1 citations · 2 across the 5 of their papers we have counts for
5 papers
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
Yu Xu, Fan Tang, Juan Cao +5
Personalized generation paradigms empower designers to customize visual intellectual properties with the help of textual descriptions by tuning or adapting pre-trained text-to-imag…
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
You Wu, Kean Liu, Xiaoyue Mi +3
Concept personalization methods enable large text-to-image models to learn specific subjects (e.g., objects/poses/3D models) and synthesize renditions in new contexts. Given that t…
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
Ziyao Huang, Fan Tang, Yong Zhang +4
Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, w…
Music Style Transfer with Time-Varying Inversion of Diffusion Models
Sifei Li, Yuxin Zhang, Fan Tang +3
With the development of diffusion models, text-guided image style transfer has demonstrated high-quality controllable synthesis results. However, the utilization of text for divers…
Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow
Haipeng Fang, Zhihao Sun, Ziyao Huang +3
The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities. However, current methods still exhibit deficienc…