22 citations · 58 across the 21 of their papers we have counts for
17 papers · 1 filter
FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity
Yuchen Rao, Xuqian Ren, Yinyu Nie +4
Recovering complete 3D representations of objects from few casual image captures remains a significant challenge. Recent 3D generative models, particularly those based on Flow-Matc…
LumiX: Structured and Coherent Text-to-Intrinsic Generation
Xu Han, Biao Zhang, Xiangjun Tang +2
We present LumiX, a structured diffusion framework for coherent text-to-intrinsic generation. Conditioned on text prompts, LumiX jointly generates a comprehensive set of intrinsic…
PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning
Jianqi Chen, Biao Zhang, Xiangjun Tang +1
6D object pose estimation, which predicts the transformation of an object relative to the camera, remains challenging for unseen objects. Existing approaches typically rely on expl…
BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patch
Pu Li, Wenhao Zhang, Weize Quan +3
Boundary representation (B-rep) is the de facto standard for CAD model representation in modern industrial design. The intricate coupling between geometric and topological elements…
iFlame: Interleaving Full and Linear Attention for Efficient Mesh Generation
Hanxiao Wang, Biao Zhang, Weize Quan +2
This paper propose iFlame, a novel transformer-based network architecture for mesh generation. While attention-based models have demonstrated remarkable performance in mesh generat…
Generative Human Geometry Distribution
Xiangjun Tang, Biao Zhang, Peter Wonka
Realistic human geometry generation is an important yet challenging task, requiring both the preservation of fine clothing details and the accurate modeling of clothing-body intera…
MatCLIP: Light- and Shape-Insensitive Assignment of PBR Material Models
Michael Birsak, John Femiani, Biao Zhang +1
Assigning realistic materials to 3D models remains a significant challenge in computer graphics. We propose MatCLIP, a novel method that extracts shape- and lighting-insensitive de…
Geometry Distributions
Biao Zhang, Jing Ren, Peter Wonka
Neural representations of 3D data have been widely adopted across various applications, particularly in recent work leveraging coordinate-based networks to model scalar or vector f…
LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion
Biao Zhang, Peter Wonka
This paper introduces a novel hierarchical autoencoder that maps 3D models into a highly compressed latent space. The hierarchical autoencoder is specifically designed to tackle th…
Vivid-ZOO: Multi-View Video Generation with Diffusion Model
Bing Li, Cheng Zheng, Wenxuan Zhu +4
While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new c…
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking
Wei Cao, Chang Luo, Biao Zhang +2
We introduce Motion2VecSets, a 4D diffusion model for dynamic surface reconstruction from point cloud sequences. While existing state-of-the-art methods have demonstrated success i…
InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions
Qian Wang, Biao Zhang, Michael Birsak +1
Recent works have explored text-guided image editing using diffusion models and generated edited images based on text prompts. However, the models struggle to accurately locate the…
MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path
Qian Wang, Biao Zhang, Michael Birsak +1
Image generation using diffusion can be controlled in multiple ways. In this paper, we systematically analyze the equations of modern generative diffusion networks to propose a fra…
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
Biao Zhang, Jiapeng Tang, Matthias Niessner +1
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface…
3DILG: Irregular Latent Grids for 3D Generative Modeling
Biao Zhang, Matthias Nießner, Peter Wonka
We propose a new representation for encoding 3D shapes as neural fields. The representation is designed to be compatible with the transformer architecture and to benefit both shape…
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
Biao Zhang, Peter Wonka
We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set w…
Point Cloud Instance Segmentation using Probabilistic Embeddings
Biao Zhang, Peter Wonka
In this paper we propose a new framework for point cloud instance segmentation. Our framework has two steps: an embedding step and a clustering step. In the embedding step, our mai…