Search Results for author: Tianhao Qi

Found 5 papers, 2 papers with code

Mask$^2$DiT: Dual Mask-based Diffusion Transformer for Multi-Scene Long Video Generation

no code implementations25 Mar 2025 Tianhao Qi, Jianlong Yuan, Wanquan Feng, Shancheng Fang, Jiawei Liu, Siyu Zhou, Qian He, Hongtao Xie, Yongdong Zhang

Both qualitative and quantitative experiments confirm that Mask$^2$DiT excels in maintaining visual consistency across segments while ensuring semantic alignment between each segment and its corresponding text description.

text annotation Video Generation

I2VControl: Disentangled and Unified Video Motion Synthesis Control

no code implementations26 Nov 2024 Wanquan Feng, Tianhao Qi, Jiawei Liu, Mingzhen Sun, Pengqi Tu, Tianxiang Ma, Fei Dai, Songtao Zhao, Siyu Zhou, Qian He

Video synthesis techniques are undergoing rapid progress, with controllability being a significant aspect of practical usability for end-users.

Motion Synthesis

I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength

no code implementations10 Nov 2024 Wanquan Feng, Jiawei Liu, Pengqi Tu, Tianhao Qi, Mingzhen Sun, Tianxiang Ma, Songtao Zhao, Siyu Zhou, Qian He

To accurately control and adjust the strength of subject motion, we explicitly model the higher-order components of the video trajectory expansion, not merely the linear terms, and design an operator that effectively represents the motion strength.

Video Generation

DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations

2 code implementations CVPR 2024 Tianhao Qi, Shancheng Fang, Yanze Wu, Hongtao Xie, Jiawei Liu, Lang Chen, Qian He, Yongdong Zhang

The Q-Formers are trained using paired images rather than the identical target, in which the reference image and the ground-truth image are with the same style or semantics.

Disentanglement

Balanced Classification: A Unified Framework for Long-Tailed Object Detection

1 code implementation4 Aug 2023 Tianhao Qi, Hongtao Xie, Pandeng Li, Jiannan Ge, Yongdong Zhang

In this paper, we contend that the learning bias originates from two factors: 1) the unequal competition arising from the imbalanced distribution of foreground categories, and 2) the lack of sample diversity in tail categories.

Hallucination Long-tailed Object Detection +1

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