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MeshAnything

[ICCV 2025] From anything to mesh like human artists. Official impl. of "MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization" observed · 2026-08-28

github.com/buaacyw/MeshAnythingV2 · homepage · Python · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

31/100

  • Activity 18
  • Release rhythm 35
  • Longevity 54

Flags: no_releases no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 758
  • days_rel: n/a
  • days_push: 492
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1015 stars · 73 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MeshAnything is an autoregressive transformer model that generates artist-created 3D meshes (up to 1600 faces in V2) aligned with a given shape, such as a point cloud or 3D asset. It integrates into 3D asset production pipelines to produce compact, human-artist-like meshes instead of dense reconstructions.

Use cases

  • convert a point cloud or 3D shape into a clean artist-style mesh
  • generate low-poly game-ready meshes from 3D assets
  • post-process 3D generation pipeline outputs into editable meshes
  • research autoregressive mesh tokenization
  • create controllable 3D meshes aligned to a reference shape

When to choose

  • you need compact, artist-like meshes rather than dense triangle soups
  • you have a GPU (A100/A6000-class) and want state-of-the-art mesh generation
  • you want to integrate mesh generation into an existing 3D asset pipeline

When to avoid

  • you need meshes with more than ~1600 faces
  • you lack a CUDA GPU with significant VRAM
  • you need permissively licensed code for commercial use (SLab license)
  • you need production-ready textured assets, not geometry only

Facets

library · maturity active

machine-learning deep-learning llm-inference graphics image-processing deep-learning large-language-models graphics computer-vision artificial-intelligence python cli mesh-generation autoregressive-transformer 3d-assets artist-created-mesh iclr2025 point-cloud-conditioned non-commercial-license linux gpu

2 sources

Member repositories

RepositoryRoleHealth v2
buaacyw/MeshAnythingV2main31
buaacyw/MeshAnythingmirror32

For agents

markdown · JSON · MCP: product_card(name="buaacyw/MeshAnythingV2")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem