24 citations · 31 across the 6 of their papers we have counts for
7 papers
Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
Yuqi Tang, Yang Shi, Zhuoran Zhang +21
Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural dist…
InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer Framework
Jiale Tao, Yanbing Zhang, Qixun Wang +9
Current learning-based subject customization approaches, predominantly relying on U-Net architectures, suffer from limited generalization ability and compromised image quality. Mea…
InstantIR: Blind Image Restoration with Instant Generative Reference
Jen-Yuan Huang, Haofan Wang, Qixun Wang +4
Handling test-time unknown degradation is the major challenge in Blind Image Restoration (BIR), necessitating high model generalization. An effective strategy is to incorporate pri…
CSGO: Content-Style Composition in Text-to-Image Generation
Peng Xing, Haofan Wang, Yanpeng Sun +5
The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on tra…
InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
Haofan Wang, Peng Xing, Renyuan Huang +3
Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another. Although diffusion models…
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Haofan Wang, Matteo Spinelli, Qixun Wang +3
Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization. However, despite this notable progress, current…
InstantID: Zero-shot Identity-Preserving Generation in Seconds
Qixun Wang, Xu Bai, Haofan Wang +5
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by…