GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation
GAP3D introduces a modular diffusion-based approach that aligns VLM-generated latents directly to the patch-level feature space of a pre-trained image encoder, enabling frozen generative models to use VLMs as prompt encoders while preserving spatial structure. It trains primarily on image-text pairs, avoids large-scale 3D data, and demonstrates zero-shot multimodal capabilities, though it currently prioritizes high-level semantics over fine-grained detail.
Article intelligence
Key points
- GAP3D uses diffusion to align VLM latents to image encoder patch-level features.
- Avoids large-scale 3D data by training on general image-text pairs.
- Shows zero-shot multimodal understanding despite text-only training.
- Prioritizes high-level semantics; fine-grained detail remains a challenge.
Why it matters
This matters because gAP3D uses diffusion to align VLM latents to image encoder patch-level features.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28995] GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation
[Submitted on 27 May 2026]
Title:GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation
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Abstract:Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end training or map features to compressed representations, discarding the dense spatial structure required for geometry-aware tasks like 3D asset generation. To address this, we propose GAP3D, a modular, diffusion-based approach that aligns VLM-generated latents directly to the complete, patch-level feature space of a pre-trained image encoder, enabling a frozen downstream generative model to utilize a VLM as prompt encoder while maintaining a spatially structured conditioning signal. Evaluated on 3D asset generation, our method bypasses the need for large-scale 3D data by training mainly on general-domain image-text pairs. It also exhibits emergent zero-shot capabilities for multimodal prompts, despite being trained exclusively on text input. Finally, while currently prioritizing high-level semantics over fine-grained detail, GAP3D demonstrates that the representation gap between VLM and image-encoder feature spaces can be partially bridged through diffusion-based alignment, taking the first steps towards a modular integration of foundation models through generative alignment to dense embedding spaces.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.28995 [cs.CV]
(or arXiv:2605.28995v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.28995
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Polytimi Anna Gkotsi [view email] [v1] Wed, 27 May 2026 18:53:09 UTC (10,193 KB)
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