Research map / Image generation / Autoregressive and latent generation
EDGE: Editable Dance Generation From Music
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Jukebox music features (ablation) | EDGE · In-the-wild music | Elo: 1603 -> 1747 | Helped |
| Add Contact Consistency Loss (ablation) | EDGE · In-the-wild music | PFC: 3.0806 -> 1.5363 | Helped |
| Add Contact Consistency Loss (ablation) | EDGE · In-the-wild music | physical-plausibility user-study win rate: 61.8% ± 7.3% | Helped |
| Increase inference guidance weight from w=1 to w=2 (ablation) | EDGE · AIST++ test | Elo: 1601 -> 1751 | Helped |
| Increase inference guidance weight from w=1 to w=2 (ablation) | EDGE · AIST++ test | Dist_k: 10.58 -> 9.48 | Mixed |
Closest papers by meaning
- Bidirectional Autoregessive Diffusion Model for Dance Generation (CVPR 2024 · similarity 0.88)
- DanceAnyWay: Synthesizing Beat-Guided 3D Dances with Randomized Temporal Contrastive Learning (AAAI 2024 · similarity 0.87)
- Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives (CVPR 2024 · similarity 0.87)
- MEGADance: Mixture-of-Experts Architecture for Genre-Aware 3D Dance Generation (NeurIPS 2025 · similarity 0.87)
- POPDG: Popular 3D Dance Generation with PopDanceSet (CVPR 2024 · similarity 0.86)
- Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation (ICML 2025 · similarity 0.86)
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