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Text, logos, and fine details have always been tough for 3D AI - but we’ve cracked it 🚀
Our latest model delivers unprecedented textured mesh quality, and it scales nicely with compute & data.
Coming soon 👀
Common Sense Machines retweeted
skip to 36:26 to see the weird wacky wonderful world @CanessaDCL created in Decentraland with @CSM_ai
absolute insanity
Next-Gen Builders: AI Tools in DCL Worlds X Space xitter.cf/i/broadcasts/1yNxabnzo…
Common Sense Machines retweeted
The Decentraland Art Week Workshop agenda is stacked with step-by-step guides from @ArtiviveApp, @CSM_ai, @SloydAi, and @ImagineArt_X 👏
Come ready to build, experiment, and get inspired.
Here are a few more examples from our upcoming Image-to-3D model, showcasing unprecedented texture quality.
By popular demand, we're sharing more examples that showcase CSM Cube's ability to generate high-quality 3D mesh topology and distinct parts. The results shown were achieved using our single-click Image-to-3D feature, but users can obtain even better quality with careful prompting and editing.
CSM Cube delivers industry-leading model quality, topology, parts, and AI re-topology, providing a significant advantage for workflows from quick prototypes to full production.
Common Sense Machines retweeted
たった1枚の写真から、3Dモデルが「完全再現」される──
@CSM_aiの新しい3D AIモデルが、パーツ単位のメッシュと、PBRテクスチャを自動生成。CSM Cube上ですでに利用可能。
「写真→3D」がかなり実用フェーズになってきている。
We are thrilled to release a new 3D AI model to output parts-based meshes 🧩 with baked and PBR textures 🎨 from single images 📸
Available now to everyone on CSM Cube. 🎨🌐
Common Sense Machines retweeted
Special thanks to @GoogleDeepMind for inviting me to try out Genie 3. I'm excited to share my thoughts on this early research prototype and also some of my live recordings below:
I spent the whole day playing with the system and when it works, it is truly mind blowing🤯. It is the first neural game engine / world model I have tried that generalizes so well and has long term world consistency. Here’s a couple of examples from my live recording and some thoughts on what it means for the future of gaming, robotics, digital experiences and ASI.
Where it shines:
- Truly general-purpose and quick startup time. Works exceptionally well for gaming environments but also generalizes to other industrial and real-world scenarios.
- It learns physics. Although there are systematic failures even for rigid body physics, it was clear to me that it can learn game engine and non-rigid physics without an underlying engine (and in limit learn from game engines via training data).
- It works exceptionally well for stylized environments with characters walking around. This will have implications for concept artists, level designers and game devs.
- It is way more fun than video models, indicating that there are high retention consumer experiences waiting to be built with this in the future
- Photorealistic walk throughs and drone shots work exceptionally well
- Global illumination and lighting works surprisingly well
- Visual memory is quite powerful and the same objects approximately remain coherent under occlusion and longer time horizons
Open Problems:
- Physics is still hard and there are obvious failure cases when I tried the classical intuitive physics experiments from psychology (tower of blocks).
- Social and multi-agent interactions are tricky to handle. 1vs1 combat games do not work
- Long instruction following and simple combinatorial game logic fails (e.g. collect some points / keys etc, go to the door, unlock and so on)
- Action space is limited
- It is far from being a real game engines and has a long way to go but this is a clear glimpse into the future.
The Future:
- It is impressive enough for me to have strong conviction that this is going to disrupt the gaming industry. It is super early days and there are a lot of failures but the writing is on the wall. Lots of challenging scientific, engineering and scaling problems to be solved but it is going to happen in the next 5 years.
- This is the final piece before we get full AGI and now I think we are well on our way to truly solve it once something like this is scaled up. In many ways it is more ASI than AGI but this is a matter of definitions. The fidelity and generalizability will reach human-level and quickly surpass humans
- People are going to combine this with 3D AI and LLMs to build AAA games.
Common Sense Machines retweeted
「1枚の画像」から、ここまでの3Dが作れる時代です。
@CSM_aiを利用したデモ。まずは、画像1枚から高精度な3Dシーンを生成。さらに、Parts with auto assemblyでパーツ毎に書き出し、Chat to 3Dを組み合わせて調整。素晴らしい精度。
もはやモデリングは「入力」次第。
In this quick and easy video, we will show you how to use our tools to create a detailed 3D scene. 🌐🖌️
Our tools used in this process:
- Parts with auto assembly (3D Generation with multiple parts)
- Single image to 3D: Turbo & Base Model (Basic 3D Generation)
- Chat to 3D (Image generation)
CSM Cube now supports highly complex 3D assets with better adaptive topology. Our underlying models now maximize accuracy and minimize poly-counts at the part-level.🎨🖌️
Steps:
- Image to Kit (optional)
- Kit or single images to parts with auto-assembly
- AI re-topology
CSM Cube has been upgraded to support single image-to-parts generation with AI re-topology.🧊⚡
Get up to 30 parts per asset, each with adaptive poly counts. Perfect for fast, high-quality 3D workflows.🎨🚀
Common Sense Machines retweeted
CSM aiで久しぶり遊んでみたら普通に良さそうなの出てきた!!!
UEとかで飛ばせそう!!!
We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free.
☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need.
✅ A parts-based approach enables high-resolution meshes.
🌐 Quad and Triangle mesh support.
Access now: 3d.csm.ai/
Common Sense Machines retweeted
Generative 3D graphics making big strides!
We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free.
☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need.
✅ A parts-based approach enables high-resolution meshes.
🌐 Quad and Triangle mesh support.
Access now: 3d.csm.ai/
アートグレードの3Dジェネレーティブモデルにおける新たな飛躍をリリースできることを大変嬉しく思います。ワンクリックモデルパイプラインは、メッシュパーツベースのトポロジーを備えた、かつてないメッシュ出力を実現します。Cubeの全プランで今すぐ無料でご利用いただけます。
☑️ 多段階階層型AIモデルは、適応型ポリゴン数を備えた完全組み立ての3Dメッシュを生成し、必要なクリーンで分離されたトポロジーを提供します。
✅ パーツベースのアプローチにより、高解像度メッシュを実現します。
🌐 四角形と三角形のメッシュをサポート。
今すぐアクセス:3d.csm.ai
We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free.
☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need.
✅ A parts-based approach enables high-resolution meshes.
🌐 Quad and Triangle mesh support.
Access now: 3d.csm.ai/













