Aligning Text, Images, and 3D Structure Token-by-Token

Aadarsh Sahoo, Vansh Tibrewal, Georgia Gkioxari

Aligning Text, Images, and 3D Structure Token-by-Token: 21 upvotes on Hugging Face Daily Papers, #8 of 21 papers on 2025-06-11. Day-by-day upvote history.

Creating machines capable of understanding the world in 3D is essential in assisting designers that build and edit 3D environments and robots navigating and interacting within a three-dimensional space. Inspired by advances in language and image modeling, we investigate the potential of autoregressive models for a new modality: structured 3D scenes. To this end, we propose a unified LLM framework that aligns language, images, and 3D scenes and provide a detailed ''cookbook'' outlining critical design choices for achieving optimal training and performance addressing key questions related to data representation, modality-specific objectives, and more. We evaluate performance across four core 3D tasks -- rendering, recognition, instruction-following, and question-answering -- and four 3D datasets, synthetic and real-world. We extend our approach to reconstruct complex 3D object shapes by enriching our 3D modality with quantized shape encodings, and show our model's effectiveness on real-world 3D object recognition tasks. Project webpage: https://glab-caltech.github.io/kyvo/

Paper page on Hugging Face · arXiv

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