One photo in, Three.js code out: how img2threejs got 11 stars in 5,000 days

img2threejs is an agent skill for Claude Code, Codex, and OpenCode. Give it a photo of an object and it doesn't produce a 3D mesh file. Instead it writes a Three.js TypeScript program that assembles the object from primitive shapes, piece by piece, editable, animatable, and git-friendly. When the author posted the first thread he had only 2,935 followers. 11 days later the repo had about 5,000 stars, and by Nov it was 6 with 20,341 stars. The idea already has an owner, but the approach works for people without a big following, and you can still build around a different "image → editable code" target.

A post with 300k views

On 2026 Jul 18, Nick (Nguyen Hoai Nho, @NickDevFE on X), a Vietnamese front-end developer, posted a thread. The body was two sentences:

"img2threejs: turn a photo of an object into procedural Three.js code, nothing but code. An open-source tool that rebuilds the object in a single reference image using procedural Three.js code (no mesh downloads), with quality controlled by a loop that compares the render against the reference. Strongest on hard-surface objects."

The post showed the original image next to a rotating 3D model. It got 2,592 likes, 212 reposts, 3,332 bookmarks, and 305k views (public fxtwitter API, 11-06). At that point he had 2,935 followers, so views were roughly a hundred times his follower count.

The repo had been created three days earlier (Jul 15), and he also posted on r/threejs at the same time. Here is how the stars grew (GitHub daily stars; the July and September points are sparse):

The first 11 days averaged about 450 stars per day. From late July to early September it was about 270 per day, and after that it held steady at 70 to 110 per day. The last week added 553, and the biggest single day was +87 on Nov 4, right around the v2.0.1 release. The fork-to-star ratio is about 8.4%, high for a skill repo, and installing it means cloning it, which suggests many people actually used it. No sign of star farming.

What it actually does

Getting a 3D model from a single image usually means one of two routes: paid sites like Meshy, Tripo, Hyper3D, or Rodin, or open-source models like Tencent Hunyuan3D or Microsoft TRELLIS (Hunyuan3D-2 has 15,335 stars). Both return a GLB mesh, a pile of triangles with a texture. It's hard to spin the wheels on their own or change the blade color.

img2threejs returns a THREE.Group factory function: the frame is a few cylinders, the wheels are tori, the surface texture is a procedural shader, and the pivots, attachment points, and collision bodies are all labeled. The pipeline has several stages, and each stage renders an image and compares it against the reference before moving on. If it can't reach the required accuracy, it says directly that the image can't be done. The whole tool uses only the Python standard library, needs no GPU, and trains no model. The output relies on the host agent's own image-understanding and code-writing ability.

People kept pointing this out. On 25 Jul, Addy Osmani, engineering lead at Google Chrome, posted on LinkedIn (1,960 reactions). He said it "generates TypeScript... code you can diff, review, and version," and praised it as "refreshingly honest." Meng To (co-founder of Design+Code, 190k followers) put it more bluntly on 20 Jul:

"AI image generation is strong, but it still struggles with editable vector and 3D. That's why a skill like this is useful."

The author admits its weak spots. Japanese developer Maoku said after trying it that the character looked "a bit like balloon art," and Nick replied under it: "Characters are still img2threejs's weakest area. Faces, clothing folds, and organic shapes are far harder than hard-surface objects." On 23 Jul, @givros ran the same Canyon road bike photo through both img2threejs and Blender MCP (402 likes, 32k views). The conclusion: Blender's "details and fidelity were clearly better," while img2threejs won on speed and produced code that runs directly on a web page.

Where the stars came from

First, the author's own post, plus big accounts amplifying it within a week. Meng To reposted it on 20 Jul (823 likes, 96k views). swyx reposted it the same day with a single word, "useful." On 21 Jul, Gary Simon (DesignCourse, 49k followers), a design tutorial creator, posted a hands-on test that got 2,858 likes and 144k views, more than the original post. He also released a YouTube video, "From Pictures of Objects to 3D Three.js in MINUTES," with 26k views. The same day, 0xMarioNawfal reposted it for 38k views. Around 25 Jul it reached the Japanese community: npaka (Eiichi Ruegawa) posted "img2threejs x Opus 5 trial in progress," and a post from an account called "Codex Research Lab" got 56k views. Then came Addy Osmani's LinkedIn post. Over the week of 20 to 25 Jul, it went from a post by a 3,000-follower account to something developer circles were all talking about.

Second, multilingual reposts on TikTok and Reddit. Searching "img2threejs" on TikTok turns up a string of re-uploads. @github.signals posted one on 19 Jul with 37k views. The Vietnamese-language @nguyendangtuan99 has 51k, @whitewhoadie has 30k, and there are accounts in Thai, Portuguese, and Arabic. New videos were still appearing in late October. There are also bait accounts with comments like "MODEL for the install tutorial." On Reddit, r/threejs, r/aigamedev, r/StableDiffusion, and r/generativeAI all have posts, one titled "GPT 5.6 Sol x Img2threejs v1.4."

Third, every release came with a showcase topic that restarted the cycle. July 21 v1.0. July 26 v1.4 focused on CS2 weapon skins. August 6 v1.5 handled characters. August 23 1.5.1 switched to "generate a GLB with Tripo first as reference, then rebuild it as code" (Tripo's official account reposted: "Happy to see Tripo in the pipeline"). September 3 1.5.2 added effects. September 6 2.0 became a plugin. November 5 2.0.1. The online gallery at img2threejs.io features subjects that already draw traffic: CS2 knife skins, League of Legends' Lee Sin, Groot, and starships.

Fourth, the long tail came from directories and lists. After September it gained 70 to 80 stars a day, mostly from skill directories (skillsllm, skillagent, claudeers), daily.dev, Medium tutorials, and Showcase threads on the three.js forum. It also came from an October article, "24 open-source vibe-coding game libraries that work like cheat codes" (25k views), which was 17k views. The README's first screen shows Trendshift daily and weekly badges, a line saying "if it's useful, a star helps others find it," plus Ko-fi and Discord links. After it took off, MiniMax, Atlas Cloud, Tripo, and Hyper3D sponsored it.

No Hacker News thread was found, and there are no signs of paid ads.

Why this one, and why now

Timing accounts for much of it. In 2026 July, the agent skill distribution model was just getting started. Claude Code skills and Codex plugins were new. The Opus 5 and GPT-5.6 generation of models had also caught up on reading images and writing code, and those model names kept appearing in trial posts. Writing three.js from an image wasn't new: in 2025 November someone had already shown "Gemini 3 writing Three.js from an image," but results were hit or miss. img2threejs turned it into a structured pipeline, and the output was the first to count as genuinely usable.

Then there's the one-line contrast: others output meshes, it outputs code. Next to Meshy and Tripo, big accounts could repost it without much explanation. The demo also travels well. A source image beside a rotating model makes sense in 15 seconds.

An idea alone isn't enough, and there's a ready comparison. vinhhien112/img2obj created its repo 6 days earlier with nearly the same approach and the same terminology. It only supports Codex, and its README and demo are weaker. It has 1,686 stars, about a tenth of this project's. I haven't confirmed how the two are related. What created the tenfold gap was the author's own post, multi-host support, the gallery subjects, and a new release every week or two.

Who is already on this path

"Image → 3D" was already a red ocean. The slot "image → editable Three.js code, shipped as an agent skill" was nearly empty before this entered, and now it's taken. The author's GitHub org page has already been renamed "img2x", so they seem to plan to expand into other target formats (inferred).

If it were you

One-line verdict: Don't build another img2threejs. That slot already has a repo with 20k stars, and name-squatting sites have shown up too. Its playbook can be copied: pick a target where "AI can tell at a glance whether it's done well, but what it outputs can't be edited," and make a skill that turns images into editable code. Its author had only 3,000 followers when they started.

Who it suits: Developers with real skill in one kind of "code-based asset": people who do SVG and icons, write Blender Python, work in Godot or Unity, build CSS animations, or do CAD (OpenSCAD, CadQuery). You need to be able to judge yourself whether a rebuild looks right, or you can't set a quality gate.

Who it doesn't suit: People who just want to swap img2threejs's framework or wrap it in a different host. The img2obj idea is the same, and it has a tenth of the traction. Also people without a demo subject that looks impressive at a glance.

How to do it:

1. Pick a target. Ready-made directions: image → editable SVG or icons (Meng To's line "AI still struggles with editable vector" points right at this); image → OpenSCAD / CadQuery parametric parts you can 3D print directly; image → Blender Python scripts; and the weak spots the author admits to: characters and organic shapes.
2. Copy its skeleton: a staged spec, compare each step's render against the original, redo if it fails, and say plainly when it can't do something. Use only the standard library, and don't make people set up a GPU environment.
3. Support Claude Code, Codex, and other hosts from day one.
4. Set up an online gallery first, choosing subjects that bring their own traffic: game items, anime characters, well-known products. Add a new batch of work with each version release.

How to get users: Post on X yourself, with the original on the left and the result on the right. Keep the caption to one line that states the contrast with existing tools ("others output PNG, I output SVG you can edit"). Post the same day to the matching Reddit niche communities. Then go after design and frontend accounts that do hands-on tests. DesignCourse's test post got more engagement than the original. Japanese, Chinese, and Vietnamese AI-tool accounts will also repost on their own. After that, ship a new version every one or two weeks, each with a new subject to push another round, and submit the repo to the various skill directory sites.

When to launch: The novelty of "a new model plus a skill finally does this thing well" will fade within about half a year as the next generation of models arrives (inferred). Get this kind of thing out before early 2027, and aim for the one-to-two weeks after the next model release, when everyone is looking for things to try on the new model.

What signals you've misread it: Two weeks after the demo goes out, no hands-on accounts test it and the stars stay in the triple digits. That means the results aren't striking enough at a glance, or people feel the bare model is good enough. The other way round: if a new model ships and can produce roughly the same results without the skill installed, this kind of skill's value vanishes fast, so stop investing in it.