3 citations · 4 across the 16 of their papers we have counts for
15 papers · 1 filter
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…
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…