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UltraFaceSwap – Multi-face swap for photos, GIFs, and videos

Hacker News

UltraFaceSwap – Multi-face swap for photos, GIFs, and videos

Hi HN, I just launched UltraFaceSwap ( https://ultrafaceswap.com ), an AI face swapping tool that handles photos, GIFs, and videos with single or multiple face swaps. *What makes it different:* - *Multi-person swapping*: Swap multiple faces in one image/video (most tools only do one) - *Multi-face GIF support*: First tool I've seen that handles multi-person face swapping in animated GIFs - *Video quality*: Maintains temporal consistency (no flickering between frames) - *Comprehensive format support*: Works seamlessly across photos, GIFs, and videos *Why I built it:* A friend needed a face swap tool for their project, so I tried every option on the market. They all had limitations: - Only single face swaps - Poor video quality with flickering - No proper GIF support, especially for multiple faces - Clunky UX requiring multiple steps So I built what I wished existed. *Pricing model:* - Free trial to test it out - Daily free credits (casual users might never pay) - Pay-as-you-go for volume (no subscription) *Tech challenges I solved:* - Face tracking across video frames - Handling occlusions (when faces turn away) - Multi-face detection and assignment in animated GIFs - GIF color palette preservation while swapping multiple faces *Current limitations:* - Processing videos takes time (30s video = ~3-5 min processing) - Works best with clear, well-lit faces - Very fast motion can still cause artifacts I'm a solo developer and spent 2 months getting the quality right. Would love feedback from HN: - What use cases would you have for this? - Is multi-face swapping something you've needed? - Any concerns about the technology? Try it out – I think you'll find the quality noticeably better than alternatives.

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Actual performance

2points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, user, single · Missing: mac, agents, macos
28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
18%18% 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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