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# Visual Representation Alignment

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  1. arXiv

    ViRA aligns planner features to frozen visual foundation models, lifting Rap∗ from 72.8 to 83.7 EPDMS on NAVSIM v2 navtest and yielding a 92.3-EPDMS ViRA-Diffusion

    The authors introduce ViRA, a planner-agnostic visual representation alignment framework that supervises the planner encoder with frozen visual foundation model (VFM) intermediate features during training and discards the alignment modules at inference, leaving planner architecture and inference cost unchanged; across NAVSIM v2 and HUGSIM, ViRA consistently improves driving performance for regression-based, diffusion-based, and scoring-based planners, with gains depending on the VFM target and on whether the planner uses auxiliary perception supervision.

    Zihao Zhang（First author）
    Visual Representation AlignmentEnd-to-End Autonomous DrivingViRA-DiffusionDINOv3Foundation Models
  2. arXiv

    PixelDense splits SAM2, Depth Anything v2 and Metric3D v2 into semantic and geometric alignment streams, lifting PixelGen-XXL's GenEval Overall from 0.7927 to 0.8093

    The work introduces PixelDense, which routes DINOv2 and SAM2 through a semantic projection stream and Depth Anything v2 and Metric3D v2 through a geometric projection stream, adding a weight-space orthogonality penalty that keeps the two streams in disjoint subspaces; with a single recipe on PixelGen and DeCo it improves GenEval, DPG-Bench and HPS v2.1, raising PixelGen-XXL's GenEval Overall from 0.7927 to 0.8093 and beating every single-teacher and unfactored multi-teacher variant.

    Lehan Yang（First author）
    PixelDenseVisual Representation AlignmentPixel-Space Diffusion TransformerText-to-Image SynthesisImage Editing

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