Search Results for "mesh generation"
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Showing 8 open source projects for "mesh generation"
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Hunyuan3D 2.0
High-Resolution 3D Assets Generation with Large Scale Diffusion Models
...Condition-aligned shape generation via the DiT model, so generated mesh is influenced by input images or prompts. -
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HunyuanWorld 1.0
Generating Immersive, Explorable, and Interactive 3D Worlds
HunyuanWorld-1.0 is an open-source, simulation-capable 3D world generation model developed by Tencent Hunyuan that creates immersive, explorable, and interactive 3D environments from text or image inputs. It combines the strengths of video-based diversity and 3D-based geometric consistency through a novel framework using panoramic world proxies and semantically layered 3D mesh representations. This approach enables 360° immersive experiences, seamless mesh export for graphics pipelines, and disentangled object representations for enhanced interactivity. ... -
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
...It supports both shape generation (mesh geometry) and texture generation modules. Physically Based Rendering texture synthesis to model realistic material effects, including reflections, subsurface scattering, etc. Cross-platform support (MacOS, Windows, Linux) via Python / PyTorch, including diffusers-style APIs. -
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DreamCraft3D
Official implementation of DreamCraft3D
...The repository includes model code, inference scripts, sample prompts, and possibly dataset preparation pipelines. It may integrate rendering or post-processing modules (e.g. mesh smoothing, texturing) to make the outputs more output-ready. Because 3D generation is hardware‐intensive, the repository likely also includes optimizations like quantization, pruning, or inference accelerations (e.g. using FlashMLA or DeepEP) to make the generation pipeline faster or more efficient. DreamCraft3D may also support style or attribute control (e.g. ... -
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SAM 3D Objects
Models for object and human mesh reconstruction
SAM 3D Objects is a foundation model that reconstructs full 3D geometry, texture, and spatial layout of objects and scenes from a single image. Given one RGB image and object masks (for example, from the Segment Anything family), it can generate a textured 3D mesh for each object, including pose and approximate scene layout. The model is specifically designed to be robust in real-world images with clutter, occlusions, small objects, and unusual viewpoints, where many earlier 3D-from-image systems struggle. It supports both single-object and multi-object generation, allowing you to reconstruct entire scenes rather than just isolated items. ... -
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img2obj
Codex plugin that turns attached object images into code-only
img2obj is a Codex plugin that reconstructs an object from a reference image as procedural Three.js code rather than extracting or downloading a mesh. It first checks whether the image is suitable, then creates an ObjectSculptSpec describing geometry, materials, hierarchy, pivots, sockets, and visual priorities. Reconstruction proceeds through blockout, form, look development, and interaction phases so details are added in a controlled order. The plugin supports hard-surface objects, organic... -
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Stable-Dreamfusion
Text-to-3D & Image-to-3D & Mesh Exportation with NeRF + Diffusion
A pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. This project is a work-in-progress and contains lots of differences from the paper. The current generation quality cannot match the results from the original paper, and many prompts still fail badly! Since the Imagen model is not publicly available, we use Stable Diffusion to replace it (implementation from diffusers). Different from Imagen, Stable-Diffusion is a latent diffusion... -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
...Unlike traditional discrete voxel grids or meshes, DeepSDF encodes shapes as continuous neural representations that can be smoothly interpolated and used for reconstruction, generation, and analysis. The repository provides complete tooling for preprocessing mesh datasets (e.g., ShapeNet), training DeepSDF models, reconstructing meshes from learned latent codes, and quantitatively evaluating results with metrics such as Chamfer Distance and Earth Mover’s Distance.
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