Problem → Solution Team Why us Roadmap Implementation Demo video AI assistant API access Launch Studio
Live demo · polymesh.ai

From a brief and one image to a game-ready 3D asset.

Polymesh compresses the modeling → retopology → UV → texturing pipeline into a single browser session. This page is the pitch: the problem, the team, the plan, a working prototype — and an API you can call right now.

Try the prototype Watch the demo
01 · Why this exists

Problem → Solution

Every studio we talked to drowns in the same bottleneck: concept art and blockouts pile up faster than 3D artists can turn them into clean, textured, engine-ready meshes.

Problem The asset bottleneck

  • Weeks per hero asset. Modeling, retopology, UV unwrapping and PBR texturing are four separate crafts — and four separate queue positions.
  • Artists blocked by iteration. When design changes a silhouette overnight, days of topology work get thrown away.
  • Outsourcing is slow and leaky. Vendors cost per revision, turn around in weeks, and require sending unreleased concepts outside the studio.
  • Off-the-shelf AI meshes don't ship. Raw diffusion outputs are dense soup — millions of tris, broken UVs, no poly budget — unusable in a game engine.

Solution Polymesh

  • Text or a single image in → textured GLB/OBJ out in one browser tab, with a poly-count target you control.
  • Automatic retopology + UV + bake — outputs are game-ready by construction, not raw sculpt dumps.
  • Minutes, not weeks. A full 3D run on our RTX 5090 worker lands in ~5–15 minutes, streaming stage-by-stage progress.
  • Iteration is free. Re-roll a variant, edit the concept by prompt, or feed four orthographic views — same session, same library.
  • Private by default. Per-account asset storage; nothing shared, nothing leaves the tenant.
02 · Who

The team

Small and senior — the entire stack, from diffusion pipelines to the edge serving this page, was designed and shipped by this team. (Names and profile links below are placeholders — swap in the real ones.)

P
Founder · Product
Product leadreplace with name
Game art pipelinesUXGo-to-market
ML
ML / Diffusion Engineer
Generations leadreplace with name
ComfyUITrellis2Qwen-Image-EditLoRA trainingCUDA
BE
Backend / Infra Engineer
Platform leadreplace with name
Python / FastAPICloudflareR2GPU orchestration
FE
Frontend / 3D Engineer
Interface leadreplace with name
WebGL / Three.jsDesign systemsGLB tooling

Stack in use across the product:
ComfyUI · Trellis2 · Qwen-Image-Edit · FLUX.2 klein · Python stdlib APIs · SQLite · Cloudflare (Tunnel, DNS, R2) · Caddy · Inter/JetBrains Mono design system

03 · Conviction

Why this team can solve it

1 We already shipped the hard part

This is not a deck-ware project: the full pipeline — six-view diffusion, shape generation, texturing, retopology — runs live on this domain today and produces the GLBs in your Asset Library. What you click here is the prototype.

2 Diffusion research fluency, not API wrapping

We build custom ComfyUI node chains, wire LoRA distillation (Lightning 4-step) and multi-angle conditioning ourselves, and swap model families mid-pipeline without touching the product layer. That's what keeps quality moving every week.

3 Full-stack GPU ownership

Dedicated RTX 5090 worker, hardened private networking, job queue with real status, automatic recovery. We control cost and latency end-to-end — no per-call reseller margins baked into the price.

4 We speak game-art, not just AI

Outputs are defined by what engines accept: poly budgets, UV sets, PBR maps, GLB/OBJ delivery. The product decisions in the studio (view controls, white-mesh preview, V1/V2 variants) come directly from art-pipeline experience.

04 · Trajectory

Roadmap

Four gates. The first four are behind us and verifiable in the product; the fifth is where the next months go.

Summer ’26

Idea done

Validate the bottleneck with studios: concept→mesh turnaround is the top cost of pre-production art. Choose the wedge: single-image to game-ready textured asset.

Aug ’26

Prototype done

End-to-end chain on one GPU: background removal → six-view synthesis (Qwen-Image-Edit + multi-angles LoRA) → Trellis2 shape+texture → decimate/UV → GLB out. Proved quality bar on real inputs.

Sep ’26

MVP done

Studio product: four generation modes, accounts with credit metering, private Asset Library (Cloudflare R2), API endpoints, admin panel. First external users generating meshes.

Oct ’26

Launched — public beta live now

polymesh.ai on Cloudflare edge with a dedicated RTX 5090 worker, self-healing tunnel + watchdog. Billing runs in test mode; free Spark credits for everyone who signs up.

05 · How it's built

Implementation plan

Five stages, each already standing in production — this is the architecture the demo runs on, stage by stage.

Stage A · AI generation core

Diffusion pipelines in ComfyUI

Text→Image and prompt/reference Image Edit run FLUX.2 klein 9B. For 3D: RMBG cutout → six-view synthesis with Qwen-Image-Edit 2511 (multi-angles LoRA, Lightning 4-step distillation) → Trellis2 4B structured-latent shape + texture stages → diffusion paint passes.

ComfyUI APITrellis2-4BQwen-Image-EditFLUX.2 kleincustom LoRA
Stage B · GPU worker layer

Dedicated RTX 5090 inference box

Supervised services: the ComfyUI engine, an api-wrapper with a job queue (submit → poll → base64/URL outputs), and a read-only output server for the large 3D artifacts. Everything binds to loopback; the box is unreachable from the public internet except through the tunnel.

RTX 5090 32GBsupervisordjob queueSSH tunnel mesh
Stage C · Platform API

Accounts, credits, assets

A hardened edge API owns sessions (hashed, expiring tokens), the credit ledger, per-account private asset storage (images and GLBs archived to R2), generation cost rules, history and an admin panel with usage events.

Python APISQLiteBearer sessionsAPI keysR2 archive
Stage D · Edge & delivery

polymesh.ai serving

Cloudflare Tunnel publishes the site over HTTPS with no open ports; Caddy routes statically and reverse-proxies the API, the image engine and the 3D output server under one domain. A watchdog revives every component every minute — the stack self-heals.

Cloudflare TunnelCaddyR2watchdog cron
Stage E · Product surface

The studio SPA

Four modes (Text→Image, Image Edit, 3D Single View, 3D Multi View), live stage progress, six-view + white-mesh + textured-V1/V2 previews in a WebGL viewport, a recoverable asset library (interrupted generations are rebuilt from server-side output listings), profile, billing UI.

Vanilla ES modulesThree.js viewportdesign system
Stage F · AI-based solutions in flight

What the models do for us

Image-conditional mesh diffusion (SLAT), multi-view consistency with pose LoRAs, texture-space diffusion painting, learned background removal, automatic retopology to a target budget, prompt-aware T-pose reshaping for character inputs.

SLAT diffusionmulti-view consistencyauto-retopoUV bake
06.1 · In motion

Demo video

Three captures, one under another — full Studio walkthrough, pipeline run, latest demo. Each plays on its own; nothing to switch.

The reel drops here

Waiting for the final capture — it will play in this box. Nothing to install, nothing to refresh twice.

06.2 · Manage the reel

Description & upload

Videos are added manually from this page — no server access needed. Upload an MP4/WebM/MOV with a title and description; it lands at the top of the playlist above and the older clips stay switchable. Up to 400 MB per file (the public CDN edge passes roughly ≤ 100 MB — compress anything longer first). Requires being signed in.

Stored privately for this site under /media/; the last three uploads are kept for rollback. Not signed in? Sign in first — the uploader checks the session token.

06.3 · Hands on

Working prototype

The studio is the prototype — and it's the production app.

Four modes, live worker, private library: polymesh.ai/studio.html. New accounts start with 200 free Spark credits, no card.

Open Studio →
Bonus · Instant answers

AI assistant

Ask the assistant anything about the product — formats, timings, pricing, the API, what runs under the hood. It answers from Polymesh's live knowledge base, with example questions preloaded below the input.

Try questions like:

  • “How long does an image-to-3D run take?”
  • “Which export formats are game-ready?”
  • “Is there an API and how do I authenticate?”
  • “What models power the 3D pipeline?”
Runs client-side against a curated product knowledge base; an LLM endpoint can be attached via window.POLY_CHAT_LLM without UI changes.
Polymesh Assistantonline
Bonus · For developers

API access

Everything the studio does is a public HTTP call against https://polymesh.ai/api — queue a workflow, poll the job, fetch the result. Personal API keys are minted in Studio → Profile; sessions authenticate with a bearer token.

Endpoints

RoutePurpose
POST/api/auth/register · /loginaccount + bearer session
POST/api/generatequeue a ComfyUI API-format workflow → {id}
GET/api/result/{id}job status + outputs (base64 / urls)
POST/api/upload/imagemultipart image upload → {name}
GET/api/output-list/{folder}/discover 3D artifacts as they land
GET/api/output/{path}download GLB / OBJ / PNG files
GET/api/state · /api/assetscredits, history, private library (CRUD)
GET/api/healthstack + worker status, no auth

Quickstart — three calls

# 1 · session token
curl -X POST https://polymesh.ai/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username":"[email protected]","password":"…"}'
# → {"token":"…","user":{…}}

# 2 · queue: ComfyUI api-format workflow
curl -X POST https://polymesh.ai/api/generate \
  -H "Content-Type: application/json" \
  -d '{"input":{"workflow_json":{…},"return_outputs_as_base64":true}}'
# → {"id":"a91f…"}   (queued → running → completed)

# 3 · collect the result
curl https://polymesh.ai/api/result/a91f…
# → {"status":"completed",
#    "output":[{"type":"base64","mimetype":"image/png",…}]}
probing from your browser…

Keys & limits

Bearer session tokens authenticate the account layer. Long-lived pmk_ API keys (Studio → Profile) are designed for server-to-server use and the decimate service contract. Job queue depth is limited per worker; heavy 3D jobs are serialized for VRAM safety.