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From studio tool to industry infrastructure: How generative AI is quietly rewriting 3D production
As generative AI continues to evolve past the hype cycle, its real momentum is forming in the less-glamorous corners of industry—within asset pipelines, production workflows, and iterative design loops. Nowhere is this more apparent than in 3D content creation, where the next wave of generative tools is not just automating creativity, but restructuring how studios and developers approach their work.
Earlier this year, a new benchmark quietly signaled this transition. On LMSYS’s Chatbot Arena—a widely respected crowd-ranked leaderboard hosted on Hugging Face—an AI model from Tencent, called Hunyuan Turbo S, entered the global top 10, ranking just behind DeepSeek among Chinese-developed models. The model stood out not only for its dialogue fluency and coding ability, but also for being part of a broader multi-modal AI ecosystem that spans text, vision, and 3D. While this achievement highlights Tencent’s capabilities, it also reflects a broader maturation across many players in the AI field.
But perhaps more telling than any benchmark is how these models are being used.
At CVPR 2025, Tencent’s AI team open-sourced Hunyuan 3D 2.1, a generative 3D model built for real-world usability rather than demo-stage spectacle. The model—capable of generating high-fidelity mesh geometry and PBR-compatible textures—has since recorded more than 1.8 million downloads. Its applications are broad: in gaming, animation, industrial design, and even autonomous vehicle simulation. Internally, Tencent reports that production time for a game-ready 3D asset dropped from 2–3 days to under 5 hours using Hunyuan 3D, producing assets easier to edit, better lit, and natively structured for engines like Unreal and Unity.
The efficiency gains are real—but Tencent is not alone in pursuing them. Startups like Tripo have found a foothold by targeting stylized character creation, enabling indie developers to quickly build expressive avatars without requiring advanced 3D skills. Meanwhile, Luma AI is pushing the frontier of NeRF-based pipelines, allowing photorealistic 3D scene reconstructions from video input—cutting costs in virtual production and immersive environments.
This orientation toward usability—rather than raw generative novelty—is what increasingly distinguishes today’s leading tools. Meshy, another rising name, has quietly gained traction among AR prototyping teams and small studios for its ability to generate quick layout meshes with minimal cleanup. And because many of these tools decouple geometry and texture generation, they enable more reliable editing and cross-platform reuse—an essential requirement for large-scale production teams.
This focus on pipeline-readiness—rather than pure output quality—marks a turning point. While early 3D generative models suffered from structural noise and low compatibility with industry formats, newer systems are emphasizing topology precision, skeletal rigging, and exportable standards like glTF and USD. That reflects a broader shift across the AI industry: usability is becoming the new frontier.
Stability AI, for example, is building out its 3D capabilities through partnerships with leading CAD software platforms. In architecture and industrial design, these integrations are already reducing modeling hours and enabling generative exploration at scale.
Adobe, meanwhile, is threading generative capabilities deeper into its Creative Cloud suite. Its Firefly models now support 3D texture and material generation directly within tools like Substance Painter, allowing designers to rapidly iterate from prompt to polish. What’s notable is how Firefly is being used not to replace creative intuition, but to accelerate and expand it—an approach that resonates with both solo creators and larger studios. Startups like Scenario and Kaedim are carving out niches by focusing specifically on game asset prototyping. Each of these tools reflects a broader shift in the ecosystem—where companies are tailoring AI solutions to solve specific creative bottlenecks, whether it’s for speed, style control, or ease of integration.
In parallel with these focused innovations, Tencent’s broader AI push is also structured around openness and accessibility. While many of its frontier models remain proprietary, it has also released versions of its Hunyuan series to the open-source community, including full-stack documentation and training data. It has integrated external open models—such as DeepSeek—into its own products like the AI assistant “Yuanbao,” which recently saw usage surge in rural education and blue-collar industries.
In Tencent’s recent earnings call, executives emphasized the company’s long-term bet on “applied AI” — focusing less on model scale and more on how AI tools improve actual services across finance, health, transportation, and education. Its internal tools like Qbot, IMA, and the upgraded AI browser from QQ are now embedding multi-modal capabilities directly into consumer-facing interfaces.
In a field often dominated by headline-grabbing demos, these quieter deployments offer a different perspective: AI is not just about breakthroughs, but about integration. The next wave of competition may not hinge on whose model scores highest—but on whose tool actually gets used.
And in that race, deployment may be the real benchmark.