DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic and functional structure of parts. While recent part-aware approaches introduce decomposition, they remain largely geometry-focused, lacking semantic grounding and failing to model how parts align with textual descriptions or their inter-part relations. We propose DreamPartGen, a framework for semantically grounded, part-aware text-to-3D generation. DreamPartGen introduces Duplex Part Latents (DPLs) that jointly model each part's geometry and appearance, and Relational Semantic Latents (RSLs) that capture inter-part dependencies derived from language. A synchronized co-denoising process enforces mutual geometric and semantic consistency, enabling coherent, interpretable, and text-aligned 3D synthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity and text-shape alignment.
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Tianjiao Yu
69bd9f663fd3fef6237a6306
Xinzhuo Li
claimed_verified
2026-03-23T09:54:31.816Z
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Xinzhuo Li
LXZ83419
user
LXZ83419
69bd9f663fd3fef6237a6307
Muntasir Wahed
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Jerry Xiong
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Yifan Shen
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Ying Shen
69bd9f663fd3fef6237a630b
Ismini Lourentzou
claimed_verified
2026-04-13T07:38:10.396Z
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Ismini Lourentzou
isminoula
user
isminoula
DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic and functional structure of parts. While recent part-aware approaches introduce decomposition, they remain largely geometry-focused, lacking semantic grounding and failing to model how parts align with textual descriptions or their inter-part relations. We propose DreamPartGen, a framework for semantically grounded, part-aware text-to-3D generation. DreamPartGen introduces Duplex Part Latents (DPLs) that jointly model each part's geometry and appearance, and Relational Semantic Latents (RSLs) that capture inter-part dependencies derived from language. A synchronized co-denoising process enforces mutual geometric and semantic consistency, enabling coherent, interpretable, and text-aligned 3D synthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity and text-shape alignment.
DreamPartGen generates 3D objects by modeling part geometry and appearance through Duplex Part Latents while capturing inter-part relationships via Relational Semantic Latents for improved text-shape alignment.
Duplex Part Latents Relational Semantic Latents co-denoising process text-to-3D generation geometric fidelity text-shape alignment