69 citations · 134 across the 21 of their papers we have counts for
22 papers
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…
Aligning Proteins and Language: A Foundation Model for Protein Retrieval
Qifeng Wu, Zhengzhe Liu, Han Zhu +3
This paper aims to retrieve proteins with similar structures and semantics from large-scale protein dataset, facilitating the functional interpretation of protein structures derive…
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…
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…
How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach
Chirui Chang, Jiahui Liu, Zhengzhe Liu +6
Recent advancements in video diffusion models enable the generation of photorealistic videos with impressive 3D consistency and temporal coherence. However, the extent to which the…
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…
Texture Generation on 3D Meshes with Point-UV Diffusion
Xin Yu, Peng Dai, Wenbo Li +3
In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with…
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…
DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation
Zhengzhe Liu, Peng Dai, Ruihui Li +2
In this paper, we present a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between text and shape modalities for gen…
You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
3D scene understanding, e.g., point cloud semantic and instance segmentation, often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to…
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…
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
Yingxian Chen, Zhengzhe Liu, Baoheng Zhang +3
Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long vi…
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
Tianyu Wang, Xiaowei Hu, Zhengzhe Liu +1
LiDAR-produced point clouds are the major source for most state-of-the-art 3D object detectors. Yet, small, distant, and incomplete objects with sparse or few points are often hard…
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…
Towards Implicit Text-Guided 3D Shape Generation
Zhengzhe Liu, Yi Wang, Xiaojuan Qi +1
In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable o…
3D-to-2D Distillation for Indoor Scene Parsing
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
Indoor scene semantic parsing from RGB images is very challenging due to occlusions, object distortion, and viewpoint variations. Going beyond prior works that leverage geometry in…
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to…
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao +3
In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on to…
Global Texture Enhancement for Fake Face Detection in the Wild
Zhengzhe Liu, Xiaojuan Qi, Philip Torr
Generative Adversarial Networks (GANs) can generate realistic fake face images that can easily fool human beings.On the contrary, a common Convolutional Neural Network(CNN) discrim…