8 citations · 40 across the 27 of their papers we have counts for
28 papers
Object-Uni: A Unified Model for Object-Centric Spatial Understanding and Controllable Generation
Mining Tan, Yinuo Wang, Ziqi Zhou +6
Unified models for visual understanding and generation have made rapid progress, yet they still lack the ability to understand and manipulate the spatial states of object instances…
COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models
Ziqi Zhou, Weize Quan, Mining Tan +6
Composition is a high-level visual intent that governs where subjects are placed and how a scene is organized, yet current unified multimodal models remain unreliable at fine-grain…
Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration
Yingying Deng, Xiangyu He, Fan Tang +2
Text-guided image generation has advanced rapidly with large-scale diffusion models, yet achieving precise stylization with visual exemplars remains difficult. Existing approaches…
Inversion-Free Style Transfer with Dual Rectified Flows
Yingying Deng, Xiangyu He, Fan Tang +2
Style transfer, a pivotal task in image processing, synthesizes visually compelling images by seamlessly blending realistic content with artistic styles, enabling applications in p…
Multi-turn Consistent Image Editing
Zijun Zhou, Yingying Deng, Xiangyu He +2
Many real-world applications, such as interactive photo retouching, artistic content creation, and product design, require flexible and iterative image editing. However, existing i…
Z-Magic: Zero-shot Multiple Attributes Guided Image Creator
Yingying Deng, Xiangyu He, Fan Tang +1
The customization of multiple attributes has gained popularity with the rising demand for personalized content creation. Despite promising empirical results, the contextual coheren…
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
Yuxin Zhang, Minyan Luo, Weiming Dong +6
The stories and characters that captivate us as we grow up shape unique fantasy worlds, with images serving as the primary medium for visually experiencing these realms. Personaliz…
VidMusician: Video-to-Music Generation with Semantic-Rhythmic Alignment via Hierarchical Visual Features
Sifei Li, Binxin Yang, Chunji Yin +4
Video-to-music generation presents significant potential in video production, requiring the generated music to be both semantically and rhythmically aligned with the video. Achievi…
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution
Yingying Deng, Xiangyu He, Fan Tang +1
Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. Conventional methods employ style loss, derived from second-…
LumiSculpt: Enabling Consistent Portrait Lighting in Video Generation
Yuxin Zhang, Dandan Zheng, Biao Gong +5
Lighting plays a pivotal role in ensuring the naturalness and aesthetic quality of video generation. However, the impact of lighting is deeply coupled with other factors of videos,…
A Comprehensive Review of Few-shot Action Recognition
Yuyang Wanyan, Xiaoshan Yang, Weiming Dong +1
Few-shot action recognition aims to address the high cost and impracticality of manually labeling complex and variable video data in action recognition. It requires accurately clas…
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
Yunbing Jia, Xiaoyu Kong, Fan Tang +3
In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition. Through empirical…
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…
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…
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…
Dance-to-Music Generation with Encoder-based Textual Inversion
Sifei Li, Weiming Dong, Yuxin Zhang +5
The seamless integration of music with dance movements is essential for communicating the artistic intent of a dance piece. This alignment also significantly improves the immersive…
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…
Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning
Chengcheng Ma, Ismail Elezi, Jiankang Deng +2
We address the challenging problem of Long-Tailed Semi-Supervised Learning (LTSSL) where labeled data exhibit imbalanced class distribution and unlabeled data follow an unknown dis…
: Zero-shot Style Transfer via Attention Rearrangement
Yingying Deng, Xiangyu He, Fan Tang +1
Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging. In contrast to existing learning/tuning m…
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…
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Yuxin Zhang, Weiming Dong, Fan Tang +6
Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textu…
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, which can fit in most existing arbitrary image style…
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…