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New Arbor method enables explicit geometric control in 3D asset generation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Arbor, a new method for controllable 3D asset generation that allows users to specify explicit geometric constraints. Unlike previous methods that relied on text prompts or image views, Arbor uses constraint meshes to define regions for occupancy, avoidance, and contact. This trainable attachment integrates with existing text-conditioned latent 3D generators, converting constraint meshes into tokens that are routed to relevant parts of the denoiser. Arbor has demonstrated improved constraint adherence while maintaining object quality and variation. AI

IMPACT This development offers more precise control over 3D asset creation, potentially streamlining workflows in game development, animation, and design.

RANK_REASON The cluster contains a research paper detailing a new method for 3D asset generation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Arbor method enables explicit geometric control in 3D asset generation

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The cluster contains a research paper detailing a new method for 3D asset generation.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation

    Arbor enables explicit 3D spatial control in text-conditioned latent generation through constraint meshes that define occupancy, avoidance, and contact regions, maintaining object quality while improving constraint adherence.

  2. arXiv cs.CV TIER_1 English(EN) · Mark Boss ·

    Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation

    Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy or avoid. In authoring, this spatial intent is often known before generation starts. A chair should fit a seating envelope, a pro…

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