Instruct 3D-to-3D
High-fidelity text-guided conversion and editing of 3D scenes using iterative diffusion updates.

Efficient 3D mesh generation from single images using sparse-view large reconstruction models.
InstantMesh represents a significant leap in the feed-forward 3D reconstruction domain, leveraging a dual-stage architecture to transform single 2D images into high-fidelity 3D meshes in under 10 seconds. Built upon a Sparse-view Large Reconstruction Model (LRM), the framework first utilizes a multi-view diffusion model to generate spatially consistent views from a single input. These views are then processed by a transformer-based architecture that predicts a triplane representation for volumetric rendering and subsequent mesh extraction. As of 2026, InstantMesh has become a cornerstone for rapid prototyping in game development and AR/VR workflows due to its superior balance between inference speed and geometric accuracy compared to previous optimization-based methods like DreamFusion. Its architecture is specifically optimized for NVIDIA's Ada Lovelace and Blackwell architectures, ensuring minimal latency when deployed on high-end consumer GPUs or enterprise-grade H100/B200 clusters. The open-source nature of the project allows for deep integration into DCC (Digital Content Creation) tools like Blender and Unreal Engine 5, providing a robust pipeline for procedural asset generation.
Uses a transformer-based Large Reconstruction Model to infer 3D structure from only a handful of generated views.
High-fidelity text-guided conversion and editing of 3D scenes using iterative diffusion updates.
High-Fidelity Shading-Guided 3D Asset Generation from Sparse 2D Inputs
High-Quality Single Image to 3D Generation using 2D and 3D Diffusion Priors
Edit 3D scenes and NeRFs with natural language instructions while maintaining multi-view consistency.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Employs a fine-tuned Stable Diffusion model to ensure generated perspectives of an object are spatially aligned.
Integrates an efficient marching cubes implementation to extract topology from triplane volumes.
Capable of generating basic Physically Based Rendering maps including roughness and metallic properties.
Automatically generates UV coordinates for the extracted mesh based on generated textures.
Weights are distributed in safetensors format for compatibility across different inference engines.
Optimizes 3D generation within the latent space of the diffusion model for faster convergence.
Indie developers often lack the time to model hundreds of secondary characters.
Registry Updated:2/7/2026
Import to Unity
Converting 2D product photos into interactive 3D viewers for websites.
Creating lightweight 3D assets for social media platforms like Snapchat/TikTok.