Yu Deng, Jiaolong Yang, Jianfeng Xiang, Xin Tong
3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still cannot generate highly-realistic images with fine det...
Jianfeng Xiang, Zhiqing Lv, Sicheng Xu, Deng‐Guang Yu et al.
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 decoding to different output formats, such as Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a spa...
Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang et al.
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 scene with an affine-invariant representation, which is agnostic to true global scale and shift. This new representation pr...