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Introducing Mesh

MESH

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141,145 次观看 • 5 个月前 •via X (Twitter)

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Launching something very special today! Introducing Mesh API

Launching something very special today! Introducing Mesh API

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Photoshop AI Agent / MCP example (using Claude), where I couple it with an image vision MCP to have Claude AI rename the layers in a Photoshop file based on the content of the layers. Thanks to Paul Trani for supplying a Photoshop file with a typical naming scheme (none).

Photoshop AI Agent / MCP example (using Claude), where I couple it with an image vision MCP to have Claude AI rename the layers in a Photoshop file based on the content of the layers. Thanks to Paul Trani for supplying a Photoshop file with a typical naming scheme (none).

Mike Chambers

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introducing Clean Mesh Generation. generate 3D models with clean topology, no fixes or cleanup needed. now available for everyone.

introducing Clean Mesh Generation. generate 3D models with clean topology, no fixes or cleanup needed. now available for everyone.

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Introducing: U7 Mesh 🔹 Extended range with redesigned antenna architecture 🔹 Streamlined mounting system 🔹 Same compact footprint Learn more:

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Introducing Embrace. Work in comfort with a sculpted waterfall seat, adjustable headrest, breathable mesh, and full-body ergonomics designed to adapt to you. Available soon:

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Elgato

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Tripo P1.0 is live. Introducing Smart Mesh in Tripo Studio — generate structured 3D meshes in ~2 seconds. ⚡ ~2s generation 🧠 Smart topology 🛠 Production-ready assets 🌍 Built for game pipelines, real-time rendering, web 3D tools, and scalable content production From prompt to usable mesh — almost instantly. Try it: #3DAI #3DCG #3dart #Tripo

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Introducing CozyBlanket Pro A next-generation mesh optimization and cleanup toolkit, built from the ground up with AI-assisted retopology, cutting-edge UV tools, and powerful tool system to transform complex geometry into clean, efficient, production-ready assets.

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🚀 Introducing Articulate Anymesh – now open-sourced! An automated framework behind our Genesis simulator, capable of transforming any rigid 3D mesh into its articulated counterpart using an open-vocabulary manner! Given a 3D mesh, our framework uses VLMs + visual prompting to extract rich semantics — enabling part segmentation and functional joint construction automatically! 🔗 Code: 📄 Paper: 🌐 Project: #EmbodiedAI #3D #OpenSource #VLM #MeshProcessing

🚀 Introducing Articulate Anymesh – now open-sourced! An automated framework behind our Genesis simulator, capable of transforming any rigid 3D mesh into its articulated counterpart using an open-vocabulary manner! Given a 3D mesh, our framework uses VLMs + visual prompting to extract rich semantics — enabling part segmentation and functional joint construction automatically! 🔗 Code: 📄 Paper: 🌐 Project: #EmbodiedAI #3D #OpenSource #VLM #MeshProcessing

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Introducing the B4-mATX • Compact 21.3L mATX SFF design • Horizontal + vertical setup support • Fits GPUs up to 358mm • Supports 360mm AIOs • Compatible with ATX & SFX PSUs • Includes 2 slim 120mm fans Available in Front Mesh and Front Wood finishes #lianli #sff #pc

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🚀 Introducing Meshy-4 — The next-gen 3D model generator is now available to everyone at ✨ Experience dramatically improved mesh geometry in both Image to 3D and Text to 3D workflows. ❤️ Watch the video and discover Meshy’s full potential with us!

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AI agents are getting real jobs now?! Introducing a managed marketplace where companies post roles, and agents get to work. Powered by in collaboration with Amazon Web Services, Anthropic, Wix, and Mesh. There's an agent out there who is the perfect fit for your team! Come and find it 🤖

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🚀 Introducing Meshy-2, our latest release, brings significant enhancements in model quality and user experience, taking text to 3D to a whole new level! 🌈 Enjoy diverse art styles, enhanced geometry and texture details, convenient mesh settings, and more! ✨ Try it now at #textto3d #generativeai #Meshy #GenAI #3DModeling

🚀 Introducing Meshy-2, our latest release, brings significant enhancements in model quality and user experience, taking text to 3D to a whole new level! 🌈 Enjoy diverse art styles, enhanced geometry and texture details, convenient mesh settings, and more! ✨ Try it now at #textto3d #generativeai #Meshy #GenAI #3DModeling

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🚀Introducing Hunyuan3D-PolyGen, our newly upgraded and industry-first art-grade 3D generative model. It brings effortless intelligent retopology, making AI-generated models ready for professional art pipelines. ✅ Superior Mesh Topology: Our self-developed mesh autoregressive model ensures higher-quality mesh topology that meets stringent art standards. ✅ Complex Object Modeling: Leveraging our high-compression BPT representation, we can generate models with 10K+ faces, enabling more complex geometry, higher topology precision, and better detail. ✅ Flexible Output: Supports both tri and quad meshes, meeting diverse pipeline requirements. Hunyuan3D-PolyGen enables direct application of AI-generated 3D assets in game development and significantly boosts artist modeling efficiency. It's a robust foundation for the future of 3D content creation. 👉Try it now:

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Introducing ✨RigidFormer: Learning Rigid Dynamics with Transformers - our attempt to scale learning-based physical dynamics with Transformers. RigidFormer learns rigid dynamics with Transformers. It is a mesh-free, object-centric Transformer for multi-object rigid-body contact dynamics from point clouds. Learning physics with purely neural simulators, without relying on traditional physics engines, is an important and widely studied problem. Prior SOTA methods often use graph neural networks for accuracy and generalization, but still struggle with efficient, high-fidelity simulation at scale. RigidFormer uses only point inputs, matches or outperforms mesh-based baselines on standard benchmarks, runs much faster, generalizes across point resolutions and datasets, and scales to 200+ objects. We also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components. RigidFormer is mesh-free: it does not require mesh connectivity, SDFs, or vertex-level message passing, making it well-suited for point-cloud observations and scalable simulation. This architecture can also be adapted to learn soft-body dynamics by replacing the rigid-body module (differentiable Kabsch alignment). 🎬See our video for more details. Many thanks to my amazing collaborators: Minghao Guo Minghao Guo, Haixu Wu Haixu Wu 吴海旭, Doug Roble, Tuur Stuyck Tuur Stuyck, and Wojciech Matusik Wojciech Matusik. Project page: Paper:

Introducing ✨RigidFormer: Learning Rigid Dynamics with Transformers - our attempt to scale learning-based physical dynamics with Transformers. RigidFormer learns rigid dynamics with Transformers. It is a mesh-free, object-centric Transformer for multi-object rigid-body contact dynamics from point clouds. Learning physics with purely neural simulators, without relying on traditional physics engines, is an important and widely studied problem. Prior SOTA methods often use graph neural networks for accuracy and generalization, but still struggle with efficient, high-fidelity simulation at scale. RigidFormer uses only point inputs, matches or outperforms mesh-based baselines on standard benchmarks, runs much faster, generalizes across point resolutions and datasets, and scales to 200+ objects. We also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components. RigidFormer is mesh-free: it does not require mesh connectivity, SDFs, or vertex-level message passing, making it well-suited for point-cloud observations and scalable simulation. This architecture can also be adapted to learn soft-body dynamics by replacing the rigid-body module (differentiable Kabsch alignment). 🎬See our video for more details. Many thanks to my amazing collaborators: Minghao Guo Minghao Guo, Haixu Wu Haixu Wu 吴海旭, Doug Roble, Tuur Stuyck Tuur Stuyck, and Wojciech Matusik Wojciech Matusik. Project page: Paper:

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Hello AI enthusiasts!👋 I’d like to share that we’re introducing the latest 3D foundation AI model AssetGen 2.0, which was designed to create high-quality 3D assets from text and image prompts. 💡AssetGen 2.0 consist of 2 models: one to generate the 3D Mesh, & a second one to generate textures. ℹ️ Technological Advancements: - Utilizes a single-stage 3D diffusion model for geometry estimation, leading to improved detail and fidelity compared to its predecessor, AssetGen 1.0. - TextureGen introduces methods for enhanced view consistency, texture in-painting, and higher texture resolution. 📌 Current Use and Future Plans: - Currently employed internally for creating 3D worlds. - Planned rollout to Horizon creators later this year. 👉 More details about this announcement: #Meta #AI #GenAI

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The goose team from Spiral joined us last night to introduce the Goose Development Kit (GDK) They walk through what goose is, why it's evolving into a toolkit for building open-source agentic apps, and what’s coming next Full talk ↓ Timestamps: 0:00 - Introducing Presidio Bitcoin and the Open Source AI Summit 1:40 - Why Spiral (Spiral) is expanding into AI with goose (Steve Lee) 4:59 - Introducing the Goose Development Kit (GDK) 10:34 - Goose's path from app to platform (Alex Hancock) 14:40 - Making agents easier to build (Douwe Osinga) 21:10 - Shipping goose as Rust APIs (Jack Amadeo) 25:15 - Building apps on top of goose (Lifei Z) 32:08 - Local models and open weights in goose (Jasper Hugo) 41:12 - Cutting token costs with routing (mic) 43:30 - Mesh-LLM: sharing local AI compute 47:53 - Q&A: Sharing compute across devices 50:20 - Finding hidden token costs (Filip Kujawa) 54:58 - Q&A: What makes goose different 59:17 - Q&A: Why GDK can stay neutral 1:06:52 - Q&A: Benchmarking harnesses without overfitting 1:10:48 - Q&A: Should goose train its own models? 1:15:20 - Q&A: Building specialized apps on goose

The goose team from Spiral joined us last night to introduce the Goose Development Kit (GDK) They walk through what goose is, why it's evolving into a toolkit for building open-source agentic apps, and what’s coming next Full talk ↓ Timestamps: 0:00 - Introducing Presidio Bitcoin and the Open Source AI Summit 1:40 - Why Spiral (Spiral) is expanding into AI with goose (Steve Lee) 4:59 - Introducing the Goose Development Kit (GDK) 10:34 - Goose's path from app to platform (Alex Hancock) 14:40 - Making agents easier to build (Douwe Osinga) 21:10 - Shipping goose as Rust APIs (Jack Amadeo) 25:15 - Building apps on top of goose (Lifei Z) 32:08 - Local models and open weights in goose (Jasper Hugo) 41:12 - Cutting token costs with routing (mic) 43:30 - Mesh-LLM: sharing local AI compute 47:53 - Q&A: Sharing compute across devices 50:20 - Finding hidden token costs (Filip Kujawa) 54:58 - Q&A: What makes goose different 59:17 - Q&A: Why GDK can stay neutral 1:06:52 - Q&A: Benchmarking harnesses without overfitting 1:10:48 - Q&A: Should goose train its own models? 1:15:20 - Q&A: Building specialized apps on goose

Presidio Bitcoin

124,174 次观看 • 18 天前