6 papers
Advancing high-fidelity 3D and Texture Generation with 2.5D latents
Xin Yang, Jiantao Lin, Yingjie Xu +2
Despite the availability of large-scale 3D datasets and advancements in 3D generative models, the complexity and uneven quality of 3D geometry and texture data continue to hinder t…
DiMeR: Disentangled Mesh Reconstruction Model
Lutao Jiang, Jiantao Lin, Kanghao Chen +7
We propose DiMeR, a novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction. Existing methods confront two persistent obstacl…
Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
Jiantao Lin, Xin Yang, Meixi Chen +7
Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods of…
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs
Xinli Xu, Wenhang Ge, Dicong Qiu +8
Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulatio…
PRM: Photometric Stereo based Large Reconstruction Model
Wenhang Ge, Jiantao Lin, Guibao Shen +4
We propose PRM, a novel photometric stereo based large reconstruction model to reconstruct high-quality meshes with fine-grained local details. Unlike previous large reconstruction…
FlexGen: Flexible Multi-View Generation from Text and Image Inputs
Xinli Xu, Wenhang Ge, Jiantao Lin +5
In this work, we introduce FlexGen, a flexible framework designed to generate controllable and consistent multi-view images, conditioned on a single-view image, or a text prompt, o…