DanceCrafter generates high-fidelity, text-controlled dance sequences using a new Choreographic Syntax framework and a large fine-grained motion dataset.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbone with noise-filtered supervision.
Rule-based and learning-based algorithms simplify dance motions to help novices learn more effectively while maintaining naturalness and style.
TrioMan is a tri-module data augmentation framework using a Generator for pose/camera perturbations, a Refiner with one-step diffusion, and an Examiner with dual-branch attention to improve 3D avatar learning from monocular videos, claiming better results than prior methods on two benchmarks.
citing papers explorer
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DanceCrafter: Fine-Grained Text-Driven Controllable Dance Generation via Choreographic Syntax
DanceCrafter generates high-fidelity, text-controlled dance sequences using a new Choreographic Syntax framework and a large fine-grained motion dataset.
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TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbone with noise-filtered supervision.
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Make it Simple, Make it Dance: Dance Motion Simplification to Support Novices' Dance Learning
Rule-based and learning-based algorithms simplify dance motions to help novices learn more effectively while maintaining naturalness and style.
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Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos
TrioMan is a tri-module data augmentation framework using a Generator for pose/camera perturbations, a Refiner with one-step diffusion, and an Examiner with dual-branch attention to improve 3D avatar learning from monocular videos, claiming better results than prior methods on two benchmarks.