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• ForgeDreamer addresses domain adaptation challenges in industrial text-to-3D generation by employing a Multi-Expert LoRA Ensemble mechanism.method 95%
• The Multi-Expert LoRA Ensemble mechanism consolidates multiple category-specific LoRA models into a unified representation to eliminate knowledge interference.method 95%
• ForgeDreamer addresses geometric reasoning deficiencies in industrial text-to-3D generation using a Cross-View Hypergraph Geometric Enhancement approach.method 95%
• The Cross-View Hypergraph Geometric Enhancement approach captures structural dependencies spanning multiple viewpoints simultaneously.method 95%
• ForgeDreamer demonstrates superior semantic generalization on a custom industrial dataset compared to state-of-the-art approaches.result 90%
• ForgeDreamer demonstrates enhanced geometric fidelity on a custom industrial dataset compared to state-of-the-art approaches.result 90%
• Conventional LoRA fusion in text-to-3D generation causes knowledge interference across categories in industrial applications.limitation 90%
• Pairwise consistency constraints in text-to-3D generation fail to capture higher-order structural dependencies essential for precision manufacturing.limitation 90%

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