1 code implementation • 3 Feb 2025 • Yiren Song, Danze Chen, Mike Zheng Shou
Generating cognitive-aligned layered SVGs remains challenging due to existing methods' tendencies toward either oversimplified single-layer outputs or optimization-induced shape redundancies.
no code implementations • 27 Jan 2025 • Hailong Guo, Bohan Zeng, Yiren Song, Wentao Zhang, Chuang Zhang, Jiaming Liu
However, the scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON.
1 code implementation • 19 Dec 2024 • Yiren Song, Xiaokang Liu, Mike Zheng Shou
Diffusion models have fundamentally transformed the field of generative models, making the assessment of similarity between customized model outputs and reference inputs critically important.
no code implementations • 16 Dec 2024 • Yiren Song, Pei Yang, Hai Ci, Mike Zheng Shou
Recently, zero-shot methods like InstantID have revolutionized identity-preserving generation.
1 code implementation • 14 Dec 2024 • Cong Wan, Xiangyang Luo, Hao Luo, Zijian Cai, Yiren Song, Yunlong Zhao, Yifan Bai, Yuhang He, Yihong Gong
Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challenging.
1 code implementation • 8 Dec 2024 • Yiren Song, Shengtao Lou, Xiaokang Liu, Hai Ci, Pei Yang, Jiaming Liu, Mike Zheng Shou
Diffusion models have revolutionized generative modeling with their exceptional ability to produce high-fidelity images.
no code implementations • 7 Oct 2024 • Yepeng Liu, Yiren Song, Hai Ci, Yu Zhang, Haofan Wang, Mike Zheng Shou, Yuheng Bu
Our primary insight involves regenerating the watermarked image starting from a clean Gaussian noise via a controllable diffusion model, utilizing the extracted semantic and spatial features from the watermarked image.
1 code implementation • 19 Jul 2024 • Yuxuan Zhang, Qing Zhang, Yiren Song, Jichao Zhang, Hao Tang, Jiaming Liu
In the second stage, we specifically designed a Hair Extractor and a Latent IdentityNet to transfer the target hairstyle with highly detailed and high-fidelity to the bald image.
no code implementations • 13 Jun 2024 • Pei Yang, Hai Ci, Yiren Song, Mike Zheng Shou
Digital watermarking techniques are crucial for copyright protection and source identification of images, especially in the era of generative AI models.
no code implementations • 12 Jun 2024 • Hai Ci, Yiren Song, Pei Yang, Jinheng Xie, Mike Zheng Shou
Watermarking is crucial for protecting the copyright of AI-generated images.
no code implementations • 10 Jun 2024 • Yiren Song, Shijie Huang, Chen Yao, Xiaojun Ye, Hai Ci, Jiaming Liu, Yuxuan Zhang, Mike Zheng Shou
The painting process of artists is inherently stepwise and varies significantly among different painters and styles.
1 code implementation • 22 Apr 2024 • Hai Ci, Pei Yang, Yiren Song, Mike Zheng Shou
We revisit Tree-Ring Watermarking, a recent diffusion model watermarking method that demonstrates great robustness to various attacks.
no code implementations • 17 Mar 2024 • Yuxuan Zhang, Yiren Song, Jinpeng Yu, Han Pan, Zhongliang Jing
Currently, personalized image generation methods mostly require considerable time to finetune and often overfit the concept resulting in generated images that are similar to custom concepts but difficult to edit by prompts.
1 code implementation • 12 Mar 2024 • Yuxuan Zhang, Lifu Wei, Qing Zhang, Yiren Song, Jiaming Liu, Huaxia Li, Xu Tang, Yao Hu, Haibo Zhao
Current makeup transfer methods are limited to simple makeup styles, making them difficult to apply in real-world scenarios.
1 code implementation • CVPR 2024 • Yuxuan Zhang, Yiren Song, Jiaming Liu, Rui Wang, Jinpeng Yu, Hao Tang, Huaxia Li, Xu Tang, Yao Hu, Han Pan, Zhongliang Jing
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging.
1 code implementation • 5 Dec 2022 • Yiren Song, Xuning Shao, Kang Chen, Weidong Zhang, Minzhe Li, Zhongliang Jing
Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation.