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

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

generative models3D human motion generationdiffusion modelsconditional generation
84.20100
Fused
band ≈ ±14 pct pts (from σ = 0.28)
92.20100
Mimo
band ≈ ±20 pct pts (from σ = 0.41)
75.20100
DeepSeek
band ≈ ±20 pct pts (from σ = 0.40)

OpenReview ground truth

Accepted

Abstract

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. Project page: https://neu-vi.github.io/omnicontrol/.

Author context

Most prolific author: 2 submissions (credibility 1.00).

No mass-submission penalty for this paper (authors within normal submission volume).

Aggregate statistics only — no individual author rankings.

Ranking trajectory

Percentile by tournament round — convergence indicates rating stability.

Judge assessments

Mean overall score 0.0 ± 0.0 (n = 36)