NOVA-3D: Non-overlapped Views for 3D Anime Character Reconstruction
Hongsheng Wang, Nanjie Yao, Xinrui Zhou, Shengyu Zhang, Huahao Xu, Fei, Wu, Feng Lin

TL;DR
NOVA-3D introduces a novel framework for reconstructing full-body anime characters from non-overlapped front and back views, overcoming challenges of view overlap absence and data scarcity, and achieves superior detail fidelity.
Contribution
The paper presents a new view-aware feature fusion method and a dedicated dataset for 3D anime character reconstruction from non-overlapped views, advancing the field significantly.
Findings
Outperforms baseline approaches in detail fidelity.
Achieves state-of-the-art results in animation head reconstruction.
Provides a new dataset and benchmark for future research.
Abstract
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…
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Taxonomy
TopicsHuman Motion and Animation · Video Analysis and Summarization · Image Processing and 3D Reconstruction
