ICON-3D Avatar Creator from 2D Pixels

Hacker News

Realistic virtual humans will play a central role in mixed and augmented reality, forming a critical foundation for the Metaverse and supporting remote presence, collaboration, education, and entertainment. To enable this, new tools are needed to easily create large-scale 3D virtual humans that can be readily animated. However, current methods need either posed 3D scans captured by expensive scanning equipment or 2D images with carefully controlled user poses. Both of them can't scale up easily. ICON ("Implicit Clothed humans Obtained from Normals") takes a step towards robust 3D clothed human reconstruction from in-the-wild images. This also enables creating animatable avatars directly from video with personalized and natural pose-dependent cloth deformation. Homepage: https://icon.is.tue.mpg.de/ Github: https://github.com/YuliangXiu/ICON Google Colab: https://colab.research.google.com/drive/1-AWeWhPvCTBX0KfMtgt... Video: https://www.youtube.com/watch?v=ZufrPvooR2Q Paper: https://arxiv.org/abs/2112.09127

Visit product View on Hacker NewsFeb 19, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, video, google · Missing: mobile apps, ios, entrepreneurs

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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