arXiv Computer Vision
UltraFlash: Accelerating Megapixel Visual Synthesis
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arXiv Computer Vision
Oct 19
PixelWizard: Towards Efficient High-Fidelity Video Generation at Ultra-Large Spatial Resolution
arXiv:2605.25801v2 Announce Type: replace Abstract: High-resolution video generation faces a coupled bottleneck of optimization instability and prohibitive computational costs. The massive expansion...
arXiv AI
Aug 11
When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution
arXiv:2608. 09133v1 Announce Type: cross Abstract: Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging.
arXiv Computer Vision
Sep 22
UltraTex: Unleashing 2K Multi-View Diffusion for 3D Texturing
arXiv:2609.23169v1 Announce Type: new Abstract: High-quality texture generation is essential for creating realistic and production-ready 3D assets. Recent multi-view diffusion methods have shown prom...
arXiv Computer Vision
Sep 1
PixelIR: Fidelity-Perception Decoupling via Pixel-Space Image-Residual Flow Matching for Efficient One-Step Real-World Super-Resolution
arXiv:2608.30782v1 Announce Type: new Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realis...
arXiv Computer Vision
Oct 21
PixelSR: Pixel-Space Orthogonal Flow Decoupling for Real-World Image Super-Resolution
PixelSR introduces a single‑stage Pixel Diffusion Transformer that operates directly in pixel space for real‑world image super‑resolution. It combines Pixel‑Space Conditional Diffusion—integrating low‑resolution conditioning, semantic guidance, and texture refinement—to avoid VAE compression, with Orthogonal Flow Decoupling that separates a low‑rank semantic component from a noise‑free texture component for specialized optimization. The approach yields perceptually realistic details while preserving structural fidelity, as shown by competitive results on multiple benchmarks.
arXiv AI
Jun 30
Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.