19 citations · 32 across the 12 of their papers we have counts for
12 papers · 1 filter
Spatial Gram Alignment for Ultra-High-Resolution Image Synthesis
Jinjin Zhang, Xiefan Guo, Di Huang
Modern ultra-high-resolution image synthesis relies heavily on the robust generative capacity of large-scale pre-trained Latent Diffusion Models (LDMs). While recent representation…
What Makes Synthetic Data Effective in Image Segmentation
Jinjin Zhang, Xiefan Guo, Yizhou Jin +2
Driven by rapid advances in large-scale generative models, synthetic data has emerged as a promising solution for visual understanding. While modern diffusion models achieve remark…
EruDiff: Refactoring Knowledge in Diffusion Models for Advanced Text-to-Image Synthesis
Xiefan Guo, Xinzhu Ma, Haoxiang Ma +2
Text-to-image diffusion models have achieved remarkable fidelity in synthesizing images from explicit text prompts, yet exhibit a critical deficiency in processing implicit prompts…
CTCal: Rethinking Text-to-Image Diffusion Models via Cross-Timestep Self-Calibration
Xiefan Guo, Xinzhu Ma, Haiyu Zhang +1
Recent advancements in text-to-image synthesis have been largely propelled by diffusion-based models, yet achieving precise alignment between text prompts and generated images rema…
ShortFT: Diffusion Model Alignment via Shortcut-based Fine-Tuning
Xiefan Guo, Miaomiao Cui, Liefeng Bo +1
Backpropagation-based approaches aim to align diffusion models with reward functions through end-to-end backpropagation of the reward gradient within the denoising chain, offering…
Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation
Jinjin Zhang, Qiuyu Huang, Junjie Liu +2
Ultra-high-resolution image synthesis holds significant potential, yet remains an underexplored challenge due to the absence of standardized benchmarks and computational constraint…
Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models
Jinjin Zhang, Qiuyu Huang, Junjie Liu +2
In this paper, we present Diffusion-4K, a novel framework for direct ultra-high-resolution image synthesis using text-to-image diffusion models. The core advancements include: (1)…
I4VGen: Image as Free Stepping Stone for Text-to-Video Generation
Xiefan Guo, Jinlin Liu, Miaomiao Cui +2
Text-to-video generation has trailed behind text-to-image generation in terms of quality and diversity, primarily due to the inherent complexities of spatio-temporal modeling and t…
Disentangling Foreground and Background Motion for Enhanced Realism in Human Video Generation
Jinlin Liu, Kai Yu, Mengyang Feng +2
Recent advancements in human video synthesis have enabled the generation of high-quality videos through the application of stable diffusion models. However, existing methods predom…
InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization
Xiefan Guo, Jinlin Liu, Miaomiao Cui +3
Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compel…
DreaMoving: A Human Video Generation Framework based on Diffusion Models
Mengyang Feng, Jinlin Liu, Kai Yu +13
In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos. Specifically, given target identity…
Image Inpainting via Conditional Texture and Structure Dual Generation
Xiefan Guo, Hongyu Yang, Di Huang
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors. Due to the lack of proper interaction with image texture du…