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Found 23 papers, 13 papers with code
ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
We open-source our models, data, and code to foster rapid progress in this crucial area of AI-driven cinematic understanding and generation.
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
To bridge this gap, we introduce VBench-2. 0, a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness.
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
We present Vchitect-2. 0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation.
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation
We then present a new I2V model, named MotionStone, developed with the decoupled motion estimator.
Imagine360: Immersive 360 Video Generation from Perspective Anchor
$360^\circ$ videos offer a hyper-immersive experience that allows the viewers to explore a dynamic scene from full 360 degrees.
VEnhancer: Generative Space-Time Enhancement for Video Generation
Given a generated low-quality video, our approach can increase its spatial and temporal resolution simultaneously with arbitrary up-sampling space and time scales through a unified video diffusion model.
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance
Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in over-smoothed content, structural distortions, and repetitive patterns.
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details.
Towards Real-world Video Face Restoration: A New Benchmark
Blind face restoration (BFR) on images has significantly progressed over the last several years, while real-world video face restoration (VFR), which is more challenging for more complex face motions such as moving gaze directions and facial orientations involved, remains unsolved.
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up.
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
However, most resources are still in standard dynamic range (SDR).
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.
Ranked #1 on
Blind Face Restoration
on LFW
DegAE: A New Pretraining Paradigm for Low-Level Vision
However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging.
Structured Sparsity Learning for Efficient Video Super-Resolution
In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.
Residual Local Feature Network for Efficient Super-Resolution
Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
The style modulation aims to generate realistic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor.
Toward Interactive Modulation for Photo-Realistic Image Restoration
The base networks comprise a generator and a discriminator.
Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
In practice, photo retouching can be accomplished by a series of image processing operations.
Efficient Image Super-Resolution Using Pixel Attention
Pixel attention (PA) is similar as channel attention and spatial attention in formulation.
Conditional Sequential Modulation for Efficient Global Image Retouching
The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels.
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods.
Ranked #2 on
Color Image Denoising
on CBSD68 sigma75
