40 citations · 77 across the 26 of their papers we have counts for
26 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…
SkelGen4D: Weakly-Supervised Skeleton-Based 4D Generation for Text-Driven Mesh Animation
Hao Feng, Zhi Zuo, Jia-Hui Pan +6
We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and vid…
EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation
Runsong Zhu, Jiaxin Guo, Xiaoyang Guo +9
This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on additional preprocessing, we d…
PhyMix: Towards Physically Consistent Single-Image 3D Indoor Scene Generation with Implicit--Explicit Optimization
Dongli Wu, Jingyu Hu, Ka-Hei Hui +4
Existing methods for single-image 3D indoor scene generation often produce visually plausible results that violate real-world physical constraints, limiting their reliability in ro…
coDrawAgents: A Multi-Agent Dialogue Framework for Compositional Image Generation
Chunhan Li, Qifeng Wu, Jia-Hui Pan +7
Text-to-image generation has advanced rapidly, but existing models still struggle with faithfully composing multiple objects and preserving their attributes in complex scenes. We p…
Imagine a City: CityGenAgent for Procedural 3D City Generation
Zishan Liu, Zecong Tang, RuoCheng Wu +6
The automated generation of interactive 3D cities is a critical challenge with broad applications in autonomous driving, virtual reality, and embodied intelligence. While recent ad…
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…
VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction
Jiarong Liang, Max Ku, Ka-Hei Hui +2
Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks rely on recognition-style protoco…
COS3D: Collaborative Open-Vocabulary 3D Segmentation
Runsong Zhu, Ka-Hei Hui, Zhengzhe Liu +5
Open-vocabulary 3D segmentation is a fundamental yet challenging task, requiring a mutual understanding of both segmentation and language. However, existing Gaussian-splatting-base…
OPA-Pack: Object-Property-Aware Robotic Bin Packing
Jia-Hui Pan, Yeok Tatt Cheah, Zhengzhe Liu +5
Robotic bin packing aids in a wide range of real-world scenarios such as e-commerce and warehouses. Yet, existing works focus mainly on considering the shape of objects to optimize…
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting
Runsong Zhu, Shi Qiu, Zhengzhe Liu +4
Lifting multi-view 2D instance segmentation to a radiance field has proven to be effective to enhance 3D understanding. Existing methods rely on direct matching for end-to-end lift…
WonderVerse: Extendable 3D Scene Generation with Video Generative Models
Hao Feng, Zhi Zuo, Jia-Hui Pan +4
We introduce \textit{WonderVerse}, a simple but effective framework for generating extendable 3D scenes. Unlike existing methods that rely on iterative depth estimation and image i…
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…
PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion
Runsong Zhu, Shi Qiu, Qianyi Wu +3
Panoptic lifting is an effective technique to address the 3D panoptic segmentation task by unprojecting 2D panoptic segmentations from multi-views to 3D scene. However, the quality…
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…
SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization
Jia-Hui Pan, Ka-Hei Hui, Xiaojie Gao +4
Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach ba…
CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration
Jingyu Hu, Ka-Hei Hui, Zhengzhe liu +2
This paper presents CLIPXPlore, a new framework that leverages a vision-language model to guide the exploration of the 3D shape space. Many recent methods have been developed to en…
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…
Neural Wavelet-domain Diffusion for 3D Shape Generation
Ka-Hei Hui, Ruihui Li, Jingyu Hu +1
This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose…
Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes
Ka-Hei Hui, Ruihui Li, Jingyu Hu +1
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural te…
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
Runsong Zhu, Di Kang, Ka-Hei Hui +6
Reconstructing 3D geometry from \emph{unoriented} point clouds can benefit many downstream tasks. Recent shape modeling methods mostly adopt implicit neural representation to fit a…
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
Ruihui Li, Xianzhi Li, Ka-Hei Hui +1
We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN is able to…