4 citations · 8 across the 9 of their papers we have counts for
9 papers
CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation
Jingyu Hu, Weilong Yan, Zhengzhe Liu +4
This paper presents a latent-space 3D shape editing framework built upon a coupled neural shape (CNS) representation and a neural feature volume optimization. This work extends CNS…
PEGAsus: 3D Personalization of Geometry and Appearance
Jingyu Hu, Bin Hu, Ka-Hei Hui +4
We present PEGAsus, a new framework capable of generating Personalized 3D shapes by learning shape concepts at both Geometry and Appearance levels. First, we formulate 3D shape per…
Not-So-Optimal Transport Flows for 3D Point Cloud Generation
Ka-Hei Hui, Chao Liu, Xiaohui Zeng +2
Learning generative models of 3D point clouds is one of the fundamental problems in 3D generative learning. One of the key properties of point clouds is their permutation invarianc…
Object-level Scene Deocclusion
Zhengzhe Liu, Qing Liu, Chirui Chang +6
Deoccluding the hidden portions of objects in a scene is a formidable task, particularly when addressing real-world scenes. In this paper, we present a new self-supervised PArallel…
CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization
Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu +2
This paper introduces a new approach based on a coupled representation and a neural volume optimization to implicitly perform 3D shape editing in latent space. This work has three…
Make-A-Shape: a Ten-Million-scale 3D Shape Model
Ka-Hei Hui, Aditya Sanghi, Arianna Rampini +4
Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantia…
EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui +3
This paper presents a new text-guided technique for generating 3D shapes. The technique leverages a hybrid 3D shape representation, namely EXIM, combining the strengths of explicit…
Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation
Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu +2
This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet dom…
ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation
Zhengzhe Liu, Peng Dai, Ruihui Li +2
Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structura…