Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Introducing Mesh

meshoptical's profile picture

MESH

1,405 subscribers

143,425 görüntüleme • 6 ay önce •via X (Twitter)

Bilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Launching something very special today! Introducing Mesh API
11:30

Launching something very special today! Introducing Mesh API

Dhruv Rathee

707,258 görüntüleme • 2 ay önce

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).
0:37

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

16,689 görüntüleme • 1 yıl önce

introducing Clean Mesh Generation. generate 3D models with clean topology, no fixes or cleanup needed. now available for everyone.
0:34

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

Customuse

811,318 görüntüleme • 5 ay önce

Introducing: U7 Mesh 🔹 Extended range with redesigned antenna architecture 🔹 Streamlined mounting system 🔹 Same compact footprint Learn more:
1:05

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

Ubiquiti Inc

34,710 görüntüleme • 6 ay önce

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:
0:42

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:

Elgato

166,765 görüntüleme • 11 ay önce

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
0:00

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

Tripo

44,954 görüntüleme • 6 ay önce

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.
0:55

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.

Sparseal

476,460 görüntüleme • 3 ay önce

🚀 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
0:22

🚀 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

Chuang Gan

35,936 görüntüleme • 1 yıl önce

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
0:58

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

LIAN LI Global

104,942 görüntüleme • 2 ay önce

🚀 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!
0:54

🚀 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!

MeshyAI

52,529 görüntüleme • 2 yıl önce

Introducing Cube 2.0, a significant step towards controllable 3D game-world and video generation from any input. Powered by: 🌟 Image-to-3D in seconds w/ a new foundation model 🐎 Text-to-animation 🌎 AI rendering to create beautiful worlds (mesh+splats) Web app → Discord → Blog →
1:11

Introducing Cube 2.0, a significant step towards controllable 3D game-world and video generation from any input. Powered by: 🌟 Image-to-3D in seconds w/ a new foundation model 🐎 Text-to-animation 🌎 AI rendering to create beautiful worlds (mesh+splats) Web app → Discord → Blog →

Common Sense Machines

411,413 görüntüleme • 2 yıl önce

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 🤖
0:43

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 🤖

monday.com

35,696 görüntüleme • 5 ay önce

🚀 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
0:46

🚀 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

MeshyAI

59,838 görüntüleme • 2 yıl önce

🚀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:
1:01

🚀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:

Tencent Hy

161,346 görüntüleme • 1 yıl önce

🔥 Introducing Genie 1.0, our first step towards building multimodal AI. Genie is a text-to-3d model capable of creating any 3d object you can dream of in under 10 seconds with materials, quad mesh retopology, variable polycount, and in all standard formats! Try it on web and in Luma’s iOS app now → #generativeai #textto3d #multimodalai
0:30

🔥 Introducing Genie 1.0, our first step towards building multimodal AI. Genie is a text-to-3d model capable of creating any 3d object you can dream of in under 10 seconds with materials, quad mesh retopology, variable polycount, and in all standard formats! Try it on web and in Luma’s iOS app now → #generativeai #textto3d #multimodalai

Luma

1,242,247 görüntüleme • 2 yıl önce

Broke from flat maps, Introducing Seismic 3D: Built a "Glass Planet" volumetric viz showing earthquakes DEEP in Earth's crust (10x depth scale for subduction zones)! Tech: React + Three.js via React-three-fiber + USGS Earthquakes API feed. Instanced Mesh for 60FPS on 8K+ events! Like/RT : Tag a friend who geeks out on this & share your thoughts below! #ThreeJS #CreativeCoding #DataViz #earthquake #USGS #ESRI
0:30

Broke from flat maps, Introducing Seismic 3D: Built a "Glass Planet" volumetric viz showing earthquakes DEEP in Earth's crust (10x depth scale for subduction zones)! Tech: React + Three.js via React-three-fiber + USGS Earthquakes API feed. Instanced Mesh for 60FPS on 8K+ events! Like/RT : Tag a friend who geeks out on this & share your thoughts below! #ThreeJS #CreativeCoding #DataViz #earthquake #USGS #ESRI

SHAHNAB AHMED

40,126 görüntüleme • 6 ay önce

Every network begins with its first peers. Introducing the Founding Inference Contributors that are integrated at Halo: Venice private, uncensored inference 0G Labs (Home of Infinite AI) decentralized AI infrastructure NEAR AI fully confidential compute through TEEs And our launch partner Virtuals Protocol, bringing the agent economy to the demand side and enabling the HALO token launch. Supply and demand, seeded from day one. The mesh starts seeded.
0:22

Every network begins with its first peers. Introducing the Founding Inference Contributors that are integrated at Halo: Venice private, uncensored inference 0G Labs (Home of Infinite AI) decentralized AI infrastructure NEAR AI fully confidential compute through TEEs And our launch partner Virtuals Protocol, bringing the agent economy to the demand side and enabling the HALO token launch. Supply and demand, seeded from day one. The mesh starts seeded.

Warden / Halo

38,703 görüntüleme • 1 ay önce

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:
0:26

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:

Zhiyang (Frank) Dou

573,749 görüntüleme • 4 ay önce