OHTA: One-shot Hand Avatar via Data-driven Implicit Priors
Xiaozheng Zheng Chao Wen Zhuo Su Zeran Xu Zhaohu Li Yang Zhao Zhou Xue
In this paper, we delve into the creation of one-shot hand avatars, attaining
high-fidelity and drivable hand representations swiftly from a single image.
With the burgeoning domains of the digital human, the need for quick and
personalized hand avatar creation has become increasingly critical. Existing
techniques typically require extensive input data and may prove cumbersome or
even impractical in certain scenarios. To enhance accessibility, we present a
novel method OHTA (One-shot Hand avaTAr) that enables the creation of detailed
hand avatars from merely one image. OHTA tackles the inherent difficulties of
this data-limited problem by learning and utilizing data-driven hand priors.
Specifically, we design a hand prior model initially employed for 1) learning
various hand priors with available data and subsequently for 2) the inversion
and fitting of the target identity with prior knowledge. OHTA demonstrates the
capability to create high-fidelity hand avatars with consistent animatable
quality, solely relying on a single image. Furthermore, we illustrate the
versatility of OHTA through diverse applications, encompassing text-to-avatar
conversion, hand editing, and identity latent space manipulation.