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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2026 4representative citing papers
Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
PianoFlow generates coordinated bimanual piano motions from audio via MIDI-distilled flow-matching, asymmetric role-gated interaction, and autoregressive streaming continuation, outperforming priors with 9x faster inference.
CoLoRSMamba steers AudioMamba using video CLS-guided conditional LoRA to adapt selective state-space parameters, outperforming baselines on audio-filtered NTU-CCTV and DVD subsets with 88.63% and 75.77% accuracy respectively.
citing papers explorer
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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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Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation
Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
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PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination
PianoFlow generates coordinated bimanual piano motions from audio via MIDI-distilled flow-matching, asymmetric role-gated interaction, and autoregressive streaming continuation, outperforming priors with 9x faster inference.
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CoLoRSMamba: Conditional LoRA-Steered Mamba for Supervised Multimodal Violence Detection
CoLoRSMamba steers AudioMamba using video CLS-guided conditional LoRA to adapt selective state-space parameters, outperforming baselines on audio-filtered NTU-CCTV and DVD subsets with 88.63% and 75.77% accuracy respectively.