OmniME integrates retrospective feature supervision, motion preservation, and triplet semantic alignment to achieve state-of-the-art text-motion editing alignment on MotionFix and STANCE datasets.
Human Motion Modeling using DVGANs
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abstract
We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization over actions, and generation of long diverse sequences. We evaluate our approach on Human 3.6M and CMU motion capture datasets using inception scores.
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cs.CV 1years
2026 1verdicts
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Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning
OmniME integrates retrospective feature supervision, motion preservation, and triplet semantic alignment to achieve state-of-the-art text-motion editing alignment on MotionFix and STANCE datasets.