2026
RecTok: Reconstruction Distillation along Rectified Flow
Qingyu Shi, Size Wu, Jinbin Bai, Kaidong Yu, Yujing Wang, Yunhai Tong, Xiangtai Li, Xuelong Li
CVPR 2026 Poster
We introduce RecTok, a visual tokenizer trained with reconstruction distillation along rectified flow. It improves the balance between perceptual reconstruction and generation quality, enabling strong image generation with diffusion transformers.
RecTok: Reconstruction Distillation along Rectified Flow
Qingyu Shi, Size Wu, Jinbin Bai, Kaidong Yu, Yujing Wang, Yunhai Tong, Xiangtai Li, Xuelong Li
CVPR 2026 Poster
We introduce RecTok, a visual tokenizer trained with reconstruction distillation along rectified flow. It improves the balance between perceptual reconstruction and generation quality, enabling strong image generation with diffusion transformers.
Beyond Text-to-Image: Liberating Generation with a Unified Discrete Diffusion Model
Qingyu Shi, Jinbin Bai, Zhuoran Zhao, Wenhao Chai, Kaidong Yu, Jianzong Wu, Shuangyong Song, Yunhai Tong, Xiangtai Li, Xuelong Li, Shuicheng YAN
ICLR 2026 Poster
We introduce Muddit, a unified discrete diffusion transformer that enables fast and parallel generation across both text and image modalities. Empirical results show that Muddit achieves competitive or superior performance compared to significantly larger autoregressive models in both quality and efficiency.
Beyond Text-to-Image: Liberating Generation with a Unified Discrete Diffusion Model
Qingyu Shi, Jinbin Bai, Zhuoran Zhao, Wenhao Chai, Kaidong Yu, Jianzong Wu, Shuangyong Song, Yunhai Tong, Xiangtai Li, Xuelong Li, Shuicheng YAN
ICLR 2026 Poster
We introduce Muddit, a unified discrete diffusion transformer that enables fast and parallel generation across both text and image modalities. Empirical results show that Muddit achieves competitive or superior performance compared to significantly larger autoregressive models in both quality and efficiency.
2025
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers For Motion Transfer
Qingyu Shi, Jianzong Wu, Jinbin Bai, Jiangning Zhang, Lu Qi, Yunhai Tong, Xiangtai Li
ICCV 2025 Poster
In this paper, we propose DeT, a method that adapts DiT models to improve motion transfer ability. Our approach introduces a simple yet effective temporal kernel to smooth DiT features along the temporal dimension, facilitating the decoupling of foreground motion from background appearance.
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers For Motion Transfer
Qingyu Shi, Jianzong Wu, Jinbin Bai, Jiangning Zhang, Lu Qi, Yunhai Tong, Xiangtai Li
ICCV 2025 Poster
In this paper, we propose DeT, a method that adapts DiT models to improve motion transfer ability. Our approach introduces a simple yet effective temporal kernel to smooth DiT features along the temporal dimension, facilitating the decoupling of foreground motion from background appearance.
2024
DreamRelation: Bridging Customization and Relation Generation
Qingyu Shi, Lu Qi, Jianzong Wu, Jinbin Bai, Jingbo Wang, Yunhai Tong, Xiangtai Li
CVPR 2025 Oral
We introduce DreamRelation, a framework that disentangles identity and relation learning using a carefully curated dataset. Our training data consists of relation-specific images, independent object images containing identity information, and text prompts to guide relation generation. Extensive results on our proposed benchmarks demonstrate the superiority of DreamRelation in generating precise relations while preserving object identities across a diverse set of objects and relationships.
DreamRelation: Bridging Customization and Relation Generation
Qingyu Shi, Lu Qi, Jianzong Wu, Jinbin Bai, Jingbo Wang, Yunhai Tong, Xiangtai Li
CVPR 2025 Oral
We introduce DreamRelation, a framework that disentangles identity and relation learning using a carefully curated dataset. Our training data consists of relation-specific images, independent object images containing identity information, and text prompts to guide relation generation. Extensive results on our proposed benchmarks demonstrate the superiority of DreamRelation in generating precise relations while preserving object identities across a diverse set of objects and relationships.