3 papers indexed
Zhendong Wang, Jintong Wang, Chen Liu, Yao Jin, Ligang Liu, Huamin Wang
Digital sewing patterns typically consist of disjoint 2D panels without explicit stitch annotations, making downstream 3D modeling reliant on labor-intensive expert specification. In this paper, we present a graph-based learning framework that reconstructs two-level stitching inf…
Yuxiao Wu, Weile Li, Boyi Zhu, Yumeng Liu, Youcheng Cai, Ligang Liu
Reconstructing high-fidelity 3D models of highly articulated animals, such as dogs, from a single in-the-wild image remains a formidable challenge. In this paper, we introduce CORGI, a novel framework for consistency-aware 3D dog reconstruction from a single unconstrained image t…
Huangsheng Du, Haoran Zhu, Youcheng Cai, Jinyang Meng, Ligang Liu
We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural rendering methods such as RenderFormer achieve promising cross-scene generalization, but suffer from lim…