VideoMDM learns coherent 3D motion manifolds from 2D supervision alone by using a pretrained lifter as noisy teacher, depth-weighted 2D reprojection loss, and adapted regularizers, nearly matching fully 3D-supervised performance on HumanML3D.
Deep high-resolution representation learning for visual recognition
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Petro-SAM adapts SAM via a Merge Block for polarized views plus multi-scale fusion and color-entropy priors to jointly achieve grain-edge and lithology segmentation in petrographic images.
Hand pose estimation accuracy generalizes to hand-impaired populations from spinal cord injury with negligible effects from object occlusions.
citing papers explorer
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VideoMDM: Towards 3D Human Motion Generation From 2D Supervision
VideoMDM learns coherent 3D motion manifolds from 2D supervision alone by using a pretrained lifter as noisy teacher, depth-weighted 2D reprojection loss, and adapted regularizers, nearly matching fully 3D-supervised performance on HumanML3D.
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From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation
Petro-SAM adapts SAM via a Merge Block for polarized views plus multi-scale fusion and color-entropy priors to jointly achieve grain-edge and lithology segmentation in petrographic images.
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Impact of Hand Impairment and Occlusions on Hand Pose Estimation Accuracy in Augmented Reality Applications
Hand pose estimation accuracy generalizes to hand-impaired populations from spinal cord injury with negligible effects from object occlusions.