Uni-AdGen uses a unified autoregressive framework with foreground perception, instruction tuning, and coarse-to-fine preference modules to generate personalized image-text ads from noisy user behaviors, outperforming baselines on a new PAd1M dataset.
ControlAR: Controllable image generation with autoregressive models
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VARestorer converts a text-to-image VAR model into a fast one-step real-world image super-resolution model via distribution matching distillation and pyramid image conditioning.
PacTure uses view packing and next-scale autoregressive prediction to generate consistent multi-view PBR textures faster than prior sequential or cross-attention methods.
Generative AI exhibits a paradox of simplicity where complex scene generation succeeds but deterministic tasks like pure color images fail, addressed via a new hierarchical obedience framework and Violin benchmark showing closed-source models outperform open-source ones.
citing papers explorer
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Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
Uni-AdGen uses a unified autoregressive framework with foreground perception, instruction tuning, and coarse-to-fine preference modules to generate personalized image-text ads from noisy user behaviors, outperforming baselines on a new PAd1M dataset.
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VARestorer: One-Step VAR Distillation for Real-World Image Super-Resolution
VARestorer converts a text-to-image VAR model into a fast one-step real-world image super-resolution model via distribution matching distillation and pyramid image conditioning.
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PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
PacTure uses view packing and next-scale autoregressive prediction to generate consistent multi-view PBR textures faster than prior sequential or cross-attention methods.
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Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk?
Generative AI exhibits a paradox of simplicity where complex scene generation succeeds but deterministic tasks like pure color images fail, addressed via a new hierarchical obedience framework and Violin benchmark showing closed-source models outperform open-source ones.