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Found 14 papers, 12 papers with code
Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models
To provide defense against both tuning and editing methods, we propose the semantic disturbance loss (SDL) to disrupt the semantic information of protected images.
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
Video inpainting, which aims to restore corrupted video content, has experienced substantial progress.
Image Conductor: Precision Control for Interactive Video Synthesis
To this end, we propose Image Conductor, a method for precise control of camera transitions and object movements to generate video assets from a single image.
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.
StyleAdapter: A Unified Stylized Image Generation Model
In this work, we propose StyleAdapter, a unified stylized image generation model capable of producing a variety of stylized images that match both the content of a given prompt and the style of reference images, without the need for per-style fine-tuning.
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
After detecting the artifact regions, we develop a finetune procedure to improve GAN-based SR models with a few samples, so that they can deal with similar types of artifacts in more unseen real data.
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly.
Mitigating Artifacts in Real-World Video Super-Resolution Models
To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated.
Rethinking Alignment in Video Super-Resolution Transformers
In this paper, we rethink the role of alignment in VSR Transformers and make several counter-intuitive observations.
Ranked #6 on
Video Super-Resolution
on Vid4 - 4x upscaling
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks.
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Enhanced Quadratic Video Interpolation
In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model.
