Arbor

Overview

Arbor is Stability AI's answer (with the University of Tübingen) to a well-known limit of generative 3D: text-to-3D models produce convincing objects but offer no control over the space an object should occupy or avoid. Released in late June 2026 as an open-source release, Arbor introduces constraint meshes as a native control interface: "hull" regions where geometry must exist, avoidance regions that must stay empty, and touch regions the object must contact. Concretely: a chair that fits a given seating envelope, a mechanical part exposing a precise contact surface, a game prop leaving the clearance a player needs. These are spatial intents known before generation starts — intents neither a prompt nor a reference image can carry. Technically, Arbor is a trainable attachment grafted onto TRELLIS.2 (whose generator stays frozen), converting constraint meshes into tokens via the O-Voxel encoders. In a user study (27 participants, 404 trials), Arbor variants won 59.2% of pairwise choices against competing methods. The release is inference-only: public pipeline, model loading, mesh export and a Blender add-on, under the Stability AI Community License. It requires Linux and an NVIDIA GPU. It's still more research tool than product, but it prefigures the next step in generative 3D: generation under geometric specification.

Skill profile

Who is it for

Ideal use cases

• 3D assets under spatial constraints (envelopes, clearances)
• Product and furniture design with imposed contact surfaces
• Level design: objects that respect the play space

Access & availability

Key specifications

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