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- Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild372
This work introduces SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up, and develops a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration.
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- OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer48
This paper designs a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively, and proposes Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples.
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- Harnessing Diffusion-Yielded Score Priors for Image Restoration45
HYPIR outperforms previous state-of-the-art methods, achieving efficient and high-quality image restoration, and inherits the capabilities of diffusion models with rich user control, enabling text-guided restoration and adjustable texture richness.
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- Interpreting Low-Level Vision Models With Causal Effect Maps20
This work introduces causality theory to interpret low-level vision models and proposes a model-/task-agnostic method called Causal Effect Map (CEM), which can visualize and quantify the input-output relationships on either positive or negative effects.
- SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution5
This work establishes a simple yet effective baseline for cascaded video super-resolution generation, offering practical insights to guide future advancements in efficient cascaded synthesis systems.
- UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models5
UniCon implements a unidirectional flow from the diffusion network to the adapter, allowing the adapter alone to generate the final output, reducing computational demands by eliminating the need for the diffusion model to compute and store gradients during adapter training.
- GET3D--: Learning GET3D from Unconstrained Image Collections2
This work proposes GET3D--, the first method that directly generates textured 3D shapes from 2D images with unknown pose and scale and proposes a novel training schedule to stably optimize both the shape generator and camera sampler in a unified framework.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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