5 papers
GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models
Sai Karthikey Pentapati, Shashank Gupta, Rajesh Sureddi +3
We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes. Instead of optimizing the parameterization of…
Learning Convex Decomposition via Feature Fields
Yuezhi Yang, Qixing Huang, Mikaela Angelina Uy +1
This work proposes a new formulation to the long-standing problem of convex decomposition through learning feature fields, enabling the first feed-forward model for open-world conv…
ART-DECO: Arbitrary Text Guidance for 3D Detailizer Construction
Qimin Chen, Yuezhi Yang, Wang Yifan +4
We introduce a 3D detailizer, a neural model which can instantaneously (in <1s) transform a coarse 3D shape proxy into a high-quality asset with detailed geometry and texture as gu…
GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations
Yuezhi Yang, Haitao Yang, Kiyohiro Nakayama +3
We present GenAnalysis, an implicit shape generation framework that allows joint analysis of man-made shapes, including shape matching and joint shape segmentation. The key idea is…
GenVDM: Generating Vector Displacement Maps From a Single Image
Yuezhi Yang, Qimin Chen, Vladimir G. Kim +3
We introduce the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. Given a single input image, our…