3 citations · 4 across the 16 of their papers we have counts for
16 papers
FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection
Jiaoyang Chen, Bin Hu, Jingyu Hu +3
AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to identify. Existing detectors usua…
FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation
Hao Feng, Zhi Zuo, MingJian Liang +6
We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than merely visually plausible layout…
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…
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…
LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
Weilong Yan, Haipeng Li, Hao Xu +4
This paper introduces LaS-Comp, a zero-shot and category-agnostic approach that leverages the rich geometric priors of 3D foundation models to enable 3D shape completion across div…
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…
Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation
Chongyang Xu, Haipeng Li, Shen Cheng +4
Bimanual manipulation requires policies that can reason about 3D geometry, anticipate how it evolves under action, and generate smooth, coordinated motions. However, existing metho…
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…
Failing on Bias Mitigation: A Case Study on the Challenges of Fairness in Government Data
Hongbo Bo, Jingyu Hu, Debbie Watson +1
The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a ca…
Hierarchical Neural Semantic Representation for 3D Semantic Correspondence
Keyu Du, Jingyu Hu, Haipeng Li +4
This paper presents a new approach to estimate accurate and robust 3D semantic correspondence with the hierarchical neural semantic representation. Our work has three key contribut…
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…
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…
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…