TripoSG — image-to-3D mesh

1.5B rectified-flow image-to-mesh generator. ≈20–30 s per mesh on an RTX 5090, producing dense watertight geometry straight from a single photo.

What this repo is: the TripoSG and TripoSG-scribble pipelines plus the RMBG-1.4 background remover they need — weights only, not a retrain. The samples below were generated with this build, not copied from upstream.

Samples

Single image in, mesh out. Source images were rendered with Z-Image Turbo; the meshes are Blender Workbench clay renders of the raw .glb output — no cleanup, no retopology, no texturing.


_{source — `a cute chunky retro robot toy standing upright, rounded metal body, simple friendly face, studio product photograph on a plain white background`}	_{mesh — 15.2 MB `.glb`, generated in 30 s, seed 42}

_{source — `an ornate ceramic teapot with a curved spout and handle, glazed blue and white porcelain, studio product photograph`}	_{mesh — 37.6 MB `.glb`, generated in 18 s, seed 42}

Textured (`img2mesh_textured`)

The same robot run through the full chain — TripoSG shape, then the paint stage — instead of shape alone.


_{`img2mesh` — 15.2 MB, 845k faces, 18 s}	_{`img2mesh_textured` — 27.6 MB with a baked base-colour texture, +24 min for the paint stage}

Turntable

Four views of the same mesh, 90° apart — the back is inferred, not copied.

The chest panel, hand hooks and boot flare survive on the front; the unseen back is plausibly closed rather than hollow or spiked. Fine surface text and sub-millimetre detail do not survive — this is a shape model.

Recommended settings

Parameter	Production value	Meaning
`seed`	any	Deterministic per seed
input	one RGB(A) image	Background is removed automatically via RMBG-1.4
output	`.glb`	Watertight triangle mesh, no texture

Supported modes: img2mesh, img2mesh_textured (texturing is a separate paint stage — see gotchas)

Notes and gotchas

Give it one clean, centred subject on a plain background. The pipeline runs RMBG-1.4 first; a busy background costs you geometry, not just time.
Mesh sizes vary hugely with subject complexity — 15 MB for the robot, 38 MB for the teapot at identical settings. Budget disk accordingly if you are generating in bulk.
img2mesh output is untextured; texturing is a separate paint stage. img2mesh_textured chains a paint model after the shape model and does produce a correctly textured mesh — but budget for it: on an 845k-face mesh the paint stage took ≈24 minutes (versus 18 s for the shape stage), and nearly all of that is a pure-Python UV hole-filling loop rather than GPU work. Plan the textured path as a batch job, not an interactive one.
Orientation is consistent: generated meshes come out facing +Y with the source image's front as the front, so a fixed camera works across batches.
≈10 GB VRAM resident at default settings.

Files

Path	Size	Role
`split/TripoSG/transformer/`	5.76 GB	main rectified-flow transformer
`split/TripoSG/vae/`	971 MB	shape VAE
`split/TripoSG/image_encoder_dinov2/`	1.22 GB	DINOv2 image encoder
`split/TripoSG-scribble/`	4.7 GB	scribble-conditioned variant (transformer + VAE + CLIP text encoder)
`split/RMBG-1.4/`	176 MB	background remover (run before meshing)

Provenance

Upstream base model: VAST-AI/TripoSG (MIT)
This build: redistributed as a split component tree alongside the scribble variant and RMBG-1.4. Weights are not retrained here.
License: MIT, per upstream.

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_{source — `a cute chunky retro robot toy standing upright, rounded metal body, simple friendly face, studio product photograph on a plain white background`}	_{mesh — 15.2 MB `.glb`, generated in 30 s, seed 42}

_{source — `an ornate ceramic teapot with a curved spout and handle, glazed blue and white porcelain, studio product photograph`}	_{mesh — 37.6 MB `.glb`, generated in 18 s, seed 42}