From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models
Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets. We argue this inherits more of the generative output interface than dense prediction requires: unlike RGB synthesis, dense prediction asks for pixel-correct, t
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “From RGB Generation to Dense Field Readout: Pixel-Space Dens” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Zanyi Wang →
“From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models”
- LinkedLinked via arxiv author · 85%Xin Lin →
“From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models”
- LinkedLinked via arxiv author · 85%Haodong Li →
“From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models”
- LinkedLinked via arxiv author · 85%Dengyang Jiang →
“From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models”
- LinkedLinked via arxiv author · 85%Yijiang Li →
“From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models”
- LinkedLinked via arxiv author · 85%Pengtao Xie →
“From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models”
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”