Stringers
GitHub open-source project · written 11-6
Supra-3D/SUPRA-Tex · Supra-3D

Huawei's "photos become 3D, small text stays sharp" earned 650 stars, and someone rented a card and ran it 10 times, succeeding only 2: this gap is what to follow

What it is A model open-sourced by Huawei and others: give it a photo of an object, and it generates a 3D model you can drop straight into a game engine, with the text and patterns still legible.

Why it caught on A big-company byline, with paper, weights, and demo all released within a week. It also landed right after Meshy had made "texture alignment" a talking point, and a comparison image using Meshy 7 got passed around by paper-sharing accounts and AI news sites.

BHow you could follow it Don't build another model. Package it with Pixal3D as a one-click Windows bundle, a ComfyUI node, or a Blender add-on, fix the failure causes listed in the Kaggle test one by one, and ship within two weeks.

Grade B: the model itself got its visibility from the big-company byline, so you can't ride that. The packaging gap is real (tested: 10 success in 2 runs, no Windows version, demo is down), but people are already building similar packagings, and if the official side patches it, the gap disappears. The ceiling is probably a few hundred to a thousand-plus stars. It's spreading in a small circle
Data (updated daily) · Repo created 2026-10-14 · 699 stars · 7 days +223
GitHub · stars
025050075010-1710-2310-2811-0211-08699
Data 2026-10-17 → 2026-11-08 (23 days, 23 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

SUPRA-Tex is a single-image-to-3D model open-sourced by Huawei with Shanghai Jiao Tong and Huazhong University of Science and Technology. It reached 650 stars in 23 days and has kept climbing steadily. It relies on things you mostly can't get: a big-company byline, a paper, and a comparison image against Meshy 7. But the part it did poorly is one you can pick up: it won't install, there's no Windows version, and the online demo is down. Below: what it is, where the stars came from, and which part you can follow.

One person spent $4.34 renting 7 kinds of GPUs

In Oct 2026, at 3:51 a.m. 27, a developer named Lucas Rangel started installing SUPRA-Tex on a rented RTX 3090. He stopped halfway because a compiler toolchain was missing. Over the next two days he switched to 4090, 5090, L40S, RTX PRO 6000 and an ARM-based GB10, 10 attempts in all. He published "Ten attempts to run an image-to-3D tool" on Kaggle in Oct 28. The result: 10 attempts succeeded 2 time(s), both on RTX 4090. 5 failed during install, 2 failed at runtime, 1 stalled on model download, and 1 were shut down by the budget cap he had set himself. The failures had 5 causes: missing compilers, native extensions that wouldn't build, missing image libraries, x86-only wheels on ARM machines, and slow model downloads. Renting GPUs cost 4.34 dollars in total, 3.57 of it on the machines that errored.

He picked SUPRA-Tex to test as the representative of the "open-source single-image-to-3D" category. That choice says something about where it stands now: the name recognition is there, but few people actually get it running.

What it is

Give it a photo of an object and it outputs a textured 3D model (a GLB file). The texture maps are comprehensive: color, metalness, and roughness are all there, so it still holds up when you change lighting in Blender, Unreal, or three.js. If you already have a mesh, it can also handle just the material.

Its headline selling point is that the text and patterns in the photo are still clear in 3D. Most photo-to-3D tools get the shape roughly right, but the surface turns to mush and label text blurs together. A themenonlab.blog post from Oct 21 describes a comparison image that's been circulating: a model airplane with "Supra-Tex" printed on the fuselage. Meshy 7 generated text you can't read, while SUPRA-Tex generated clear text that stays on the fuselage.

It works in three steps: first estimate the material of the visible side of the photo, then project the visible pixels directly onto the geometric surface, and finally use the model to fill in the back and sides. The geometry comes from Tencent's open-source Pixal3D. The code is under the MIT license, but the material adapter uses Qwen's non-commercial research license. The authors say a 24GB GPU is enough to run it.

How the stars grew

Date (GitHub stars)What happened
10-13Registered a Supra-3D account specifically for this project
10-15 / 10-16Posted to arXiv, then appeared on Hugging Face Daily Papers the next day (13 upvotes); gamedev.net, AGI Hunt ("Huawei and others propose SUPRA-Tex"), and Zhichai.net followed the same day
10-17,35 starsCovered by aiweekly; 10-18 released a visual browsing page
10-19 → 10-21,41 → 132 stars10-19 released the inference code, 10-20 got a newsletter recommendation, 10-21 themenonlab published a post; on 10-21 it gained +84 stars in one day, the biggest single-day jump so far
10-22 → 11-02,196 → 500 starsShared by @CSVisionPapers, Russian site DTF and Instagram reposter accounts picked it up, and there was a Kaggle test writeup; steadily gaining 20–50 stars per day
11-03,571 stars+71 that day; at the same time it was listed on GETAI.APP and an X post with an RTX 5090 test went up (about 1000 views), but the direct cause of such a large jump is unverified
11-06,650 stars+7 in the last 225 days and +7 in the first 199 days, still climbing; 34 forks

This curve shows no Hacker News or Reddit hot threads, and no big X accounts resharing it. Its spread stayed inside a small circle: paper-sharing accounts, Chinese and English AI news sites, a few small blogs, and reposter accounts. Its readers are mainly researchers and game developers working on 3D generation.

Why this one

First, the byline and completeness. The byline is Huawei plus Qi Tian's group. The paper, weights, online demo, visual browsing page, and bilingual Chinese-English README were all released within a week, and in three stages (paper → browsing page → code), so each release brought another wave of people in. Most paper repos only post a "coming soon," and on this point it outperformed them.

Second, it rode the topic at the right moment. In Oct 10, commercial leader Meshy had just published a blog post turning "texture alignment" into a public benchmark, used to promote Meshy 7. Within 5 days SUPRA-Tex was pushing the same selling point, and the rival in its comparison image was Meshy 7.

Third, the image is easy to share. "One photo becomes 3D, even the small text is kept" is exactly the kind of headline reposter accounts love, and the comparison image is understandable at a glance.

So it got hundreds of stars, not dozens, and mainly because of the first point. You can't copy that one.

Where it falls short

  • It won't install. The Kaggle test: 10 attempts, 2 success. As early as Oct 20, someone on GitHub asked "I want to use this for windows?" and there is still no Windows version.
  • The demo is down. The online demo on Hugging Face was returning runtime errors as of Nov 6.
  • Some people reject the quality. In Oct 23 an issue titled "Poor 3D quality" was filed. The claim that it "matches or even beats commercial systems" comes from the paper itself, and no one has independently reproduced it.
  • Only half of it is open. Someone asked whether the weights for another material backend would be released (issue #3), and someone asked whether the texture training code would be released (issue #5). Neither has an answer yet.
  • The code hasn't moved. Last push was Oct 29. The same team has already put out their next paper under the same account, PhysAg3D (repo created Oct 26, 165 stars), using the same playbook.

Who's competing

Open source is crowded here. Microsoft's TRELLIS and TRELLIS.2 each have over 10k stars (14.2k / 12.7k), and Tencent's Hunyuan3D-2 has 15.3k. SUPRA-Tex is an order of magnitude smaller. Its only edge is front-face detail fidelity. On the commercial side there's Meshy, Tripo, Rodin, and Hunyuan 3D Studio.

There's one more category that's more relevant to you: wrapper tools. Among 3D generation repos created in the last ~4 months, the ones with more stars than SUPRA-Tex aren't models, they're tools. Examples: AssetFurnace (~1.1k stars), img2threejs, and Scenario's Blender plugin. In other words, turning a model into something easy to use can pull more stars than the model itself.

If it were you

Bottom line: B. Building another research model on top of it makes no sense. You don't have Huawei's authorship or their paper. What's worth doing is the part they didn't finish: pair SUPRA-Tex with Pixal3D (ideally TRELLIS.2 too) and package it as a tool that installs cleanly and runs with one click on Windows. The gap is real, but someone else will go after it, and the ceiling is probably a few hundred to a bit over a thousand stars.

Who it's for: People comfortable with Python packaging, CUDA environments, and ComfyUI or Blender plugin development, with a 24GB GPU on hand and ideally a Windows machine.

Who it's not for: Anyone who wants to make money from it (the material adapter is non-commercial licensed, so charging is a dead end), or anyone who only wants to tweak the model and won't spend time on installation errors. The value here is entirely "others can't install it, you get it running for them."

How to build it: Pick one of three, not all. (1) A Windows one-click package with pre-compiled extensions, resumable model downloads, and automatic fallback when VRAM is short. (2) A ComfyUI node that plugs into existing image workflows. (3) A Blender add-on: pick an image inside Blender and the generated model goes straight into the scene. The README's first screen should show three things: the comparison image where the text stays legible, a 30-second screen recording from install to first model, and a line saying "tested GPU list." The Kaggle hands-on post lists 5 failure causes clearly. Fix them one by one and that becomes your changelog.

How to get users: Reply to issue #2 on SUPRA-Tex (the Windows one) with your repo link. Ask the author of the Kaggle hands-on post to re-test it. They'd be your best first users. On X, tag the person who posted the RTX 5090 test. Write a "get SUPRA-Tex running on Windows in 10 minutes" post in both Chinese and English, and submit it to AI news sites and communities like r/StableDiffusion and r/blender. A single line linking to your project in the original README is worth more than anything else, so open an issue asking for it.

When to move: Within two weeks, with a first working version out before Nov 20. Its heat is still rising now. If the official project adds Windows support itself or fixes the demo, half your reason is gone.

What would mean you misread it: (1) The official repo adds Windows support or a one-click install before end of November. (2) SUPRA-Tex's daily star gains drop below 5, and PhysAg3D pulls attention away (if nobody cares about it anymore, nobody wants a wrapper either). (3) Two or more similar wrappers show up in the ComfyUI or Blender communities within 2 weeks. (4) Your repo is up for 2 weeks and has fewer than 50 stars, which means this crowd is bookmarking papers and not planning to actually run them.

GitHub repo ↗

Research notes: competitor table, acquisition evidence, full research report
Competitors
NameWhereLaunchedScaleHow it differs from this one
Meshy 7Web2026 (texture-alignment benchmark, published 10-10)Leading commercial 3D generation SaaSClosed and paid; SUPRA-Tex is open source and goes head-to-head using the same selling point (detail alignment) in its comparison images
Tripo / Rodin / Hunyuan3D StudioWeb2024–2026Commercial productsClosed end-to-end; SUPRA-Tex is a research model you can self-host
microsoft/TRELLIS, TRELLIS.2Open source2024-12 / 2025-1114.2k / 12.7k starsThe general single-image-to-3D benchmark; SUPRA-Tex focuses on front-face detail fidelity and full PBR
Tencent-Hunyuan/Hunyuan3D-2 / 2.1Open source2025-01 / 2025-0615.3k / 4.4k starsTexturing via multi-view fusion; SUPRA-Tex uses surface-aligned observations plus 3D inpainting to avoid fusion artifacts
TencentARC/Pixal3DOpen source2026-052.6k starsA geometry generation model. SUPRA-Tex uses its geometry, so they're upstream and downstream, not rivals
MaterialAnything / TEXGenOpen source2024-11369 / 341 starsEarly PBR material / texture generation, smaller in scale and completeness than SUPRA-Tex
Where users come from
  • Verified arXiv + HF Daily Papers + paper-sharing accounts: posted to arXiv on 10-15, entered HF Daily Papers on 10-16 (13 upvotes); reposted by gamedev.net 10-16 and @CSVisionPapers 10-22
  • Verified Chinese AI news sites: AGI Hunt 10-16 "Huawei and others propose SUPRA-Tex", Zhichai Network paper reading, ZICQ, AICoder, N884, GETAI.APP 11-03
  • Verified English newsletters / blogs / reposting accounts: aiweekly 10-17, "AI TOOLS WORTH YOUR TIME" 10-20, themenonlab 10-21, DTF 10-25, Instagram githubsignals reel
  • Verified Staged release milestones: 10-16 paper → 10-18 Studio → 10-19 code; 10-21 with +84 stars in a single day
  • Verified User hands-on content: Kaggle "Ten attempts" 10-28; X @niklaslenz_ai posted an RTX 5090 test (1k views)
  • Verified Riding a competitor's topic: Meshy 10-10 published a texture-alignment benchmark; SUPRA-Tex's comparison images name Meshy 7 (themenonlab 10-21)

SUPRA-Tex (Supra-3D/SUPRA-Tex) research report

Channel: GitHub · Research date: 2026-11-06 · Data card: cards/gh_Supra-3D__SUPRA-Tex.json

One line

An open-source research project from Huawei (Jiemin Fang, Qi Tian's group, with Shanghai Jiao Tong and Huazhong University of Science and Technology) for single-image 3D asset generation and texturing existing meshes. The main pitch is "super-aligned texturing": text, patterns, and material boundaries in the input image survive onto the 3D model, and the output is a relightable GLB with base color, metallic, and roughness. It gained traction through a big-lab paper, same-day code and weight release, and automatic amplification in paper feeds, timed right as Meshy turned "texture alignment" into a public benchmark in Oct 10. It's a research repo, not a business: no revenue, and some of the weights carry non-commercial licenses.

1. Data (source: GitHub repo_profile / repo_day, GitHub API, 2026-11-06)

  • Repo created 2026-10-14; the account Supra-3D was registered 2026-10-13 specifically for this project, and it has 5 repos to date.
  • Stars: 10-17 was 35 → 10-20 was 48 → 10-21 was 132 (+84 in one day, the largest gain in the observation window) → 10-22 was 196 → 10-28 was 378 → 11-02 was 500 → 11-03 was 571 (+71) → 11-06 was 650 (live GitHub API: 676). Forks: 34.
  • +225 over the last 7 days, +199 over the prior 7 days. Steady growth, no spike followed by a drop. It hasn't hit trending: the repo is under 60 days old, so not making the list is expected (per the github repo SCHEMA notes).
  • Hugging Face: paper page with 13 upvotes (10-16 on Daily Papers); model supra3d/supra-tex with 14 likes; Demo Space UniLat3D/Supra-Tex with 12 likes. Status today: RUNTIME_ERROR (HF API, 2026-11-06).
  • PhysAg3D (created 10-26, 165 stars) and Gate3DGen (20 stars) under the same author account are follow-up papers from the same group, using the same playbook.
  • Compared with 3D generation / texturing repos created in the last 4 months, SUPRA-Tex has the most stars among pure research model repos (per GitHub repo search; the earlier img2threejs, AssetFurnace, and Scenario Blender plugin are tools or wrappers, not models).

II. Competitors

NameWhereWhenScaleHow SUPRA-Tex differs
Meshy 7 / 7.1Commercial web / API2026 (MeshyBench blog 10-10)Leading commercial 3D generation SaaSClosed and paid. 10-10 published its own texture-alignment benchmark claiming a 66.2% lead. SUPRA-Tex's head-to-head comparison is against Meshy 7 (comparison images with "Supra-Tex" text on them)
Tripo / Rodin(Hyper3D)/ Hunyuan3D StudioCommercial web2024–2026Commercial productsClosed end-to-end. The SUPRA-Tex paper says it "matches or even surpasses leading commercial systems" but doesn't name them in the abstract (aiweekly 10-17)
microsoft/TRELLIS, TRELLIS.2Open source2024-12 / 2025-1114.2k / 12.7k starsThe open-source single-image-to-3D benchmark, generating geometry and texture together. SUPRA-Tex focuses on fidelity of the visible face and full PBR
Tencent-Hunyuan/Hunyuan3D-2 / 2.1Open source2025-01 / 2025-0615.3k / 4.4k starsHas a Paint texture module using multi-view fusion. SUPRA-Tex's paper criticizes exactly this approach for its fusion artifacts
TencentARC/Pixal3DOpen source2026-052.6k starsSUPRA-Tex uses Pixal3D for its geometry (per niklaslenz's X hands-on post). The two are upstream and downstream, not rivals
3DTopia/MaterialAnything, CVMI-Lab/TEXGenOpen source2024-11369 / 341 starsEarly open-source PBR material / texture generation, small in scale
stepfun-ai/Step1X-3D, VAST TripoSGOpen source2025901 / 1.9k starsOpen-source end-to-end 3D generation
UniLat3D/UniLat3DOpen source2025-09149 starsPrevious-generation work from the same group; the Demo Space is still under UniLat3D

The category is crowded: the leading open-source repos have over 10k stars, and commercial players include Meshy and Tripo. SUPRA-Tex's star count is an order of magnitude below the top.

III. Growth levers (where users come from)

  1. arXiv + HF Daily Papers + paper reposts (confirmed): 10-15 on arXiv (2610.22352), 10-16 in HF Daily Papers. gamedev.net 10-16 news is auto-generated from arXiv cs.GR; @CSVisionPapers 10-22 reposts it; alphaxiv, paperium, deeplearn.org and Semantic Scholar also index it.
  2. Chinese AI news sites rolled out (confirmed): AGI Hunt (10-16 "Huawei and others propose SUPRA-Tex"), Zhichai Network paper reading (10-16), ZICQ (10-15), AICoder (10-18), N884 (10-18), GETAI.APP (11-03).
  3. English aggregators / blogs / short-video accounts (confirmed): aiweekly.co (10-17), the "AI TOOLS WORTH YOUR TIME — Oct 20" newsletter, themenonlab.blog (10-21, headline names Huawei and notes the comparison image against Meshy 7 "is circulating"), the Russian DTF (10-25), the githubsignals reel on Instagram / Facebook repost accounts (date not verified), and daily.dev.
  4. Code release timing (confirmed): 10-19 inference code went up; 10-21 saw +84 stars in one day, right after the code release and the newsletter / blog coverage. The +71 stars on 11-03 line up with GETAI.APP (11-03) and X user @niklaslenz_ai's RTX 5090 hands-on post (about 11-04,1k views, 22 likes), but these are all small-scale, so the direct cause of the 11-03 surge is unverified.
  5. Community testing / secondary content (confirmed): A Kaggle post, "Ten attempts to run an image-to-3D tool" (10-28), used SUPRA-Tex as the representative of this kind of tool. The author ran it 10 times and got 2 successes. GitHub issues have users asking about Windows support, commercial weights, and training code, and someone reported "Poor 3D quality".
  6. Not found: paid promotion, HN / Reddit hot posts, big X account reposts (the x-radar local corpus searched for SUPRA-Tex in the last 30 days and returned 0 posts).

4. Why it broke out

  • Backing and completeness: Huawei added the name Qi Tian, and within one week the project page, paper, HF weights, online demo, Studio gallery, and a bilingual Chinese-English README were all released (README Release Notes: 10-16 paper, 10-18 Studio, 10-19 code). Most paper releases only ship a "coming soon", so this full set is rare for a research release.
  • Hit the pain point and the topic at the right time: Meshy 10-10 had just promoted "texture alignment / detail fidelity" as a public benchmark to market Meshy 7. 5 days later SUPRA-Tex used the same selling point (clear text and patterns) and even used Meshy 7 in its comparison images, effectively riding on a topic a commercial leader had just heated up.
  • Shareable content: "One photo turned into 3D, even the small text kept" is a headline repost accounts love (Instagram reels and themenonlab titles were written this way), and the comparison images are understandable at a glance.
  • Moderate barrier, you can run it yourself: It runs on a 3090 with 24GB, there is a local Gradio app, and the code is MIT, so people are willing to star it for later and write up test results.

5. Differentiation

  • On method: it splits "reference-view PBR material estimation (using an MLLM image editing model) → aligning to the surface to get 3D observations → an Interleaved Flow Transformer that completes the unseen sides" into three steps. Visible parts use the pixels in the image directly instead of guessing from implicit features. There is also a dual-view mode: first generate the back view, then fuse the front and back.
  • Against competitors: leading open-source models (TRELLIS, Hunyuan3D) focus on overall quality and geometry, while SUPRA-Tex focuses on faithful front-side detail and complete, relightable PBR, and it benchmarks against the closed-source Meshy 7.
  • Is that why it took off? Partly. The "keeps text" selling point gave it a memorable comparison image in a flood of paper reposts. But what decided that it got hundreds of stars rather than dozens was big-company backing and fast release of code and weights. The differentiation itself isn't solid either: geometry relies on Pixal3D, materials on a Qwen-Image adapter (non-commercial license), the HF demo is currently up, users report poor quality, and Kaggle tests show a high install failure rate.

6. Visibility and copycats

  • Revenue: none, and nothing is sold. Media: only AI news aggregators, paper-repost accounts, small blogs, and repost accounts have covered it; no mainstream tech media, no big X accounts. It falls under "spreading within a small circle" (3D generation / AIGC research and game dev circles).
  • Copycats: I haven't seen any repo directly copying it. PhysAg3D and Gate3DGen from the same group are reusing the same playbook.

7. What's worth borrowing

  • When the research team open-sourced it, they released the "paper + weights + online demo + visual Studio + bilingual README" all at once, and the release steps (paper → Studio → code) spread over several days, each one pulling in another wave of attention.
  • Watch the metric a commercial leader has just heated up (here, Meshy's texture alignment benchmark), use the same metric and compare against their product in your own images, and ride the topic.
  • For people building products: SUPRA-Tex itself is a non-commercial research artifact, so the real opportunity is to wrap models like Pixal3D and TRELLIS.2 into one-click tools / hosted services / Blender plugins. Hard installation (10 successes out of 2 in Kaggle tests), the demo being down, and no Windows version are all gaps; a similar wrapper, AssetFurnace (1.1k stars), already exists.

VIII. Unknowns

  • The direct source of the 11-03 wave of +71 stars was not verified; the Instagram reel's posting date and view count were not verified.
  • I didn't check how much it was shared on Chinese communities (Xiaohongshu, Bilibili, Zhihu); whether it was promoted inside Huawei was not checked.
  • Stars do not equal usage: the HF model page downloads show 0 (this number may not have refreshed yet), the demo is down, and the actual users may be few.
  • "Surpasses commercial systems" is the paper's own claim, with no third-party reproduction.

Sources

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.