creation of new voices. For natural voices you may want to look for emotional text-to-speech.
• Music generations

Image generation

• Content generation based on:
  • Text prompts
  • Own drafts
  • Merging (main image and references)
• Changing existing content
  • Expanding
  • Inpainting (replacement of subsections)
  • Style transfers

3D content generation

There exists content generators that turn 2D data into 3D data by calculating plausible assumptions for the missing dimension.

Tools

2D images

All major multimodal LLMs such as ChatGPT, Claude, Gemini, Grok, and Mistral provide image generation capabilities.

• In addition, there are specialized tools (e.g., diffusion-based systems) that offer more control and customization. However, most users already have access to at least one of these platforms and can begin generating images immediately.
• For high-volume generation, a paid subscription or plan is typically required to increase rate limits and output capacity.

For image editing and post-processing, dedicated graphics software such as GIMP, Krita, or Photoshop is recommended. These tools allow precise control (e.g., masking, compositing, color correction) and can complement GenAI workflows.

Beginner workflow

Mini tutorial based on ChatGPT. Though, this should similar for all MLLMs.

• Iterative prompting: Describe the desired result as clearly as possible: Motive, perspective, colors, lights, shadows, art style. Refine the prompt step by step based on undesired aspects rather than expecting a perfect result on the first attempt.
• Avoid quality loss: If the GenAI degenerates the image quality because of too many iterations, try from a new start with combined text prompts.
• Reference images: When supported, provide one or more images to guide style, composition, or subject consistency. This is often more reliable than text-only prompting.
• Context management: If previous prompts begin to overly influence results, start a new prompt and explicitly restate the desired outcome. This prevents unintended bias from earlier context.
• Merging / composition: Supplying multiple images in a single prompt can help combine elements. However, repeated re-editing of generated outputs may degrade detail or introduce artifacts.
• Batch generation: Since outputs are probabilistic, generate multiple final candidates and select the best.
• Post-processing workflow: Combine the best elements using external tools (e.g., masking in Photoshop or Krita). This hybrid approach often yields higher-quality results than relying on a single generation.

Generate, refine via text prompts, select final candidate, refine via graphic tools.

In context of its limitations, this workflow is still great for rapid prototyping and exploring different creative directions for drafts.

(Add examples here.)

Advanced workflow

AUTOMATIC1111 (aka Stable diffusion), ComfyUI

Videos

Grok (xAI)

Gemini (Google)

Sora (OpenAI)

3D objects

• ...

3D animations

• ...

World generators

• World generator inside Unreal Engine 5
• ...