65 skills found · Page 1 of 3
TencentARC / InstantMeshInstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
AiuniAI / Unique3D[NeurIPS 2024] Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
wgsxm / PartCrafter[NeurIPS 2025] PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers
MIT-SPARK / Kimera VIOVisual Inertial Odometry with SLAM capabilities and 3D Mesh generation.
nv-tlabs / LLaMA MeshUnifying 3D Mesh Generation with Language Models
caiyuanhao1998 / Open DiffusionGSBaking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction (ICCV 2025)
HKUST-SAIL / CraftsMan3DCraftsMan: High-fidelity Mesh Generation with 3D Native Diffusion and Interactive Geometry Refiner
ThibaultGROUEIX / AtlasNetThis repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image.
if-ai / ComfyUI IF TrellisComfyUI TRELLIS is a large 3D asset generation in various formats, such as Radiance Fields, 3D Gaussians, and meshes. The cornerstone of TRELLIS is a unified Structured LATent (SLAT) representation that allows decoding to different output formats and Rectified Flow Transformers tailored for SLAT as the powerful backbones.
walsvid / Pixel2MeshPlusPlus[ICCV2019] Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation.
OpenMeshLab / MeshXL[NeurIPS 2024] MeshXL: Neural Coordinate Field for Generative 3D Foundation Models, a 3D fundamental model for mesh generation
CVMI-Lab / Point UV Diffusion(ICCV2023) This is the official PyTorch implementation of ICCV2023 paper: Texture Generation on 3D Meshes with Point-UV Diffusion
cvlab-epfl / Voxel2meshVoxel2Mesh: 3D Mesh Model Generation from Volumetric Data
krober10nd / SeismicMesh2D/3D serial and parallel triangular mesh generation tool for finite element methods.
dariopavllo / ConvmeshCode for "Convolutional Generation of Textured 3D Meshes", NeurIPS 2020
kjfu / MeshACA 3D Mesh Generation and Adaptation Package for Multiscale Coupling Simulation for Materials Defects
facebookresearch / VCMeshConvLearning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.
KunalMGupta / NeuralMeshFlowNeural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows
whaohan / PivotmeshOfficial code for paper: PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance [ICLR'25]
theEricMa / TriplaneTurbo[CVPR2025] Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data
