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img2threejs - turn one object photo into a code-only procedural Three.js model Open-source toolkit that rebuilds the object in a single reference image as procedural Three.js code (no mesh downloads), quality-gated by a render-vs-reference loop - strong for hard-surface objects

Nick

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287,869 次观看 • 9 天前 •via X (Twitter)

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img2threejs v1.3 is now available. 🎋 One photo → procedural Three.js code. No meshes. No manual modeling. GitHub: v1.3 brings major improvements to geometry reconstruction, material generation, validation, and overall output quality. For this demo, I used the reference image of a CS2 ★ M9 Bayonet | Doppler Phase 3: If you’re new to img2threejs, now is a great time to try it. If you’ve used it before, I’d love for you to compare the results with previous versions. Feedback, bug reports, and PRs are always welcome.

img2threejs v1.3 is now available. 🎋 One photo → procedural Three.js code. No meshes. No manual modeling. GitHub: v1.3 brings major improvements to geometry reconstruction, material generation, validation, and overall output quality. For this demo, I used the reference image of a CS2 ★ M9 Bayonet | Doppler Phase 3: If you’re new to img2threejs, now is a great time to try it. If you’ve used it before, I’d love for you to compare the results with previous versions. Feedback, bug reports, and PRs are always welcome.

Nick

652,210 次观看 • 4 天前

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

PyCharm, a JetBrains IDE

34,908 次观看 • 2 个月前

Introducing 📦𝗔𝗿𝘁𝗶𝗟𝗮𝘁𝗲𝗻𝘁🔧 (SIGGRAPH Asia 2025) — a high-quality 3D diffusion model that explicitly models object articulation, paving the way for richer, more realistic assets in embodied AI and simulation: – Generates fully articulated 3D objects – Physically plausible joints & motion – High-fidelity 3D Gaussian appearance – Supports generation from a single real image arXiv: Project: Code (coming soon):

Introducing 📦𝗔𝗿𝘁𝗶𝗟𝗮𝘁𝗲𝗻𝘁🔧 (SIGGRAPH Asia 2025) — a high-quality 3D diffusion model that explicitly models object articulation, paving the way for richer, more realistic assets in embodied AI and simulation: – Generates fully articulated 3D objects – Physically plausible joints & motion – High-fidelity 3D Gaussian appearance – Supports generation from a single real image arXiv: Project: Code (coming soon):

Xingang Pan

11,517 次观看 • 8 个月前

Vibe coded an infinite 3D tunnel scroll using Three.js with object pooling and GSAP animations, all created with Google Gemini 3 Pro AI in under 30 minutes :D It’s a fully procedural, mathematically generated world that renders endlessly as you scroll up and down without eating your device memory (that's what at least AI said 😆) Live: Code: now returning to the job that funds my ability to overwork myself :)

Vibe coded an infinite 3D tunnel scroll using Three.js with object pooling and GSAP animations, all created with Google Gemini 3 Pro AI in under 30 minutes :D It’s a fully procedural, mathematically generated world that renders endlessly as you scroll up and down without eating your device memory (that's what at least AI said 😆) Live: Code: now returning to the job that funds my ability to overwork myself :)

The Bugged Dev

95,858 次观看 • 6 个月前

New brand for Relace. Americana adventure, but in the wild west of code. We reference old science books, 1960s trail maps, and created a loop mark that doubles as a lasso.

Nick Pattison

66,294 次观看 • 4 个月前

Nah no, this is not a pre-made or baked Blender model. It is created entirely from scratch using extruded geometry and procedural textures for the mustard hatching pattern on the model... all done with Google Gemini 3 pro AI. In fact, every Three.js 3D model you see on this website has been vibe coded with AI 😎 Live: Code:

Nah no, this is not a pre-made or baked Blender model. It is created entirely from scratch using extruded geometry and procedural textures for the mustard hatching pattern on the model... all done with Google Gemini 3 pro AI. In fact, every Three.js 3D model you see on this website has been vibe coded with AI 😎 Live: Code:

The Bugged Dev

54,391 次观看 • 6 个月前

NVIDIA finally released Neuralangelo's source code! The model can turn videos from any device into detailed 3D structures, fully replicating buildings, sculptures, or other real aworld objects or spaces virtually. Here's how it works: A model utilizes a 2D video with multiple angles of an object or scene. I selects frames from different viewpoints to understand depth, size, and shape. The AI creates an initial 3D representation, similar to a sculptor shaping a subject. The render is optimized to enhance details, like a sculptor refining texture. The outcome is a 3D object or scene suitable for virtual reality, digital twins, or robotics.

NVIDIA finally released Neuralangelo's source code! The model can turn videos from any device into detailed 3D structures, fully replicating buildings, sculptures, or other real aworld objects or spaces virtually. Here's how it works: A model utilizes a 2D video with multiple angles of an object or scene. I selects frames from different viewpoints to understand depth, size, and shape. The AI creates an initial 3D representation, similar to a sculptor shaping a subject. The render is optimized to enhance details, like a sculptor refining texture. The outcome is a 3D object or scene suitable for virtual reality, digital twins, or robotics.

Lior Alexander

478,039 次观看 • 2 年前

Yesterday someone told me that Three.js is a toy and that as soon as the scene grows too much, it gets laggy. imho, nope 😎 Of course, nothing comes for free: • Web Worker for procedural city gen • Static shadow bake • 2D spatial hash for physics • city chunking + frustum culling everywhere • InstancedMesh props • KTX2 textures • Distance-gated water noise Frame time cut in half. VRAM down 80%. #vibejam #gamedev

Yesterday someone told me that Three.js is a toy and that as soon as the scene grows too much, it gets laggy. imho, nope 😎 Of course, nothing comes for free: • Web Worker for procedural city gen • Static shadow bake • 2D spatial hash for physics • city chunking + frustum culling everywhere • InstancedMesh props • KTX2 textures • Distance-gated water noise Frame time cut in half. VRAM down 80%. #vibejam #gamedev

Francesco Di Donato

18,112 次观看 • 3 个月前

MOONSHOT JUST CLONED CLAUDE CODE AND MADE IT FREE. It's called Kimi Code CLI. Open source, MIT license, maintained by the lab that shipped K3 yesterday. And it does things Claude Code doesn't: → drop a screen recording into the chat as input → built-in coder, explore, and plan subagents, each in its own context → plan mode before it touches a single file → MCP servers configured by the agent itself via /mcp-config → plugs into Zed, JetBrains, and VS Code → one binary, no Node setup, starts in milliseconds The CLI costs $0. K3 behind it starts at $3 per million tokens. Grab it for free👇

MOONSHOT JUST CLONED CLAUDE CODE AND MADE IT FREE. It's called Kimi Code CLI. Open source, MIT license, maintained by the lab that shipped K3 yesterday. And it does things Claude Code doesn't: → drop a screen recording into the chat as input → built-in coder, explore, and plan subagents, each in its own context → plan mode before it touches a single file → MCP servers configured by the agent itself via /mcp-config → plugs into Zed, JetBrains, and VS Code → one binary, no Node setup, starts in milliseconds The CLI costs $0. K3 behind it starts at $3 per million tokens. Grab it for free👇

darkzodchi

400,197 次观看 • 9 天前

Massive performance improvement. This is a bit more of technical post, but man do I love this stuff! Units navigate the map using a 'Navigation Mesh'. Before, I was using one giant nav mesh that spanned the entire map. The more objects that were placed (especially on a large map such as this 'RadarAttack' map designed by Syphotic | Steel Command), the larger the 'lag' would be after placement. You can see here that there is a massive frame drop and the navmesh doesnt update for almost 5 seconds. Now, there are a ton of tiny navmeshes that connect to one another, and together they cover the whole map. Now, when an object is placed, the navmesh will update instantly, because it no longer needs to parse through every object on the map (potentially thousands!!!). It only needs to parse through the objects that exist in the mini navmesh that the object was placed in (probably only 1-5 objects now!). Performance XP Boost +100! Charles Horwood You might appreciate this one :) #Rts #RTSGame #IndieGame

Massive performance improvement. This is a bit more of technical post, but man do I love this stuff! Units navigate the map using a 'Navigation Mesh'. Before, I was using one giant nav mesh that spanned the entire map. The more objects that were placed (especially on a large map such as this 'RadarAttack' map designed by Syphotic | Steel Command), the larger the 'lag' would be after placement. You can see here that there is a massive frame drop and the navmesh doesnt update for almost 5 seconds. Now, there are a ton of tiny navmeshes that connect to one another, and together they cover the whole map. Now, when an object is placed, the navmesh will update instantly, because it no longer needs to parse through every object on the map (potentially thousands!!!). It only needs to parse through the objects that exist in the mini navmesh that the object was placed in (probably only 1-5 objects now!). Performance XP Boost +100! Charles Horwood You might appreciate this one :) #Rts #RTSGame #IndieGame

Smitty | Steel Command

60,072 次观看 • 6 个月前

Here are the steps to create this animation: 1. Design and model your own mech in Plasticity. 2. Import the model into the animation software 3ds Max. 3. Create the animation on your own. 4. Render and export the monochrome model animation. 5. Make the final render for reference. 6. Use Seedance 2 to re-render your monochrome model animation according to the reference render you provided. Summary: In this workflow, Seedance 2 acts as a renderer that saves you hours of rendering time. All other work has to be done manually by yourself. Don’t blindly believe that AI can fully produce mech animations for you — it simply cannot do that.

Here are the steps to create this animation: 1. Design and model your own mech in Plasticity. 2. Import the model into the animation software 3ds Max. 3. Create the animation on your own. 4. Render and export the monochrome model animation. 5. Make the final render for reference. 6. Use Seedance 2 to re-render your monochrome model animation according to the reference render you provided. Summary: In this workflow, Seedance 2 acts as a renderer that saves you hours of rendering time. All other work has to be done manually by yourself. Don’t blindly believe that AI can fully produce mech animations for you — it simply cannot do that.

LAS91214

46,446 次观看 • 1 个月前

Create a short film like this in just 1 minute with GPT Image 2.0 + Seedance 2.0. GPT Image 2.0 can naturally combine multiple photos into one single image, while Seedance 2.0 can use that image as a reference to automatically separate the scenes, generate a coherent video sequence, and add suitable background music. This workflow greatly improves the overall creative efficiency. When using this method, simply provide the merged image as a reference for Seedance 2.0 and briefly describe each scene with a simple prompt. This can significantly increase the success rate of the final video. All of the above was created on GPT Image Prompt: Seedance Prompt:

Create a short film like this in just 1 minute with GPT Image 2.0 + Seedance 2.0. GPT Image 2.0 can naturally combine multiple photos into one single image, while Seedance 2.0 can use that image as a reference to automatically separate the scenes, generate a coherent video sequence, and add suitable background music. This workflow greatly improves the overall creative efficiency. When using this method, simply provide the merged image as a reference for Seedance 2.0 and briefly describe each scene with a simple prompt. This can significantly increase the success rate of the final video. All of the above was created on GPT Image Prompt: Seedance Prompt:

Midjourney Sref and prompt Library

40,572 次观看 • 2 个月前

Utilizing a massive library of high quality, free UGC avatars for your three.js project is way less difficult than you think. No animation baking required, can swap out any Mixamo animations in real time. Step 1. Download an avatar on VRoid Hub Step 2. Download a skinless Mixamo animation from Step 3. Paste this into claude code / codex / cursor chat Now you have avatars, animations and a function to apply animations to your avatars whenever you desire. Source code from the video is here:

saori

14,962 次观看 • 4 个月前

BREAKING: Chinese cybersecurity company just found a way around one of AI's most expensive problems. Meta built an AI model called SAM that could outline any object in a photo. The catch: it needed millions of hand-labeled images to learn how. 360 AI Research's new ECCV 2026 paper, MoSA teaches AI to recognize objects by watching video move. No human labels. 10,000 hours of unlabeled video. 21 million+ self-generated labels. Zero humans in the loop. Learn more 🧵👇

BREAKING: Chinese cybersecurity company just found a way around one of AI's most expensive problems. Meta built an AI model called SAM that could outline any object in a photo. The catch: it needed millions of hand-labeled images to learn how. 360 AI Research's new ECCV 2026 paper, MoSA teaches AI to recognize objects by watching video move. No human labels. 10,000 hours of unlabeled video. 21 million+ self-generated labels. Zero humans in the loop. Learn more 🧵👇

Shruti

112,897 次观看 • 12 天前

wan 2.2 makes lighting feel programmable you start with one normal clip drop a single reference frame and the model rebuilds the scene same framing, same motion, same timing but now you can swap mood change light direction and create cinematic variants without reshooting brands are using this to turn one talking-head ad into multiple high-end looks from the same source video i broke down the exact workflow rt + comment “wan” and i’ll send it (follow for dm)

wan 2.2 makes lighting feel programmable you start with one normal clip drop a single reference frame and the model rebuilds the scene same framing, same motion, same timing but now you can swap mood change light direction and create cinematic variants without reshooting brands are using this to turn one talking-head ad into multiple high-end looks from the same source video i broke down the exact workflow rt + comment “wan” and i’ll send it (follow for dm)

Daffy

24,979 次观看 • 7 个月前

Claude Code + ChatGPT Images 2.0 is f*cking cracked 🤯 I rebuilt my static ad system inside Claude Code on the new ChatGPT Images 2.0 model. One brand name + one URL = 40 production-ready static ads. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva. If you're finding winning ad concepts on Meta and manually recreating them one at a time — copying prompts, pasting product details, tweaking aspect ratios, downloading, organizing... This system eliminates the entire loop: → Give Claude a brand name and URL → It researches the brand's fonts, colors, packaging, and photography style → Builds a Brand DNA document from scratch → Fills in 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details → Fires every prompt to ChatGPT Images 2.0 with your product photos as reference → Downloads finished ads into organized folders with an HTML gallery No manual prompt filling. No Canva templates. No copy-pasting between tools. What you get: → 40 ad formats filled with your exact brand colors, fonts, and copy → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Product photos passed as reference so the model matches your real packaging → A reusable system — new brand, new folder, same pipeline Built 100% in Claude Code with ChatGPT Images 2.0. I put together a DIY playbook showing the exact architecture so you can build this yourself in Claude Code. Want it for free? > Like this post > Comment "CHAT" And I'll send it over (must be following so I can DM)

Mike Futia

188,829 次观看 • 3 个月前

Claude Code + ChatGPT Images 2.0 is f*cking cracked 🤯 I rebuilt my static ad system inside Claude Code on the new ChatGPT Images 2.0 model. One brand name + one URL = 40 production-ready static ads. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva. If you're finding winning ad concepts on Meta and manually recreating them one at a time — copying prompts, pasting product details, tweaking aspect ratios, downloading, organizing... This system eliminates the entire loop: → Give Claude a brand name and URL → It researches the brand's fonts, colors, packaging, and photography style → Builds a Brand DNA document from scratch → Fills in 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details → Fires every prompt to ChatGPT Images 2.0 with your product photos as reference → Downloads finished ads into organized folders with an HTML gallery No manual prompt filling. No Canva templates. No copy-pasting between tools. What you get: → 40 ad formats filled with your exact brand colors, fonts, and copy → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Product photos passed as reference so the model matches your real packaging → A reusable system — new brand, new folder, same pipeline Built 100% in Claude Code with ChatGPT Images 2.0. I put together a DIY playbook showing the exact architecture so you can build this yourself in Claude Code. Want it for free? > Like this post > Comment "CHAT" And I'll send it over (must be following @learnwithella so I can DM)

Ismail Khan

19,740 次观看 • 3 个月前

Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering discuss: The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently "understand" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.

Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering discuss: The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently "understand" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.

AK

19,101 次观看 • 1 年前

At first glance, there is no image only separated fragments painted across a series of vertical glass panels Viewed from the side, the composition appears abstract and disconnected. But from one precise position, the fragments align and suddenly resolve into a single floating cloud suspended inside the space Created by Leandro Erlich, the installation transforms perspective into the medium itself. The cloud is not physically painted as one object instead, it emerges through the exact arrangement of distance, alignment, and viewpoint

At first glance, there is no image only separated fragments painted across a series of vertical glass panels Viewed from the side, the composition appears abstract and disconnected. But from one precise position, the fragments align and suddenly resolve into a single floating cloud suspended inside the space Created by Leandro Erlich, the installation transforms perspective into the medium itself. The cloud is not physically painted as one object instead, it emerges through the exact arrangement of distance, alignment, and viewpoint

Amazing Things

14,988 次观看 • 1 个月前

i built an open world minigame that's controlled with hand movements only here's a step-by-step tutorial: > started with my mediapipe + threejs template (see QT) > added a 3D model made by quaternius > sent a few prompts to gemini 3... --- prompt #1: repurpose the attached script, but now I want to import a gltf model (assets/model.gltf) and use that in the scene instead of the cube when the user moves their hand, a waypoint indicator should move around the scene, and the 3D model should smoothly move there when the user makes a fist, the model should jump the 3D model contains bundled animations in it. use the "idle", "run", and "jump" animations --- prompt #2: make this a procedural open world adventure. generate very simple procedural voxel terrain when the model gets near the edges of the screen, the camera should move, allowing the model to keep going in that direction --- prompt #3: add some floating glowing gems around the map that the user can collect --- i made some manual tweaks for styling and game feel, but that's the gist of it thanks for reading if you got all the way here full code is available at my link in bio

i built an open world minigame that's controlled with hand movements only here's a step-by-step tutorial: > started with my mediapipe + threejs template (see QT) > added a 3D model made by quaternius > sent a few prompts to gemini 3... --- prompt #1: repurpose the attached script, but now I want to import a gltf model (assets/model.gltf) and use that in the scene instead of the cube when the user moves their hand, a waypoint indicator should move around the scene, and the 3D model should smoothly move there when the user makes a fist, the model should jump the 3D model contains bundled animations in it. use the "idle", "run", and "jump" animations --- prompt #2: make this a procedural open world adventure. generate very simple procedural voxel terrain when the model gets near the edges of the screen, the camera should move, allowing the model to keep going in that direction --- prompt #3: add some floating glowing gems around the map that the user can collect --- i made some manual tweaks for styling and game feel, but that's the gist of it thanks for reading if you got all the way here full code is available at my link in bio

AA

131,799 次观看 • 7 个月前