TL;DR
ForgeDreamer introduces a novel industrial text-to-3D generation framework that combines multi-expert LoRA ensembles with cross-view hypergraph modeling to improve semantic understanding and geometric accuracy.
Contribution
It presents a multi-expert LoRA ensemble and a cross-view hypergraph approach to address domain adaptation and geometric reasoning in industrial text-to-3D generation.
Findings
Outperforms state-of-the-art methods on industrial datasets.
Achieves better semantic generalization across categories.
Enhances geometric fidelity and structural consistency.
Abstract
Current text-to-3D generation methods excel in natural scenes but struggle with industrial applications due to two critical limitations: domain adaptation challenges where conventional LoRA fusion causes knowledge interference across categories, and geometric reasoning deficiencies where pairwise consistency constraints fail to capture higher-order structural dependencies essential for precision manufacturing. We propose a novel framework named ForgeDreamer addressing both challenges through two key innovations. First, we introduce a Multi-Expert LoRA Ensemble mechanism that consolidates multiple category-specific LoRA models into a unified representation, achieving superior cross-category generalization while eliminating knowledge interference. Second, building on enhanced semantic understanding, we develop a Cross-View Hypergraph Geometric Enhancement approach that captures structural…
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