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University of Illinois Urbana-Champaign
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Achieve precise, intuitive 3D control over video generation by moving rigid object meshes, eliminating depth ambiguity and viewpoint inconsistencies.
SAM-V introduces a prompt-fusion mechanism that enriches sparse SAM prompt tokens with view-specific camera tokens and local VGGT features, making the prompt representation both view-aware and spatially grounded, together with a mask decoder that attends to dense 2D and 3D features.
ReToken boosts vision-language model performance by over 20% on key benchmarks while fitting training and inference within a single GPU's memory constraints.
