MoGeFlow learns text-conditioned flows over PartVQ group-specific code embeddings to generate motions, achieving SOTA R-Precision on HumanML3D and KIT-ML while preserving discrete token validity.
Irg-motionllm: Interleaving motion generation, assessment and refinement for text-to-motion generation
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
PhysiGen reduces interpenetration in text-driven 3D human interaction generation by simplifying meshes to geometric primitives for fast collision detection and guiding optimization with collision regions.
MotionHiFlow generates text-aligned 3D human motions using hierarchical flow matching across temporal scales, cross-scale transitions, a Text-Motion Diffusion Transformer, and a topology-aware Motion VAE, achieving state-of-the-art results on HumanML3D and KIT-ML.
citing papers explorer
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MoGeFlow: Flowing Through Motion Codebook Geometry for Text-to-Motion Generation
MoGeFlow learns text-conditioned flows over PartVQ group-specific code embeddings to generate motions, achieving SOTA R-Precision on HumanML3D and KIT-ML while preserving discrete token validity.
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PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation
PhysiGen reduces interpenetration in text-driven 3D human interaction generation by simplifying meshes to geometric primitives for fast collision detection and guiding optimization with collision regions.
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MotionHiFlow: Text-to-motion via hierarchical flow matching
MotionHiFlow generates text-aligned 3D human motions using hierarchical flow matching across temporal scales, cross-scale transitions, a Text-Motion Diffusion Transformer, and a topology-aware Motion VAE, achieving state-of-the-art results on HumanML3D and KIT-ML.