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Found 5 papers, 4 papers with code
MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation
Specifically, we separate the denoising objectives of a diffusion model into two stages: obtaining conditional rough motion approximations in the initial $T-T^*$ steps by learning the noisy annotated motions, followed by the unconditional refinement of these preliminary motions during the last $T^*$ steps using unannotated motions.
Priority-Centric Human Motion Generation in Discrete Latent Space
We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token within the entire motion sequence.
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities.
PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #37 on
3D Human Pose Estimation
on MPI-INF-3DHP
PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation
To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
