ForgeDreamer: Industrial Text-to-3D Generation with Multi-Expert LoRA and Cross-View Hypergraph

2026-03-10

AI Summary

Existing text-to-3D generation methods are limited in industrial settings by cross-category semantic interference and inadequate modeling of high-order geometric structures. To address these challenges, this work proposes a multi-expert LoRA ensemble mechanism to disentangle semantic knowledge and suppress interference, alongside a cross-view hypergraph modeling framework to capture high-order geometric dependencies among multiple viewpoints. Through joint semantic-geometric optimization, the proposed approach significantly outperforms current state-of-the-art methods on a newly curated industrial dataset, achieving manufacturing-grade geometric consistency while preserving strong semantic generalization capabilities.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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 dependencies spanning multiple viewpoints simultaneously. These components work synergistically improved semantic understanding, enables more effective geometric reasoning, while hypergraph modeling ensures manufacturing-level consistency. Extensive experiments on a custom industrial dataset demonstrate superior semantic generalization and enhanced geometric fidelity compared to state-of-the-art approaches. Our code and data are provided in the supplementary material attached in the appendix for review purposes.

Problem

• text-to-3D generation
• domain adaptation
• geometric reasoning
• knowledge interference
• structural dependencies

Innovation

• Multi-Expert LoRA
• Cross-View Hypergraph
• Text-to-3D Generation
• Geometric Reasoning
• Industrial 3D Modeling