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In this paper, we take a significant step towards real-world applicability ofmonocular neural avatar reconstruction by contributing InstantAvatar, a systemthat can reconstruct human avatars from a monocular video within seconds, andthese avatars can be animated and rendered at an interactive rate. To achievethis efficiency we propose a carefully designed and engineered system, thatleverages emerging acceleration structures for neural fields, in combinationwith an efficient empty space-skipping strategy for dynamic scenes. We alsocontribute an efficient implementation that we will make available for researchpurposes. Compared to existing methods, InstantAvatar converges 130x faster andcan be trained in minutes instead of hours. It achieves comparable or evenbetter reconstruction quality and novel pose synthesis results. When given thesame time budget, our method significantly outperforms SoTA methods.InstantAvatar can yield acceptable visual quality in as little as 10 secondstraining time.

InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds