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Three different jobs grouped under one label
“AI for characters” can mean an image generator that produces a concept, a 3D generator that produces a prop, an animation tool that produces motion, or a runtime system that lets a non-player character hold a conversation. Each has different inputs, outputs, and failure modes, so each should be judged on its own terms.
| Workflow stage | Typical output | Example described in the sources | Where it runs |
|---|---|---|---|
| Concept exploration and asset generation | Concept images, sample characters, props, landscapes | Scenario, as described in the 2025 AWS guide to generative AI for game developers; generation from team workspaces or inside a game | Cloud service, API-first offer (AWS guide) |
| Animation assistance | Base animation sets adapted to a character’s style | Listed as a possible use in the AWS 2025 guide; no specific product named | Not stated |
| Facial animation from dialogue | Facial blendshapes driven by streaming audio | Audio2Face-3D, documented by NVIDIA with Unreal Engine and Maya workflows | Not stated for this specific tool |
| Runtime character behavior | Speech, dialogue, decisions, and actions during play | NVIDIA ACE for Games, with cloud and on-device models for speech, intelligence, and animation | Cloud or on-device |
The practical consequence is that a studio asking “should we use AI for characters?” is really asking four questions. Answering one does not answer the others.
How asset generation fits into a character or prop pipeline
The clearest published example is Scenario, which the AWS 2025 guide describes as a way to generate characters, props, and landscapes through team workspaces or by integrating into a game. The guide frames the value in terms of speed and infrastructure. Wang Yu, CEO of iFUN.COM GCR, is quoted in the guide saying that generative AI on the cloud lets the company quickly obtain the materials it needs, adding: “Whether it is the design of characters, props or scenes, generative AI on the cloud allows us to quickly obtain the materials we need and does not require us to operate and maintain AI-related infrastructure ourselves.” Scenario’s co-founder and CTO, Hervé Nivon, is quoted saying the company “has served and generated millions of images with only three people.” Both statements are executives describing their own experience, reported inside a vendor-published guide. They are not independent measurements of labor savings.
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A workable review sequence for generated character or prop material looks like this. The steps are a practical suggestion for how teams can structure the work; the sources do not test or validate this exact sequence.
- Write a style brief that defines silhouette, palette, proportions, and any constraints the character or prop must meet.
- Generate a batch of candidates rather than accepting the first output.
- Have an artist select, edit, and reject candidates against the style brief.
- Carry approved material through the team’s normal production steps before it reaches the engine.
The AWS guide says that improved consistency is part of its customer example. It does not independently test consistency, and it does not quantify general productivity gains. Consistency across a full cast of characters, and whether generated material can be edited cleanly, are questions a studio has to test on its own project.
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Animation: generating base motion and adapting it
The AWS 2025 guide lists two animation uses: generating base animation sets, and adapting them to a character’s style. This is a described workflow, not evidence that the resulting animation is production quality. Animation is also where the Google report’s grouped survey figure applies (see the survey section below), but that figure does not separate animation from other tasks.
Facial animation is a related but separate problem. NVIDIA describes Audio2Face-3D as converting streaming audio into facial blendshapes, with documented Unreal Engine and Maya workflows. That brings dialogue to an animated face. It does not generate the character’s underlying appearance or any props.
Runtime characters: speech, decisions, and behavior
Runtime character systems work differently from asset generators. They run while the game is being played and govern what a character says or does. NVIDIA’s ACE for Games documentation describes cloud and on-device models for speech, intelligence, and animation, along with Unreal Engine plugins and integration SDKs. The examples NVIDIA names include PUBG Co-Player Characters, inZOI Smart Zois, MIR5 bosses, and a Total War: PHARAOH advisor. These are NVIDIA’s own descriptions of in-game interaction and behavior. They are not independent evaluations, and they do not show that ACE generates character meshes or props.
The AWS guide also lists NPC dialogue and interactive storytelling as use cases, which places runtime characters in the same broader family as generative narrative tools.
Cloud or on-device inference
The two inference locations carry different trade-offs, and the choice affects both hardware and operating costs:
- Cloud inference moves model computation to a remote service. Local hardware demands are lower, but the team depends on network latency and the provider’s pricing and availability.
- On-device inference runs models on the player’s or developer’s hardware. NVIDIA documents models optimized for gaming hardware, and some models that can run across GPU, NPU, and CPU. Hardware requirements depend on the specific model and the project, so a studio should check the model’s documentation rather than assume a particular GPU is needed.
NVIDIA’s live documentation lists plugin versions and model access that may change over time. Confirm current versions before planning a build.
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What the survey numbers do and do not show
Several industry surveys report AI use and sentiment. They use different samples and different questions, so they should be read separately rather than combined into one trend.
- Unity, 2024: 62% of surveyed studios said they used AI in their workflows. The report names prototyping, concepting, asset creation, and worldbuilding as main uses.
- Unity, 2024: 63% of surveyed AI adopters used generative technology for asset creation. This is a share of AI adopters, not of all developers.
- Unity, 2025: 79% of developers polled said they felt positive about using AI in gaming. This measures sentiment among respondents, not usage.
- Google, AI Meets The Games Industry, 2025: 36% of respondents were using AI for dynamic level design, animation and rigging, and dialogue writing. The report groups these tasks together, so the figure cannot be read as a separate rate for animation or dialogue.
Read together, the surveys indicate that AI use in asset creation and prototyping is common among those surveyed. They do not establish that character and prop generation has reached a particular level of maturity across the industry.
What the evidence does not establish
- Production readiness without direction. No reviewed source shows generated characters or props shipping without artist direction and review.
- Cross-tool quality. There is no independent, balanced comparison of output quality across vendors.
- Rights and provenance. The sources do not settle licensing, ownership, or the provenance of generated assets. Check each tool’s terms before using generated material commercially.
- Cost and labor outcomes. Comparative production cost and labor savings are not established by independent data. The executive quotes above are self-reported.
- Industry-wide standard. None of these tools is established as a standard across the industry.
How to choose an approach for a specific project
Use these questions to scope the work before comparing tools:
Quick Recap
- Which stage? Concept exploration, asset generation, animation, facial animation, or runtime behavior each need a different tool category.
- Which output type? 2D images, 3D assets, rigging or motion, text, or speech.
- Which integration? Standalone workspace, engine plugin, API, or local SDK.
- Where does inference run? Cloud or on-device, with the hardware implications noted above.
- What are the production constraints? Consistency across assets, editability, rights and provenance, latency, compute cost, and the human review each output will need before it reaches players.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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