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OmniControl: Control Any Joint at Any Time for Human Motion Generation

About

We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process. Unlike previous methods that can only control the pelvis trajectory, OmniControl can incorporate flexible spatial control signals over different joints at different times with only one model. Specifically, we propose analytic spatial guidance that ensures the generated motion can tightly conform to the input control signals. At the same time, realism guidance is introduced to refine all the joints to generate more coherent motion. Both the spatial and realism guidance are essential and they are highly complementary for balancing control accuracy and motion realism. By combining them, OmniControl generates motions that are realistic, coherent, and consistent with the spatial constraints. Experiments on HumanML3D and KIT-ML datasets show that OmniControl not only achieves significant improvement over state-of-the-art methods on pelvis control but also shows promising results when incorporating the constraints over other joints.

Yiming Xie, Varun Jampani, Lei Zhong, Deqing Sun, Huaizu Jiang• 2023

Related benchmarks

TaskDatasetResultRank
Motion ControlHumanML3D (test)
Average Error0.0338
65
Geometric-Constrained Motion GenerationGeometric-Constrained Generation
Trajectory Error17.89
8
spatial-text human motion controllabilityHumanML3D 6
Error0.0938
8
Human motion controllabilityHumanML3D
Error0.2372
8
Human motion controllabilityOMOMO
Positional Error0.2687
8
spatial-text human motion controllabilityOMOMO 15
Error0.1389
8
Reinforcement Learning Policy SuccessFull-Mix (test)
Group 1 Success Ratio4
4
Human Motion GenerationFull-Mix (test)
FID0.981
4
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