7 citations · 26 across the 16 of their papers we have counts for
14 papers · 1 filter
Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion
Henglin Liu, Fangyuan Kong, Jing Wang +7
Recent advances in preference alignment for diffusion-based video generation, particularly via Direct Preference Optimization (DPO), have significantly improved visual quality. How…
MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer
Nisha Huang, Henglin Liu, Yizhou Lin +5
Recent diffusion-based methods for material transfer rely on image fine-tuning or complex architectures with assistive networks, but face challenges including text dependency, extr…
Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment
Henglin Liu, Nisha Huang, Chang Liu +6
The aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC. However, its inherently complex nature, spanning visual per…
Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning
Chubin Chen, Sujie Hu, Jiashu Zhu +8
Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, whil…
Stochastic Self-Guidance for Training-Free Enhancement of Diffusion Models
Chubin Chen, Jiashu Zhu, Xiaokun Feng +7
Classifier-free Guidance (CFG) is a widely used technique in modern diffusion models for enhancing sample quality and prompt adherence. However, through an empirical analysis on Ga…
ArtCrafter: Text-Image Aligning Style Transfer via Embedding Reframing
Nisha Huang, Kaer Huang, Yifan Pu +5
Recent years have witnessed significant advancements in text-guided style transfer, primarily attributed to innovations in diffusion models. These models excel in conditional guida…
Taming Rectified Flow for Inversion and Editing
Jiangshan Wang, Junfu Pu, Zhongang Qi +6
Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust genera…
MotionCrafter: One-Shot Motion Customization of Diffusion Models
Yuxin Zhang, Fan Tang, Nisha Huang +4
The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements. While text-to-video generative diffusion models have recent…
CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion
Nisha Huang, Weiming Dong, Yuxin Zhang +6
Although remarkable progress has been made in image style transfer, style is just one of the components of artistic paintings. Directly transferring extracted style features to nat…
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Nisha Huang, Yuxin Zhang, Weiming Dong
Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and t…
Region-Aware Diffusion for Zero-shot Text-driven Image Editing
Nisha Huang, Fan Tang, Weiming Dong +2
Image manipulation under the guidance of textual descriptions has recently received a broad range of attention. In this study, we focus on the regional editing of images with the g…
Inversion-Based Style Transfer with Diffusion Models
Yuxin Zhang, Nisha Huang, Fan Tang +4
The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes includi…
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Nisha Huang, Yuxin Zhang, Fan Tang +5
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a st…
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Nisha Huang, Fan Tang, Weiming Dong +1
Digital art synthesis is receiving increasing attention in the multimedia community because of engaging the public with art effectively. Current digital art synthesis methods usual…