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Surflo. turns a few standard photos into clean 3D models for game dev/VR. - creates usable meshes - geometric consistency - predicts millions of oriented surface points - beats VGGT-1B and DepthAnything-3

Wildminder

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15,486 views • 2 months ago •via X (Twitter)

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GenRecon turns a handful of room photos into professional-grade 3D models. Nice for games, VR, and architectural design. - PBR meshes from sparse RGB images - Trellis.2+DINOv3 - partitions large scenes into overlapping chunks for global consistency

GenRecon turns a handful of room photos into professional-grade 3D models. Nice for games, VR, and architectural design. - PBR meshes from sparse RGB images - Trellis.2+DINOv3 - partitions large scenes into overlapping chunks for global consistency

Wildminder

13,597 views • 3 months ago

Super clean and efficient meshes by an AI? YES! The typical 3D Generative AI solutions produce lots of artifacts and usually way to many polygons due to volumetric approaches. In comparison “MeshGPT creates triangle meshes by autoregressively sampling from a transformer model that has been trained to produce tokens from a learned geometric vocabulary. These tokens can then be decoded into the faces of a triangle mesh. This method generates clean, coherent, and compact meshes, characterized by sharp edges and high fidelity.” Surely it is limited by the trained vocabulary but various versions can be trained for specific sets to create generative model libraries for certain object groups. Very promising approach with the high quality.

Super clean and efficient meshes by an AI? YES! The typical 3D Generative AI solutions produce lots of artifacts and usually way to many polygons due to volumetric approaches. In comparison “MeshGPT creates triangle meshes by autoregressively sampling from a transformer model that has been trained to produce tokens from a learned geometric vocabulary. These tokens can then be decoded into the faces of a triangle mesh. This method generates clean, coherent, and compact meshes, characterized by sharp edges and high fidelity.” Surely it is limited by the trained vocabulary but various versions can be trained for specific sets to create generative model libraries for certain object groups. Very promising approach with the high quality.

René Schulte

20,772 views • 2 years ago

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

AK

66,533 views • 2 years ago

🚀🚀🚀Hunyuan 3D Studio just leveled up to 1.1! We've integrated the art-grade 3D generative model, Hunyuan 3D-PolyGen 1.5, to deliver the industry's most advanced mesh quality directly to your workflow. 🎨 🖌️ Art-Grade Quad Mesh: We've pioneered an end-to-end native quad mesh generation method. Unlike older methods that generated only tri-meshes, PolyGen 1.5 directly learns quad topology to produce cleaner, continuous edge loops and superior wireframe quality. 🎮 Professional Viability: Achieving this topology standard makes models instantly production-ready for game artists, 3D designers, and developers across game development, animation, and VR content creation. ⚙️ Flexible Output: PolyGen 1.5 supports both Quad and Triangular Topology, ensuring viability for both soft-surface and hard-surface models in professional pipelines. PolyGen 1.5 sets a new SOTA in stability, detail, and wireframe quality. Explore Hunyuan 3D Studio 1.1 and see the results:

🚀🚀🚀Hunyuan 3D Studio just leveled up to 1.1! We've integrated the art-grade 3D generative model, Hunyuan 3D-PolyGen 1.5, to deliver the industry's most advanced mesh quality directly to your workflow. 🎨 🖌️ Art-Grade Quad Mesh: We've pioneered an end-to-end native quad mesh generation method. Unlike older methods that generated only tri-meshes, PolyGen 1.5 directly learns quad topology to produce cleaner, continuous edge loops and superior wireframe quality. 🎮 Professional Viability: Achieving this topology standard makes models instantly production-ready for game artists, 3D designers, and developers across game development, animation, and VR content creation. ⚙️ Flexible Output: PolyGen 1.5 supports both Quad and Triangular Topology, ensuring viability for both soft-surface and hard-surface models in professional pipelines. PolyGen 1.5 sets a new SOTA in stability, detail, and wireframe quality. Explore Hunyuan 3D Studio 1.1 and see the results:

Tencent Hy

146,533 views • 9 months ago

Excited to share this demo from Over the Reality! Watch our Unitree Go2 robodog navigating our office while reconstructing the 3D space in real-time using the VGGT-based foundation vision model. This is a prime example of machine perception in action, turning raw RGB camera feeds into rich, detailed 3D maps! The robodog's RGB cam generates a dense, textured 3D reconstruction via VGGT from a few photograms, capturing nuances like object shapes and surfaces with impressive fidelity (main view). Compare that to the standard LiDAR system (top right), it's sparser, more point-cloud focused, lacking the visual richness. Vision models are closing the gap fast! What's powering this? VGGT, a cutting-edge foundation model for 3D perception, trained on datasets orders of magnitude smaller than our massive OVER 3D maps dataset. Imagine the leap when we apply OVER's scale to VGGT-like transformer based architectures, denser reconstructions, better generalization, revolutionary for robotics, machine perception & AR! Stay tuned for more breakthroughs at the intersection of AI, robotics, and DePIN. We're building the future of Physical AI and Spatial Computing at Over the Reality 🌐 What do you think, ready for robodogs in your world? Drop your thoughts! 🤖🌐

Excited to share this demo from Over the Reality! Watch our Unitree Go2 robodog navigating our office while reconstructing the 3D space in real-time using the VGGT-based foundation vision model. This is a prime example of machine perception in action, turning raw RGB camera feeds into rich, detailed 3D maps! The robodog's RGB cam generates a dense, textured 3D reconstruction via VGGT from a few photograms, capturing nuances like object shapes and surfaces with impressive fidelity (main view). Compare that to the standard LiDAR system (top right), it's sparser, more point-cloud focused, lacking the visual richness. Vision models are closing the gap fast! What's powering this? VGGT, a cutting-edge foundation model for 3D perception, trained on datasets orders of magnitude smaller than our massive OVER 3D maps dataset. Imagine the leap when we apply OVER's scale to VGGT-like transformer based architectures, denser reconstructions, better generalization, revolutionary for robotics, machine perception & AR! Stay tuned for more breakthroughs at the intersection of AI, robotics, and DePIN. We're building the future of Physical AI and Spatial Computing at Over the Reality 🌐 What do you think, ready for robodogs in your world? Drop your thoughts! 🤖🌐

Over the Reality 🌐

359,717 views • 11 months ago

Tested this concepting workflow. ▶️ Converting a level design image to a 3D asset gallery image ▶️ re-generating segmented subjects into separate high quality images. ▶️ using these images to generate the 3D meshes. First time using 'for loops' in ComfyUI, works great and the prompts are updated for every segment. I used the Nano Banana 2 API node in ComfyUI to generate the final high quality images. Those SAM3 nodes are crazy btw, just set some points and it creates a mask! For the 3D model generation I used the new Tripo Smart Mesh tool, which is fast! The output is not perfect yet. I think in a lot of cases not production ready right now but that depends on the input image and the goal as well. For pre-production / concepting phase it's perfect and for far distance meshes it’s fine. I’m sure these tools keep improving, curious what we can work with in 3-6 months. Minimal cleanup and scaling in Blender and tested the meshes in UEFN / Unreal Engine. After creating the ComfyUI workflow it took less than an hour to do everything.

Tested this concepting workflow. ▶️ Converting a level design image to a 3D asset gallery image ▶️ re-generating segmented subjects into separate high quality images. ▶️ using these images to generate the 3D meshes. First time using 'for loops' in ComfyUI, works great and the prompts are updated for every segment. I used the Nano Banana 2 API node in ComfyUI to generate the final high quality images. Those SAM3 nodes are crazy btw, just set some points and it creates a mask! For the 3D model generation I used the new Tripo Smart Mesh tool, which is fast! The output is not perfect yet. I think in a lot of cases not production ready right now but that depends on the input image and the goal as well. For pre-production / concepting phase it's perfect and for far distance meshes it’s fine. I’m sure these tools keep improving, curious what we can work with in 3-6 months. Minimal cleanup and scaling in Blender and tested the meshes in UEFN / Unreal Engine. After creating the ComfyUI workflow it took less than an hour to do everything.

Jerome | InsaneUnreal

17,461 views • 5 months ago

Flexible Isosurface Extraction for Gradient-Based Mesh Optimization paper page: This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local flexible adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.

Flexible Isosurface Extraction for Gradient-Based Mesh Optimization paper page: This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local flexible adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.

AK

38,452 views • 3 years ago

Well, it appears that turning a Gaussian Splat into a #3D mesh was not that hard after all 😅. I have recently tried an app called KIRI Engine - 3D Scanner App and it offers several nice features that for me made it stand out from many other scanning apps: - It supports both photos and videos - You can clean up the splat directly in the app - It works on both Android, iOS and web! - BUT MOST IMPORTANTLY you can export the mesh making gaussian splitting a much more usable format for various production pipelines. I will be interviewing Jack Wang, the CEO of Kiri for an upcoming episode of XR AI Spotlight: What questions should I ask him?

Well, it appears that turning a Gaussian Splat into a #3D mesh was not that hard after all 😅. I have recently tried an app called KIRI Engine - 3D Scanner App and it offers several nice features that for me made it stand out from many other scanning apps: - It supports both photos and videos - You can clean up the splat directly in the app - It works on both Android, iOS and web! - BUT MOST IMPORTANTLY you can export the mesh making gaussian splitting a much more usable format for various production pipelines. I will be interviewing Jack Wang, the CEO of Kiri for an upcoming episode of XR AI Spotlight: What questions should I ask him?

Gabriele Romagnoli

22,997 views • 1 year ago

[NeurIPS '24] DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation Abstract (excerpt) We introduce DreamMesh4D, a novel framework that combines mesh representation with sparse-controlled deformation technique to generate high-quality 4D object from a monocular video. To overcome the limitation of classical texture representation, we bind Gaussian splats to the surface of the triangular mesh for differentiable optimization of both the texture and mesh vertices. In particular, DreamMesh4D begins with a coarse mesh provided by a single image based 3D generation method. Sparse points are then uniformly sampled across the surface of the mesh, and are used to build a deformation graph to drive the motion of the 3D object for the sake of computational efficiency and providing additional constraint. For each step, transformations of sparse control points are predicted using a deformation network, and the mesh vertices as well as the bound surface Gaussians are deformed via a geometric skinning algorithm. The skinning algorithm is a hybrid approach combining LBS (linear blending skinning) and DQS (dual-quaternion skinning), mitigating drawbacks associated with both approaches. The static surface Gaussians and mesh vertices as well as the dynamic deformation network are learned via reference view photometric loss, score distillation loss as well as other regularization losses in a two-stage manner. Extensive experiments demonstrate that our method outperforms prior video-to-4D generation methods in terms of rendering quality and spatial-temporal consistency.

[NeurIPS '24] DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation Abstract (excerpt) We introduce DreamMesh4D, a novel framework that combines mesh representation with sparse-controlled deformation technique to generate high-quality 4D object from a monocular video. To overcome the limitation of classical texture representation, we bind Gaussian splats to the surface of the triangular mesh for differentiable optimization of both the texture and mesh vertices. In particular, DreamMesh4D begins with a coarse mesh provided by a single image based 3D generation method. Sparse points are then uniformly sampled across the surface of the mesh, and are used to build a deformation graph to drive the motion of the 3D object for the sake of computational efficiency and providing additional constraint. For each step, transformations of sparse control points are predicted using a deformation network, and the mesh vertices as well as the bound surface Gaussians are deformed via a geometric skinning algorithm. The skinning algorithm is a hybrid approach combining LBS (linear blending skinning) and DQS (dual-quaternion skinning), mitigating drawbacks associated with both approaches. The static surface Gaussians and mesh vertices as well as the dynamic deformation network are learned via reference view photometric loss, score distillation loss as well as other regularization losses in a two-stage manner. Extensive experiments demonstrate that our method outperforms prior video-to-4D generation methods in terms of rendering quality and spatial-temporal consistency.

MrNeRF

12,323 views • 1 year ago

WOW 😮 Caitlin Clark silenced 🤫 all of her haters last night‼️ After being the target of attacks from jealous and racist players. After being snubbed in All-Star voting. After the WNBA left her off of promotional material despite her being their biggest star. She just made history. 45 points and 10 assists in a single game. Let’s take a quick look at just a few of the records she broke: 1. First WNBA player with 40 points and 10 assists in a game. 2. First WNBA player to score 40+ points in fewer than 30 minutes. 3. Fastest player in WNBA history to score 200 3 point shots. 4. Franchise points record for the Fever. Turns out that her haters are just mad because she’s really good at basketball.

WOW 😮 Caitlin Clark silenced 🤫 all of her haters last night‼️ After being the target of attacks from jealous and racist players. After being snubbed in All-Star voting. After the WNBA left her off of promotional material despite her being their biggest star. She just made history. 45 points and 10 assists in a single game. Let’s take a quick look at just a few of the records she broke: 1. First WNBA player with 40 points and 10 assists in a game. 2. First WNBA player to score 40+ points in fewer than 30 minutes. 3. Fastest player in WNBA history to score 200 3 point shots. 4. Franchise points record for the Fever. Turns out that her haters are just mad because she’s really good at basketball.

Cinema Shogun

12,328 views • 1 month ago

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,524 views • 8 months ago

A 23-year-old game modder from Prague uploaded one image of a battle axe into Claude Opus 4.8 and built a $6,200/month 3D asset factory without opening a single modeling tab he let the model write the entire geometry script for Blender from scratch the workflow that prints ready-to-sell assets: > reference upload: drop a high-res weapon concept into the chat window > reasoning layer: Claude Opus 4.8 analyzes the edges, weight distribution, and scale > code generation: the model spits out a flawless python script in 13 minutes using 5,000 tokens > render loop: the code executes inside Blender, auto-generating complex 3D meshes with surface imperfections he found a minor shading bug, fixed it manually in 40 seconds, and uploaded the finished axe to TurboSquid and Sketchfab Earnings breakout: Week 1 > $1,120 Week 2 > $1,450 Week 3 > $1,780 Week 4 > $1,850 1 month > $6,200 net profit > most 3D artists are still spending 14 hours extruding vertices manually and crying about industry layoffs he bypassed the entire pipeline for the price of an API key

A 23-year-old game modder from Prague uploaded one image of a battle axe into Claude Opus 4.8 and built a $6,200/month 3D asset factory without opening a single modeling tab he let the model write the entire geometry script for Blender from scratch the workflow that prints ready-to-sell assets: > reference upload: drop a high-res weapon concept into the chat window > reasoning layer: Claude Opus 4.8 analyzes the edges, weight distribution, and scale > code generation: the model spits out a flawless python script in 13 minutes using 5,000 tokens > render loop: the code executes inside Blender, auto-generating complex 3D meshes with surface imperfections he found a minor shading bug, fixed it manually in 40 seconds, and uploaded the finished axe to TurboSquid and Sketchfab Earnings breakout: Week 1 > $1,120 Week 2 > $1,450 Week 3 > $1,780 Week 4 > $1,850 1 month > $6,200 net profit > most 3D artists are still spending 14 hours extruding vertices manually and crying about industry layoffs he bypassed the entire pipeline for the price of an API key

Mr. Buzzoni

20,877 views • 2 months ago

A 20-year-old turns screenshots into kids YouTube videos that make $3,000 for every 100,000 views. He finds a channel already pulling millions of views, screenshots the page and uploads it to Claude. Claude studies the colors, character rhythm, pacing and moments designed to trigger another replay. Higgsfield is connected through MCP, so the finished prompt goes directly into video generation. A few minutes later, an original 3D animation appears inside the same chat ready to publish. He is not asking AI to guess what children might watch. The screenshot is a map of demand that YouTube already proved.

A 20-year-old turns screenshots into kids YouTube videos that make $3,000 for every 100,000 views. He finds a channel already pulling millions of views, screenshots the page and uploads it to Claude. Claude studies the colors, character rhythm, pacing and moments designed to trigger another replay. Higgsfield is connected through MCP, so the finished prompt goes directly into video generation. A few minutes later, an original 3D animation appears inside the same chat ready to publish. He is not asking AI to guess what children might watch. The screenshot is a map of demand that YouTube already proved.

Woody

211,438 views • 20 days ago

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

Om Patel

157,824 views • 3 months ago

Microsoft open-sourced a 4B model that turns any image into a production-ready 3D asset in 3 seconds. It’s called TRELLIS.2, a fully textured, physically accurate 3D models with PBR textures out of the box. → Full PBR (base color, roughness, metallic, opacity) → Handles hair, cloth, glass, non-manifold geometry → Exports .glb ready for Unity/Unreal/Blender → Runs locally, ships in 3 seconds It's not a demo or a research preview. The full training codebase is public. You can fine-tune it on your own asset library and get a model that generates in your studio's exact style. 100% Open Source

Microsoft open-sourced a 4B model that turns any image into a production-ready 3D asset in 3 seconds. It’s called TRELLIS.2, a fully textured, physically accurate 3D models with PBR textures out of the box. → Full PBR (base color, roughness, metallic, opacity) → Handles hair, cloth, glass, non-manifold geometry → Exports .glb ready for Unity/Unreal/Blender → Runs locally, ships in 3 seconds It's not a demo or a research preview. The full training codebase is public. You can fine-tune it on your own asset library and get a model that generates in your studio's exact style. 100% Open Source

Superman

282,217 views • 1 month ago

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

@levelsio

72,338 views • 4 months ago

WE ARE WATCHING THE TRADITIONAL 3D RENDERING PIPELINE GET REPLACED IN REAL TIME just dropped an amazing early experiment called PixVerse Game. Instead of hardcoding mechanics to specific character models, they figured out how to completely decouple a game's underlying math from what you actually see on screen. Here is how their new 3-layer architecture works: ✦ The Engine → handles the raw rules, objectives, and health points. ✦ The Agent → uses AI to translate a player's natural language into valid game moves. ✦ The Visuals → an underlying R1 model generates the world and audio on the fly. Why does this matter? Because you can take the exact same game rules and swap the world instantly. (e.g., swapping from a sci-fi mission to an anime adventure without rewriting the game). This is a fascinating look at where gaming is heading! You can try PixVerse Game here → Currently invite-only, but I snagged a few access codes for y'all :) Code 1: CTQLMY Code 2: LQCAYS Enjoy!

WE ARE WATCHING THE TRADITIONAL 3D RENDERING PIPELINE GET REPLACED IN REAL TIME just dropped an amazing early experiment called PixVerse Game. Instead of hardcoding mechanics to specific character models, they figured out how to completely decouple a game's underlying math from what you actually see on screen. Here is how their new 3-layer architecture works: ✦ The Engine → handles the raw rules, objectives, and health points. ✦ The Agent → uses AI to translate a player's natural language into valid game moves. ✦ The Visuals → an underlying R1 model generates the world and audio on the fly. Why does this matter? Because you can take the exact same game rules and swap the world instantly. (e.g., swapping from a sci-fi mission to an anime adventure without rewriting the game). This is a fascinating look at where gaming is heading! You can try PixVerse Game here → Currently invite-only, but I snagged a few access codes for y'all :) Code 1: CTQLMY Code 2: LQCAYS Enjoy!

Charly Wargnier

74,853 views • 1 month ago

TWO DEVS BUILT A FULL MULTIPLAYER 3D GAME WITH WEBGL + THREE.JS - IN 5.7MB. NO UNITY. NO INSTALL. "Messenger" by Abeto is a planetary-delivery game: you're a kid on a tiny round world, dropping off mail, bumping into real players, waving with emojis. It runs entirely in a browser tab. The part that breaks your brain is the size. The whole thing loads in ~5.7MB. Not 5.7GB. Megabytes. You click a link and you're already playing - no download, no launcher, no account. what's under the hood: → WebGL + Three.js as the core - no Unity, no Godot → three-mesh-bvh for fast collisions on the curved planet → 3D models in Houdini + Blender, textures from Substance → real-time multiplayer over WebSockets on Node.js → lo-fi soundtrack streamed in, not bundled for scale: → AAA games ship at 50-150GB → this is ~10,000× lighter → install time for most 3D games: minutes → this: ~0 seconds → platform needed: none. it's a URL. We spent a decade saying "real games need a 100GB download and a gaming rig." A two-person studio just shipped a multiplayer 3D world that fits in the size of a few photos and opens in a tab. The browser quietly became a game console. Most people haven't noticed yet. 🔗 Link:

TWO DEVS BUILT A FULL MULTIPLAYER 3D GAME WITH WEBGL + THREE.JS - IN 5.7MB. NO UNITY. NO INSTALL. "Messenger" by Abeto is a planetary-delivery game: you're a kid on a tiny round world, dropping off mail, bumping into real players, waving with emojis. It runs entirely in a browser tab. The part that breaks your brain is the size. The whole thing loads in ~5.7MB. Not 5.7GB. Megabytes. You click a link and you're already playing - no download, no launcher, no account. what's under the hood: → WebGL + Three.js as the core - no Unity, no Godot → three-mesh-bvh for fast collisions on the curved planet → 3D models in Houdini + Blender, textures from Substance → real-time multiplayer over WebSockets on Node.js → lo-fi soundtrack streamed in, not bundled for scale: → AAA games ship at 50-150GB → this is ~10,000× lighter → install time for most 3D games: minutes → this: ~0 seconds → platform needed: none. it's a URL. We spent a decade saying "real games need a 100GB download and a gaming rig." A two-person studio just shipped a multiplayer 3D world that fits in the size of a few photos and opens in a tab. The browser quietly became a game console. Most people haven't noticed yet. 🔗 Link:

ZEUS⚡️

50,596 views • 2 months ago

Black Forest Labs just announced FLUX 3: a unified multimodal model for image, video, audio and action prediction. Founded in Freiburg, Germany, one of the few globally significant European companies in the AI sector. The current release is gated early access, not open source. But if FLUX 3 Dev actually ships with usable open weights, this could become one of the most important open-model releases in generative video yet. Especially after the Chinese Minimax H3 release. BFL claims, based on preliminary internal comparisons, that FLUX 3 outperformed models such as Runway Gen-4.5, Luma Ray 3.2, Kling 3 Pro, and Seedance 2.0. However, these figures stem from the early-access phase and do not constitute independent validation. Be that as it may, it's great to see the quality that today's video models can produce at affordable prices. Something that would have been unthinkable a year ago. Really cool release!

Black Forest Labs just announced FLUX 3: a unified multimodal model for image, video, audio and action prediction. Founded in Freiburg, Germany, one of the few globally significant European companies in the AI sector. The current release is gated early access, not open source. But if FLUX 3 Dev actually ships with usable open weights, this could become one of the most important open-model releases in generative video yet. Especially after the Chinese Minimax H3 release. BFL claims, based on preliminary internal comparisons, that FLUX 3 outperformed models such as Runway Gen-4.5, Luma Ray 3.2, Kling 3 Pro, and Seedance 2.0. However, these figures stem from the early-access phase and do not constitute independent validation. Be that as it may, it's great to see the quality that today's video models can produce at affordable prices. Something that would have been unthinkable a year ago. Really cool release!

Chubby♨️

39,869 views • 25 days ago