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- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks4,943
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.
- Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data2,160
This work extends the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data and employs a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics.
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- EDVR: Video Restoration With Enhanced Deformable Convolutional Networks1,341
This work proposes a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, and proposes a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration.
- Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation1,235
This work proposes a new T2V generation setting—One-Shot Video Tuning, where only one text-video pair is presented, and introduces Tune-A-Video, which involves a tailored spatio-temporal attention mechanism and an efficient one-shot tuning strategy.
- Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform1,224
It is shown that it is possible to recover textures faithful to semantic classes in a single network conditioned on semantic segmentation probability maps through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation.
- Activating More Pixels in Image Super-Resolution Transformer1,170
A novel Hybrid Attention Transformer (HAT) is proposed, which combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability.
- MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing902
MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency, and proposes a mask-guided mutual self-attention strategy.
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- BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond698
A succinct pipeline is shown that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms and can serve as strong baselines for future VSR approaches.
- Towards Real-World Blind Face Restoration with Generative Facial Prior676
This work proposes GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration that achieves superior performance to prior art on both synthetic and real-world datasets.
- MotionCtrl: A Unified and Flexible Motion Controller for Video Generation673
MotionCtrl is presented, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion and is a relatively generalizable model that can adapt to a wide array of camera poses and trajectories once trained.
- VideoCrafter1: Open Diffusion Models for High-Quality Video Generation633
This model is the first open-source I2V foundation model capable of transforming a given image into a video clip while maintaining content preservation constraints, and can generate realistic and cinematic-quality videos with a resolution of $1024 \times 576.
- FateZero: Fusing Attentions for Zero-shot Text-based Video Editing594
F FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask, is proposed, which is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
- 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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- Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models334
This paper proposes a new framework called Mix-of-Show, which adopts an embedding-decomposed LoRA (ED-LoRA) for single-client tuning and gradient fusion for the center node to preserve the in-domain essence of single concepts and support theoretically limitless concept fusion.
- GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution305
This work shows that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR) and shows clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods.
- From Persona to Personalization: A Survey on Role-Playing Language Agents291
A comprehensive survey of RPLAs, illustrating the evolution and recent progress in RPLAs integrating with cutting-edge LLM technologies and categorizing personas into three types: 1) Demographic Persona, which leverages statistical stereotypes; 2) Character Persona, focused on well-established figures; and 3) Individualized Persona, customized through ongoing user interactions for personalized services.
- DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models241
A novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models, that can transform the editing signals into gradients via feature correspondence loss to modify the intermediate representation of the diffusion model.
- Making LLaMA SEE and Draw with SEED Tokenizer227
SEED is introduced, an elaborate image tokenizer that empowers LLMs with the ability to SEE and Draw at the same time, and with SEED tokens, LLM is able to perform scalable multimodal autoregression under its original training recipe, i.e., next-word prediction.
- VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution216
An automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16, 000 high-fidelity clips of diverse interview scenarios, and shows that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance.
- SmartEdit: Exploring Complex Instruction-Based Image Editing with Multimodal Large Language Models213
A Bidirectional Interaction Module (BIM) is proposed that enables comprehensive bidirectional information interactions between the input image and the MLLM output and demonstrates that a small amount of complex instruction editing data can effectively stimulate SmartEdit’ s editing capabilities for more complex instructions.
- Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models209
This paper makes the first attempt to introduce explicit 3D shape priors into the CLIP-guided 3D optimization process, and proposes to jointly optimize a learnable text prompt and fine-tune the text-to-image diffusion model for rendering-style image generation.
- FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling205
This work proposes FreeNoise, a tuning-free and time-efficient paradigm to enhance the generative capabilities of pretrained video diffusion models while preserving content consistency, and designs a novel motion injection method to support the generation of videos conditioned on multiple text prompts.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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