9 papers
Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training?
Lingchen Sun, Rongyuan Wu, Zhengqiang Zhang +4
Recent works such as REPA have shown that guiding diffusion models with external semantic features (e.g., DINO) can significantly accelerate the training of diffusion transformers…
VOSR: A Vision-Only Generative Model for Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang +4
Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale text-image data. While effecti…
GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
Qiaosi Yi, Shuai Li, Rongyuan Wu +3
Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step ge…
Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement
Xinyue Liang, Zhinyuan Ma, Lingchen Sun +2
Although recent 3D-native generators have made great progress in synthesizing reliable geometry, they still fall short in achieving realistic appearances. A key obstacle lies in th…
AlignCVC: Aligning Cross-View Consistency for Single-Image-to-3D Generation
Xinyue Liang, Zhiyuan Ma, Lingchen Sun +2
Single-image-to-3D models typically follow a sequential generation and reconstruction workflow. However, intermediate multi-view images synthesized by pre-trained generation models…
One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution
Yujing Sun, Lingchen Sun, Shuaizheng Liu +3
It is a challenging problem to reproduce rich spatial details while maintaining temporal consistency in real-world video super-resolution (Real-VSR), especially when we leverage pr…