Virtual Try-On, 1 click Chrome extension for visualizing clothes on you

Hacker News

We created a Chrome extension to let you virtually try on clothes from popular websites like Nike and Amazon using your own photo (I also use it for my kids - as a parent, they are the one I mostly buy clothes for). Key features and motivations: - One-click visualization on supported websites: no more downloading and re-uploading images -> this was really the flow I wanted and that other extension or website do not offer right now. I see something I like, I click, and that's it. - Use your own picture as the model - Works for tops, pants and dresses on multiple clothing sites (does not work for shoes or accessories) We used this AI to build the engine: https://idm-vton.github.io/ While working on it there was a new cool one (Kolors) that has been released and that we would like to try as well in the Future (anyone has been using it?) It's a weekend project built with my friend Kalkidan from Ethiopia, currently in beta. We'd love your feedback!

Visit product View on Hacker NewsAug 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, visual · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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