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Found 22 papers, 16 papers with code
ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding
Building upon the 3D-aware discrete tokens, we innovatively construct a large-scale continuous training dataset named 3D-Alpaca, encompassing generation, comprehension, and editing, thus providing rich resources for future research and training.
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning
Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering.
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model.
RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
Bimanual manipulation is essential in robotics, yet developing foundation models is extremely challenging due to the inherent complexity of coordinating two robot arms (leading to multi-modal action distributions) and the scarcity of training data.
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applications.
MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization
Meshes are the de facto 3D representation in the industry but are labor-intensive to produce.
Freeplane: Unlocking Free Lunch in Triplane-Based Sparse-View Reconstruction Models
We first analyze the role of triplanes in feed-forward methods and find that the inconsistent multi-view images introduce high-frequency artifacts on triplanes, leading to low-quality 3D meshes.
Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels
Vidu4D also contains a novel initialization state that provides a proper start for the warping fields in DGS.
MicroDreamer: Efficient 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
Optimization-based approaches, such as score distillation sampling (SDS), show promise in zero-shot 3D generation but suffer from low efficiency, primarily due to the high number of function evaluations (NFEs) required for each sample and the limitation of optimization confined to latent space.
FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
However, since directly reconstructing triangle meshes from multi-view images is challenging, most methodologies opt to 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
Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models.
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
As RDC does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
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.
