In the animation industry, 3D modelers typically rely on front and back
non-overlapped concept designs to guide the 3D modeling of anime characters.
However, there is currently a lack of automated approaches for generating anime
characters directly from these 2D designs. In light of this, we explore a novel
task of reconstructing anime characters from non-overlapped views. This
presents two main challenges: existing multi-view approaches cannot be directly
applied due to the absence of overlapping regions, and there is a scarcity of
full-body anime character data and standard benchmarks. To bridge the gap, we
present Non-Overlapped Views for 3D \textbf{A}nime Character Reconstruction
(NOVA-3D), a new framework that implements a method for view-aware feature
fusion to learn 3D-consistent features effectively and synthesizes full-body
anime characters from non-overlapped front and back views directly. To
facilitate this line of research, we collected the NOVA-Human dataset, which
comprises multi-view images and accurate camera parameters for 3D anime
characters. Extensive experiments demonstrate that the proposed method
outperforms baseline approaches, achieving superior reconstruction of anime
characters with exceptional detail fidelity. In addition, to further verify the
effectiveness of our method, we applied it to the animation head reconstruction
task and improved the state-of-the-art baseline to 94.453 in SSIM, 7.726 in
LPIPS, and 19.575 in PSNR on average. Codes and datasets are available at
this https URL

通过非重叠视图从二维设计中重建三维动漫角色的研究，提出了 NOVA-3D 方法来有效地学习和合成全身动漫角色，实现了优秀的细节保真度，并在动画头部重建任务上将基准值提高至 94.453（SSIM）、7.726（LPIPS）和 19.575（PSNR）。