Tiled Prompts generates tile-specific text prompts for each latent tile in diffusion super-resolution to reduce errors from global prompts and improve perceptual quality.
arXiv preprint arXiv:2302.02412 , year=
5 Pith papers cite this work. Polarity classification is still indexing.
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A delighting network trained via Dataset Latent Modulation on heterogeneous OLAT and Light Stage data enables high-quality in-the-wild facial reflectance capture from video and produces the NeRSemble-Scan dataset.
Map2World produces scale-consistent 3D worlds from text and arbitrary segment maps via a detail enhancer that incorporates global structure information.
CASR enables stable arbitrary-scale super-resolution by breaking extreme magnifications into cyclic in-distribution transitions with SSAM for structural distribution alignment and SARM for texture self-similarity preservation.
InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.
citing papers explorer
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Tiled Prompts: Overcoming Prompt Misguidance in Image and Video Super-Resolution
Tiled Prompts generates tile-specific text prompts for each latent tile in diffusion super-resolution to reduce errors from global prompts and improve perceptual quality.
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Learning a Delighting Prior for Facial Appearance Capture in the Wild
A delighting network trained via Dataset Latent Modulation on heterogeneous OLAT and Light Stage data enables high-quality in-the-wild facial reflectance capture from video and produces the NeRSemble-Scan dataset.
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Map2World: Segment Map Conditioned Text to 3D World Generation
Map2World produces scale-consistent 3D worlds from text and arbitrary segment maps via a detail enhancer that incorporates global structure information.
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CASR: A Robust Cyclic Framework for Arbitrary Large-Scale Super-Resolution with Distribution Alignment and Self-Similarity Awareness
CASR enables stable arbitrary-scale super-resolution by breaking extreme magnifications into cyclic in-distribution transitions with SSAM for structural distribution alignment and SARM for texture self-similarity preservation.
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InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation
InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.