4 citations · 4 across the 2 of their papers we have counts for
6 papers
Map2World: Segment Map Conditioned Text to 3D World Generation
Jaeyoung Chung, Suyoung Lee, Jianfeng Xiang +2
3D world generation is essential for applications such as immersive content creation or autonomous driving simulation. Recent advances in 3D world generation have shown promising r…
Native and Compact Structured Latents for 3D Generation
Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu +8
Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to cap…
NeAR: Coupled Neural Asset-Renderer Stack
Hong Li, Chongjie Ye, Houyuan Chen +12
Neural asset authoring and neural rendering have traditionally evolved as disjoint paradigms: one generates digital assets for fixed graphics pipelines, while the other maps conven…
MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
Ruicheng Wang, Sicheng Xu, Yue Dong +6
We propose MoGe-2, an advanced open-domain geometry estimation model that recovers a metric scale 3D point map of a scene from a single image. Our method builds upon the recent mon…
Structured 3D Latents for Scalable and Versatile 3D Generation
Jianfeng Xiang, Zelong Lv, Sicheng Xu +6
We introduce a novel 3D generation method for versatile and high-quality 3D asset creation. The cornerstone is a unified Structured LATent (SLAT) representation which allows decodi…
MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision
Ruicheng Wang, Sicheng Xu, Cassie Dai +4
We present MoGe, a powerful model for recovering 3D geometry from monocular open-domain images. Given a single image, our model directly predicts a 3D point map of the captured sce…