1 citations · 2 across the 13 of their papers we have counts for
13 papers
Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models
Songping Wang, Yueming Lyu, Shiqi Liu +5
The rise of customized diffusion models has fueled a boom in personalized visual content creation, but it also introduces serious risks of malicious misuse, thereby posing threats…
Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark
Qi Li, Weining Wang, Shuangjun Du +5
Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generative models such as GANs and diffusion models.Despite these advances, exi…
Revisiting MLLM Based Image Quality Assessment: Errors and Remedy
Zhenchen Tang, Songlin Yang, Bo Peng +2
The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch b…
HandEval: Taking the First Step Towards Hand Quality Evaluation in Generated Images
Zichuan Wang, Bo Peng, Songlin Yang +2
Although recent text-to-image (T2I) models have significantly improved the overall visual quality of generated images, they still struggle in the generation of accurate details in…
DREAM: A Benchmark Study for Deepfake photoREalism AssessMent
Bo Peng, Zichuan Wang, Sheng Yu +3
Deep learning based face-swap videos, widely known as deepfakes, have drawn wide attention due to their threat to information credibility. Recent works mainly focus on the problem…
Concept Corrector: Erase concepts on the fly for text-to-image diffusion models
Zheling Meng, Bo Peng, Xiaochuan Jin +4
Text-to-image diffusion models have demonstrated the underlying risk of generating various unwanted content, such as sexual elements. To address this issue, the task of concept era…
Dark Miner: Defend against undesirable generation for text-to-image diffusion models
Zheling Meng, Bo Peng, Xiaochuan Jin +4
Text-to-image diffusion models have been demonstrated with undesired generation due to unfiltered large-scale training data, such as sexual images and copyrights, necessitating the…
S^3D-NeRF: Single-Shot Speech-Driven Neural Radiance Field for High Fidelity Talking Head Synthesis
Dongze Li, Kang Zhao, Wei Wang +4
Talking head synthesis is a practical technique with wide applications. Current Neural Radiance Field (NeRF) based approaches have shown their superiority on driving one-shot talki…
Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Space
Zheling Meng, Bo Peng, Jing Dong
Watermarking is a tool for actively identifying and attributing the images generated by latent diffusion models. Existing methods face the dilemma of image quality and watermark ro…
RS-Corrector: Correcting the Racial Stereotypes in Latent Diffusion Models
Yue Jiang, Yueming Lyu, Tianxiang Ma +2
Recent text-conditioned image generation models have demonstrated an exceptional capacity to produce diverse and creative imagery with high visual quality. However, when pre-traine…
Learning Dense Correspondence for NeRF-Based Face Reenactment
Songlin Yang, Wei Wang, Yushi Lan +4
Face reenactment is challenging due to the need to establish dense correspondence between various face representations for motion transfer. Recent studies have utilized Neural Radi…
AE-NeRF: Audio Enhanced Neural Radiance Field for Few Shot Talking Head Synthesis
Dongze Li, Kang Zhao, Wei Wang +4
Audio-driven talking head synthesis is a promising topic with wide applications in digital human, film making and virtual reality. Recent NeRF-based approaches have shown superiori…
DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing
Yueming Lyu, Kang Zhao, Bo Peng +5
Text-guided image editing faces significant challenges when considering training and inference flexibility. Much literature collects large amounts of annotated image-text pairs to…