# TencentARC/InstantMesh

InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

Repository: https://github.com/TencentARC/InstantMesh
Canonical: https://ross.abutalabs.com/products/instantmesh
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-01-03T09:09:13+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 62
- inputs: {"age_days": 875, "days_push": 607, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4509, forks 501 (observed 2026-08-28T04:08:52.567588+00:00)

## What it is
InstantMesh is a feed-forward framework for generating 3D meshes from a single image using sparse-view large reconstruction models (LRM/Instant3D architecture). It provides inference and training code, model weights, and a Gradio demo.

## Use cases
- generate a 3d mesh from a single photo
- image to 3d model conversion
- create 3d assets for games from images
- reconstruct 3d geometry from one view
- fine-tune multi-view diffusion models for 3d generation
- run a local image-to-3d demo

## When to choose
- you need fast feed-forward single-image to 3D mesh generation
- you want pretrained model weights and a ready Gradio demo
- you want to fine-tune Zero123++ or train sparse-view reconstruction models

## When to avoid
- you need high-fidelity textured 3D scans from real multi-view captures
- you have no GPU available
- you need parametric CAD modeling rather than mesh generation

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, graphics, llm-inference
- domain: artificial-intelligence, computer-vision, graphics, machine-learning, deep-learning
- platform: python
- tags: 3d-reconstruction, image-to-3d, mesh-generation, lrm, multi-view-diffusion, gradio-demo, pytorch, gpu, linux, docker

## Member repositories
- TencentARC/InstantMesh (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:52.567588+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:20:21.307284+00:00, confidence not recorded.
  - readme: https://github.com/TencentARC/InstantMesh (fetched 2026-08-28T04:08:52.567588+00:00, sha bb44654d7ca5)
- Data as of 2026-08-30T08:39:29.467469+00:00.