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Found 12 papers, 7 papers with code
GetMesh: A Controllable Model for High-quality Mesh Generation and Manipulation
Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares.
Point Cloud Pre-training with Diffusion Models
This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object.
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.
Ranked #1 on
Blind Face Restoration
on LFW
MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR
In this work, we propose Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR (\textbf{MATLABER}) that leverages a novel latent BRDF auto-encoder for material generation.
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Besides, we devise hierarchical guidance and patch-based methods, enabling the GDP to generate images of arbitrary resolutions.
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
In this work, we design a novel sparse latent point diffusion model for mesh generation.
Guided Diffusion Model for Adversarial Purification
In this paper, we propose a novel purification approach, referred to as guided diffusion model for purification (GDMP), to help protect classifiers from adversarial attacks.
Accelerating Diffusion Models via Early Stop of the Diffusion Process
By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise.
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
The RFNet refines the coarse output of the CGNet and further improves quality of the completed point cloud.
Towards Evaluating and Training Verifiably Robust Neural Networks
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks.
Fastened CROWN: Tightened Neural Network Robustness Certificates
We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints.
POPQORN: Quantifying Robustness of Recurrent Neural Networks
The vulnerability to adversarial attacks has been a critical issue for deep neural networks.
