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DragAnything: Motion Control for Anything using Entity Representation

About

We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, DragAnything offers several advantages. Firstly, trajectory-based is more userfriendly for interaction, when acquiring other guidance signals (e.g., masks, depth maps) is labor-intensive. Users only need to draw a line (trajectory) during interaction. Secondly, our entity representation serves as an open-domain embedding capable of representing any object, enabling the control of motion for diverse entities, including background. Lastly, our entity representation allows simultaneous and distinct motion control for multiple objects. Extensive experiments demonstrate that our DragAnything achieves state-of-the-art performance for FVD, FID, and User Study, particularly in terms of object motion control, where our method surpasses the previous methods (e.g., DragNUWA) by 26% in human voting.

Weijia Wu, Zhuang Li, Yuchao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, Di Zhang• 2024

Related benchmarks

TaskDatasetResultRank
Video GenerationDAVIS (val)
PSNR15.119
18
Image-to-Video GenerationVIPSeg (test)
FID78.6
12
Track-Conditioned Video GenerationDAVIS (val)
PSNR15.119
12
Object MovementObjMove A (test)
PSNR16.36
12
Video GenerationShort-horizon tasks (test)
Aesthetic Quality50
8
User-controlled Image EditingCurated benchmark of 50 subjects
LPIPS0.655
8
Image-to-Video GenerationCustom 27-video (test)
Motion Smoothness0.985
7
Object ManipulationGeoEditBench
PSNR17.65
7
Object ManipulationObjectMover-A
PSNR16.36
7
Motion-controllable Video GenerationMotion Controllability Benchmark 206-clip
Latency (s)68.76
6
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