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This paper proposes VLM3, a method that adapts vision language models for 3D understanding tasks through simple architectural modifications and text-based training, achieving competitive performance without complex designs. It demonstrates significant improvements in depth estimation accuracy and enables diverse 3D tasks like pixel correspondence, camera pose estimation, and object-level understanding.
EVA01 is a unified framework that integrates 3D mesh as a native modality into multimodal language models via a Mixture-of-Transformers architecture, enabling state-of-the-art text-to-3D generation and long-context multi-turn geometric editing.