Stringers
GitHub open-source project · written 11-6
qwdwqfwq/topconf-paper-figure-gallery · qwdwqfwq

A new account with 2 followers filtered 48k top-conference paper Figure 1s into a wall: 50 days, 1,462 stars

What it is A web gallery: it crops the main figure (Figure 1) from the first page of papers at top conferences such as ICLR, NeurIPS and CVPR, hand-curated, filterable by conference, year, acceptance tier and layout, for finding references while writing papers.

Why it took off Every grad student submitting to a top conference gets stuck drawing Figure 1 with nowhere to find references. It launched just ahead of submission season, was featured the same day by PaperWeekly's headline column, and the author then used the "featured by headline" line to pitch HelloGitHub, Ruan Yifeng's weekly and Reddit, and it was reposted by CSDN, Bilibili and a Korean PyTorch community.

BHow you could follow it Stop making figure or poster galleries. Pick another target that people have to make at the same time every year and still can't find references for (defense slides, grant proposal roadmaps, mechanism diagrams in other fields, patent drawings). Hand-curate a small, high-quality static gallery, launch before the deadline, and first earn one feature from a vertical WeChat account before pitching weeklies and communities.

Grade B: the playbook is clear, the cost is very low, and it transfers to many audiences. But its stars came almost entirely from one big account's exposure and launch timing. Two later followers in the same circle got only about twenty stars each. Swapping in a new target would also need another media feature, and the image copyright carries a risk of takedown. Circulating in small circles
Data (updated daily) · Repo created 2026-09-17 · 1,491 stars · 7 days +199
GitHub · stars
7501k1.2k1.5k10-0910-1610-2411-0111-081.5k
Data 2026-10-09 → 2026-11-08 (31 days, 30 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

Top-Conf Figure Gallery is a pure static web page. It crops the Figure 2023 (the main figure on the first page) from papers at ICLR, ICML, NeurIPS, CVPR, ACL and AAAI for years 2026–1, hand-curates 3,516 of them, and lets you filter by conference, year, acceptance tier and layout type. The author qwdwqfwq has only 2 followers on GitHub and this one repo. Created in Sep 2026 17, it reached 6 1,462 stars by Nov (GitHub, 11-06). This piece covers why it took off, why an idea that appeared 3 days later with 20 times as many figures got only 25 stars, and where to start if you want your open source project to get stars like this.

A WeChat headline post

On the afternoon of Sep 2026 21, an article spread on Sohu titled "Can't draw your paper Figure 1? Six top conferences' 2298 main figures open-sourced, this inspiration gallery took off." Its opening two lines: "About 48k candidate papers, finally narrowed to 2298 main figures." "A reference set that used to take dozens of PDFs to assemble can now be browsed in one page."

The source was PaperWeekly, one of the largest WeChat accounts in Chinese AI academia. The author later wrote in a HelloGitHub submission: "Featured by PaperWeekly's headline column on launch day, 9000+ reads, 1800+ shares." (Author's own account; the original wasn't directly checked.) Shares were a fifth of reads, which is high for a WeChat post and suggests it was passed widely into lab and research-group chats. On GitHub it matches: on Sep 21 there were 18 forks, while the days before and after saw only 1 to 6.

By day 4 it had 250 stars; in the first week, over 400.

What it hit

Every grad student submitting to a top conference for the first time gets stuck on the same thing: Figure 1 needs a narrative, has to let reviewers grasp the method at a glance, and there's nowhere to find references. The old way was flipping through PDFs one by one. The author's submission says exactly this: "Knowing you need a clear, narrative Figure 1, it's hard to quickly find a suitable layout, information hierarchy and visual references."

The timing also fit. It launched in mid-September, right in ICLR's submission season (inferred: ICLR 2027 deadline around late September to early October), the few weeks of the year when the most people are drawing Figure 1.

What it offers is concrete: open it and you get a waterfall grid, each figure tagged with conference, year and Oral / Spotlight badges, and clicking jumps to the original paper. You can search "agent", "robot" or "DPO" and pull up main figures from that subfield at once. No sign-up, no backend.

What the author did after the spike

There was only one spike, but the author used it as leverage:

  • Four versions in four days after the spike (9-19 to 9-23, v0.3 to v0.5.0): added 2026 years of conferences, added acceptance-tier badges, and added a figure-generation tool called FigureForge. The figure count went from 2,298 to 2,730, 3,452, 3,516, and each new number became the headline for the next reprint. On Bilibili in Sep 24, someone posted "Research figure lifesaver! 3528 free top-conference figure searches", which got 1,138 views.
  • Pitched other outlets with the "featured by headline" line: 9-26 to HelloGitHub, 10-03 to Ruan Yifeng's tech weekly, with headlines like "Featured by paperweekly headline…"; also posted to Reddit r/SideProject.
  • Overseas people reposted it on their own: the Korean PyTorch user group PyTorchKR 9-23 posted a Korean introduction on LinkedIn; a site selling PaperBanana (AI for scientific figures), 9-22, wrote a piece called "What 2,298 Figure 1s Reveal", analyzing its data and sending traffic its way along the way.
  • Then mirroring: CSDN accounts that bulk-repost GitHub projects wrote two tutorials, and someone mirrored the whole site to their own domain.
  • The README added a "click the star in the top right" GIF, plus a demo GIF and a finished-product wall. For this kind of tool, a star works like a bookmark, since people come back to it while drawing, so the share of visitors who star it is high.

The result is a flat long tail: 10-16 977 → 11-06 1,462, +182 in the last 7 days, +134 in the first 7 days, peak single day +46 (10-20). It never made GitHub Trending, had almost no voice on HN or English X, and ran entirely on the Chinese research community and this chain of reposts.

Who it's competing with

The "AI-drawn scientific figures" route was crowded by the first half of 2026; a "real top-conference main-figure reference library" was essentially empty before it.

ProjectLaunchedScaleHow it differs
dair-ai/ml-visuals2020-0517k starsML figure templates (PPT material), not figures from real papers
dvgodoy/dl-visuals2021-051.6k starHand-drawn architecture diagram assets by the author
Mondrian-He/Awesome-artist2025-11109 starAlso collects good top-conference figures, but it's just an awesome list with no search or filtering
PaperBanana and its clones2026-01 / 027.6k / 2.5k starAI auto-generates academic figures, no reference library
AutoFigure, Paper2Any, academic-figure-generator2025-10 to 2026-032k–4.4k starUpload a paper, generate figures
Various Claude / Codex scientific-figure skillsFrom ¥2026-041k to 50k starsAgent image generation

It sits right on the seam between two tracks. The core product is a human-curated reference library (25 rules strip out line charts, tables and screenshots, then dedupe and manual review, one by one). Image generation is a later add-on: "search similar reference figures first, summarize the layout, then produce a first draft." PaperWeekly and every repost talk about the "main-figure reference library" and none of them talk about generation.

It has 20x more figures. Why didn't it take off?

The clearest evidence is the copycats:

  • PaperFigureLib / paperfigure.net: library built in Sep 20, only 3 days after it, claims 5,672 papers and 70,452 figures, sorted into method diagrams, bar charts and tables. 25 GitHub stars.
  • topconf-poster-gallery: built in Oct 9, 35,884 top-conference posters, name copied from "topconf-…-gallery", and the author had submitted a PR to this repo a few days earlier. 20 stars.

20x the volume, 50x fewer stars. The reason isn't the data. It got there first, it got that headline, and it only collects "designed Figure 1" and marks which ones are Oral. Grad students want to see "what the figures from accepted papers look like," not seventy thousand images.

There's also a risk to note: the figures are cropped from publisher PDFs. The README says CC BY and "taken down within 72 hours," but not every CVPR or AAAI paper is CC BY, so takedown demands are possible.

If it were you

Verdict: Don't build another top-conference main-figure or poster library. Two late entrants in the same circle already proved the second one can't get stars. What you can take is a method: find a group that has to do something at the same time every year and has nowhere to look for references, hand-curate a small, high-quality library, ship it as a static site that's browsable at a glance, launch before that deadline, then use one hit of vertical-media exposure to pry open other channels.

Who it fits: People already inside a circle who know where everyone is stuck: grad students, junior faculty who have written defense slides and grant applications, engineers who have filed patents. You need to be able to judge which figure is "good," because manual curation is the only real barrier to entry for this kind of project. Not for people who want to win on code volume. This project has almost no technical content.

How to do it: Change the target, not the format. Directions worth testing: layout libraries for defense / proposal slides, technical roadmap figures for NSFC grant applications, mechanism diagrams for biology and materials, patent drawings, rebuttal writing patterns for top conferences. Each figure gets a source, tags, and "accepted / won an award" status. Later, add an AI tool that does "retrieve references → draft output," with users bringing their own API key and running in the browser for zero server cost.

How to get users: On launch day, go after the top WeChat Official Accounts in your circle (for AI, that's PaperWeekly, Machine Heart, QbitAI; for other fields, find their equivalents). Title it with a big number ("Screened 2,298 figures out of 48k papers"). Once you get coverage, pitch HelloGitHub, Ruan Yifeng's weekly, Reddit and overseas user groups with "featured by XX." Each time the data expands, switch to a new number and go out again. Put a "give it a Star" prompt at the top of the README.

When to launch: Watch the deadlines for your audience. For example, NSFC grant applications usually close around March each year, and proposal and defense season clusters in spring and at year-end. Launch three to four weeks early. Miss it and you wait until next year.

Signs you've misread it: Two weeks after launch, after one media hit, you're still under 200 stars. Or the reposts of your headline are about "AI image generation" rather than "reference library." That means this audience doesn't actually lack references, or someone has already built what you picked.

GitHub repo ↗

Research notes: competitor table, acquisition evidence, full research report
Competitors
NameWhereLaunchedScaleHow it differs from this one
dair-ai/ml-visualsOpen source2020-0517.4k starReusable ML figure templates, not a real top-conference main-figure reference library
dvgodoy/dl-visualsOpen source2021-051.6k starHand-drawn deep learning architecture assets, not top-conference references
Mondrian-He/Awesome-artistOpen source2025-11109 starAlso collects good top-conference figures, but only as an awesome list. No searchable site, no filtering
dwzhu-pku/PaperBanana / llmsresearch/paperbananaOpen source2026-01 / 2026-027.6k / 2.5k starAgent that auto-generates academic figures. No hand-curated reference library
ResearAI/AutoFigure(-Edit)Open source2025-12 / 2026-022.0k / 4.4k starPaper-to-editable-figure generation, no reference browsing
LigphiDonk/academic-figure-generator, OpenDCAI/Paper2AnyOpen source2026-03 / 2025-102.8k / 2.8k starPlatform: upload a paper, generate figures/slides
nature-skills and other Claude/Codex scientific-figure skillsOpen sourceFrom ¥2026-0452k to 1k stars, variesAgent skill image generation, no reference library
paperfig.ioWebNot verified84 sample figures + paid generationCommercial image-generation site, reference library is just a lead-gen page
Nano Banana / GPT-Image / ChatGPT draws directlyBig-company features—HugeGeneral-purpose image generation, produces generic flowcharts, no top-conference layout references
Where users come from
  • Verified Vertical WeChat media (PaperWeekly headline, Sohu repost): the author states in HelloGitHub #3781 that PaperWeekly ran it as the headline on launch day, 9000+ reads and 1800+ shares; Sohu 2026-09-21 repost; 9-21 forks in a single day, 18
  • Verified Author self-submissions to newsletters/communities: HelloGitHub issue #3781 (9-26), ruanyf/weekly #12058 (10-03), Reddit r/SideProject post
  • Verified Overseas developer community reshares: PyTorchKR LinkedIn Korean introduction 9-23; shared by Tahir Rasheed on LinkedIn and @dooo.sth.design on Threads
  • Verified Competitor / SEO site content: external link to a paper-banana.org long-form data analysis 9-22; indexed by aggregator sites like aikitapp
  • Verified CSDN reposts + Bilibili video: two tutorials on CSDN gitblog; Bilibili BV1yRaK6rEQH 9-24 posted 1,138 views
  • Inferred Domestic mirror AtomGit G-Star: README links an AtomGit repo and G-Star badge; how much traffic it brought is unknown
  • Inferred Trendshift chart: the data card says 10/18–19 made the Trendshift top 25; not separately verified this time

Research report on qwdwqfwq/topconf-paper-figure-gallery (top-conference main-figure inspiration gallery + FigureForge)

Date 2026-11-06 · Channel github · Repo github.com/qwdwqfwq/topconf-paper-figure-gallery · Live site qwdwqfwq.github.io/topconf-paper-figure-gallery/

Bottom line

A Chinese AI researcher's pure static "Figure 1 reference gallery": cropped the main figures from papers at ICLR/ICML/NeurIPS/CVPR/ACL/AAAI 2023–2026, hand-filtered to about 3.5k images, filterable by conference, year, acceptance tier and visual pattern. Later added FigureForge, a browser-local tool that searches the reference images and generates a first draft of the main figure. It took off thanks to a real pain point (no reference library for drawing Figure 1) plus a front-page feature by PaperWeekly, the top Chinese AI-academia WeChat account, on launch day. After that, the author submitted it to HelloGitHub, Ruan Yifeng's weekly, and Reddit, and it got reposted by Korea's PyTorchKR, CSDN, Bilibili, SEO sites and competitor blogs, building a long tail of 15–45 stars a day. The differentiation is "hand-curated, only well-designed Figure 1, with Oral/Spotlight badges," not AI image generation. The image-generation space was already crowded, so it got in from the "reference library" angle that nobody was doing seriously.

1. Data

  • Built 2026-09-17, 1,462 stars today (GitHub API, 2026-11-06) / data card 1,433, 73–74 forks, 5 open issues. Author account registered 2024-04, 2 followers, and this is their only repo with stars (GitHub API).
  • Timeline: 4 days after launch, 250 stars (per the paper-banana.org blog 2026-09-22 quote); "400+ stars in the first week" (self-reported by the author in HelloGitHub submission #3781,2026-09-26); 10-16 977 → 11-06 1,433 (our GitHub repo_day / data card daily). Over the last 7 days +182, first 7 days +134, single-day peak +46 (10-20). The Trendshift page shows +1,213 stars over 48 days.
  • Fork timing: 9-21 single day had 18 forks, other days 1–6 (GitHub forks API). 9-21 was the day of the PaperWeekly/Sohu repost, the only spike.
  • Commits: near-daily commits since 9-19, peak 10-06/07 with 30/23 commits in one day, v0.3→v0.5.0 released in four days from 9-19 to 9-23 (GitHub commits/releases API). The author iterated intensively after the spike.
  • The data card says 10/18–19 twice made the Trendshift top 25; not separately verified this time. trending_days_any=0 (never made GitHub Trending).
  • Not on HN (no related story in our hackernews library). No mentions in the X local corpus (x-radar 60 days).

II. Competitors

NameWhereLaunchedScaleHow it differs
dair-ai/ml-visualsOpen source2020-0517.4k starReusable ML figure templates (PPT), not a real paper main-figure library. Old project.
dvgodoy/dl-visualsOpen source2021-051.6k star200+ deep learning architecture diagrams, hand-drawn assets, not top-conference references
Mondrian-He/Awesome-artistOpen source2025-11109 starAlso collects top-conference "high-quality figures," but only as an awesome list: no searchable site, no filtering, no generation
dwzhu-pku/PaperBananaOpen source2026-017.6k starAutomatic academic figure generation from Google Research papers; generation agent, no hand-curated reference library
llmsresearch/paperbananaOpen source2026-022.5k starExtension of the PaperBanana clone, same as above
ResearAI/AutoFigure(-Edit)Open source2025-12 / 2026-022.0k / 4.4k starPaper-to-editable-scientific-figure generation
LigphiDonk/academic-figure-generatorOpen source2026-032.8k starPlatform: upload a paper, generate figures
OpenDCAI/Paper2AnyOpen source2025-102.8k starPaper to editable figures / technical roadmap figures / PPT
Yuan1z0825/nature-skills and other Claude/Codex scientific figure skillsOpen sourceFrom ¥2026-0452k / 2.8k / 2.3k / 1.2k stars, varyingagent skill for image generation, no reference-image browsing
paperfig.io/galleryweb (commercial)Not verified84 sample figures + paid generationa small set of examples per discipline; the goal is to sell image generation
figureforge.ioWebNot verifiedNot verifiedsame name, but it does composite layouts for paper submissions, a different problem
Nano Banana / GPT-Image / ChatGPT draws directlyBig-company features—Hugegeneral image generation; the author uses it as a baseline in the README ("a general chat box only produces generic flowcharts")

结论:"生成科研图"这条赛道 2026 年上半年已经被 PaperBanana、AutoFigure、一堆 skill 挤满;但"真实顶会 Figure 1 可检索参考库"在它之前基本空着(最接近的 Awesome-artist 只有 109 star)。它把参考库做成主产品,再把生图做成"基于参考库的 RAG 生图",正好站在两个赛道的接缝上。

3. Growth tactics (evidence)

  1. PaperWeekly headline (confirmed): The author's submissions to HelloGitHub #3781 and Ruan Yifeng's weekly #12058 are literally titled "Featured by PaperWeekly headlines…". They state: "Featured by PaperWeekly headlines on launch day, 9000+ reads and 1800+ shares." Sohu reposted it the same day (2026-09-21 14:03) as "Can't draw your paper's Figure 1? 2298 open-source main figures from six top conferences, this inspiration gallery blew up." Forks hit 9-21 in a single day (18), which matches. A shares-to-reads ratio of 1:5 suggests the content was passed around heavily in grad student groups.
  2. Author self-submitted to newsletters/communities (confirmed): HelloGitHub issue (9-26), ruanyf/weekly issue (10-03), and a Reddit r/SideProject post "[Open Source] Top Conference Paper Figure Gallery + FigureForge" (page blocked by a captcha, body not read).
  3. Overseas secondary spread (confirmed): PyTorchKR (Korean PyTorch user group, 1,247 LinkedIn followers) 9-23 posted an introduction in Korean; a LinkedIn personal account, Tahir Rasheed, reshared it; a Threads designer account, @dooo.sth.design, reshared it (at that time 1,086 stars); aggregator sites such as aikitapp.com indexed it.
  4. Competitor/SEO site wrote a long-form piece (confirmed): paper-banana.org (a site running the PaperBanana business) 9-22 published "What 2,298 Figure 1s Reveal", a data analysis with an outbound link. A second piece was not verified separately. The competitor rode its heat for SEO while also sending it traffic.
  5. CSDN gitblog auto-SEO articles + Bilibili video (confirmed): two CSDN tutorials from gitblog_00561 / 00463 (gitblog is an account that batch-reposts GitHub projects); a Bilibili video "Scientific figure rescue! 3528 top-conference figures searchable for free" 9-24 was posted, with 1,138 views.
  6. Domestic mirrors (confirmed): hosted on AtomGit with a "G-Star selected" badge; a third-party mirror at jingweb.me/tools/paperfig/ copies the whole site.
  7. Conversion design inside the README (confirmed): a star-hint GIF at the top ("click the star button in the top right"), a demo GIF, a wall of generated outputs, and a badge wall, all converting landing visitors into stars.
  8. No evidence of ad buys or paid influencers found; no traction on X or HN.

4. Why it took off

  • The pain point is sharp and seasonal: before ICLR/CVPR deadlines, every student writing a paper has to make Figure 1, and until now they had to flip through PDFs one by one (the PaperWeekly post and PyTorchKR thread both open with exactly that). Launch in mid-September lands right before the ICLR 2027 deadline season (around late September/early October).
  • One front-page exposure at a top vertical media outlet lit the fuse: traffic in the Chinese AI academic scene concentrates on a few WeChat Official Accounts. A single PaperWeekly front-page piece got 9000 reads / 1800 shares, and it passed 400+ stars in the first week. All later reposts cited the "featured on the front page" line as endorsement.
  • The author used the spike as leverage: in the four days after the spike, they shipped 4 versions in a row, added 2026-year conference coverage, Oral/Spotlight badges, and FigureForge. The figure count went 2,298 → 2,730 → 3,452/3,516, and each new number became a new round of reposting headlines ("3528 figures", "3516 figures").
  • Low barrier, value visible at a glance: a pure static site, no sign-up, opening it shows a wall of main figures right away. FigureForge brings its own key and runs locally in the browser, is free, and has zero friction.
  • Retention comes from the "bookmark" nature: a star here is effectively a bookmark, since you come back to it while drawing figures, so star conversion is high. Daily growth held steady at 15–45.

5. Differentiation

  • Compared with image generation tools (PaperBanana, AutoFigure, skills), its core is a manually curated library of real top-conference reference figures (25+ rules to filter out matplotlib charts, tables, and screenshots, dHash deduplication, and page-by-page manual review), and image generation is framed as "retrieve similar reference figures first, summarize the layout, then generate." Generation itself is not new; "generation with a reference library" is the story it tells.
  • Compared with figure template libraries (ml-visuals, dl-visuals): those are raw material, while this offers "what figures from papers that got into top conferences look like", plus Oral/Spotlight/Best badges. It hits the anxiety of "wanting your figures to look like the accepted paper's."
  • Compared with list-style collections like Awesome-artist: a searchable, filterable site, visual-pattern tags, and data scale.
  • Was it engineered? Yes. The titles PaperWeekly picked and the reposts everywhere are all about "a reference library of main figures", not generation. FigureForge was added after the spike, serving to add buzz and retention.

6. Visibility and copycats

  • Visibility: spreading within small circles. The Chinese AI grad-student circle (PaperWeekly, CSDN, Bilibili) and Korea's PyTorchKR carried it. English-language X and HN had no traction and there were no major media. It is open source with no revenue. The README carries a referral promo link to Infistar.cc (an LLM API relay station) with a commission code, the online site has a tip jar, and sponsorship is openly solicited. Monetization is API-relay commissions, scale unknown.
  • Copycats (already appeared):
    • fengxijia/PaperFigureLib + paperfigure.net: built 9-20 (3 days after it, right around the spike), claims 5,672 papers and 70,452 figures, sorted by figure type (method diagrams, bar charts, tables…), but only 25 stars on GitHub. Bigger in scope but it didn't catch on.
    • lee-plus-plus/topconf-poster-gallery: built 10-09, a gallery of 35,884 top-conference posters with 20 stars. The author lee-plus-plus 10-05 had just submitted a UI issue/PR to this repo, and the naming copies "topconf-…-gallery".
    • jingweb.me/tools/paperfig/: an unofficial mirror of the entire site.
    • paperfig.io's gallery page (a commercial image-generation site adding a reference library; date unverified).
  • The copycats are bigger but didn't take off, which suggests its moat is the launch timing + that media exposure + "curated, small and precise", not data volume.

VII. What to take away

  • The "reference library + AI generation based on it" playbook transfers: posters (already copied), defense slides, patent drawings, grant proposal framework diagrams, other disciplines (mechanism diagrams in biology and materials), product/UI screenshot libraries.
  • Cold-start path: launch just before deadline season → get a single front-page feature from a vertical top WeChat account (PaperWeekly, Machine Heart, QbitAI) → use "featured on XX's front page" to pitch HelloGitHub / Ruan Yifeng's weekly / Reddit / overseas user groups → wait for CSDN and SEO sites to repost.
  • Use the number as the headline (2298 → 3516); every data expansion is a new round of promotional material. Put the star-hint GIF and output wall at the top of the README.
  • Earn stars open source, monetize through API-relay commissions and sponsorships, zero server cost (pure static + BYOK).

8. Not sure

  • The PaperWeekly original and the read/share counts are only the author's self-report; I did not see the original article directly.
  • The specific sources of the 10/18–19 Trendshift top 25 position and the daily growth recovery in late October and early November (30–45/day) are not identified (AtomGit G-Star, the second paper-banana piece, or other reposts?).
  • Our token cannot access the stargazer list, so we can't check for star farming. Forks and issue authors look like real users and I see no sign of inflated numbers, but the author's follower-to-star ratio of 2 for 1.4k stars is low (common for utility-type projects).
  • Copyright risk: figures are cropped from publisher PDFs, and the README declares CC BY plus a 72-hour takedown policy. Not all CVPR/AAAI papers are CC BY, so takedown requests are possible.
  • Unknown whether the Infistar sponsorship is paid, or how much commission income it brings.

Sources

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