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Found 13 papers, 8 papers with code
FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
However, due to the challenge of directly deforming the mesh representation to approach the target topology, most methodologies learn an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction.
DreamReward: Text-to-3D Generation with Human Preference
Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer.
V3D: Video Diffusion Models are Effective 3D Generators
To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator.
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model.
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method
In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field.
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation
Extensive experiments demonstrate the capability of our method in generating high-flexibility text-guided 3D models from the monocular video, while also showing improved reconstruction performance over existing non-rigid reconstruction methods.
Score Regularized Policy Optimization through Diffusion Behavior
Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
In this paper, we propose DIFFender, a novel defense method that leverages a text-guided diffusion model to defend against adversarial patches.
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e., $7. 5$).
Robust Classification via a Single Diffusion Model
Since our method does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
Ranked #2 on
Adversarial Defense
on CIFAR-10
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image
As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images.
GNOT: A General Neural Operator Transformer for Operator Learning
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors
In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible.
