9 papers
FastVMT: Eliminating Redundancy in Video Motion Transfer
Yue Ma, Zhikai Wang, Tianhao Ren +9
Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed in a reference video. Recent…
Follow-Your-Motion: Video Motion Transfer via Efficient Spatial-Temporal Decoupled Finetuning
Yue Ma, Yulong Liu, Qiyuan Zhu +8
Recently, breakthroughs in the video diffusion transformer have shown remarkable capabilities in diverse motion generations. As for the motion-transfer task, current methods mainly…
InstanceAnimator: Multi-Instance Sketch Video Colorization
Yinhan Zhang, Yue Ma, Bingyuan Wang +5
We propose InstanceAnimator, a novel Diffusion Transformer framework for multi-instance sketch video colorization. Existing methods suffer from three core limitations: inflexible u…
Controllable Video Generation: A Survey
Yue Ma, Kunyu Feng, Zhongyuan Hu +19
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…
EEdit: Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing
Zexuan Yan, Yue Ma, Chang Zou +3
Inversion-based image editing is rapidly gaining momentum while suffering from significant computation overhead, hindering its application in real-time interactive scenarios. In th…
Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis
Kunyu Feng, Yue Ma, Xinhua Zhang +9
With the growing demands of AI-generated content (AIGC), the need for high-quality, diverse, and scalable data has become increasingly crucial. However, collecting large-scale real…