A survey that groups efficient video diffusion methods into four paradigms—step distillation, efficient attention, model compression, and cache/trajectory optimization—and outlines open challenges for practical use.
LM2D: Lyrics- and Music-Driven Dance Synthesis
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Sparse interleaved multi-view inputs with DenseWarper outperform traditional dense simultaneous multi-view methods for 3D human pose estimation on Human3.6M and MPI-INF-3DHP datasets.
LRCM is a new multimodal diffusion model with audio and text Conformers plus Motion Temporal Mamba for generating long, coherent dance sequences from rhythm and descriptions using a decoupled dataset.
citing papers explorer
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Efficient Video Diffusion Models: Advancements and Challenges
A survey that groups efficient video diffusion methods into four paradigms—step distillation, efficient attention, model compression, and cache/trajectory optimization—and outlines open challenges for practical use.
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From Sparse to Dense: Spatio-Temporal Fusion for Multi-View 3D Human Pose Estimation with DenseWarper
Sparse interleaved multi-view inputs with DenseWarper outperform traditional dense simultaneous multi-view methods for 3D human pose estimation on Human3.6M and MPI-INF-3DHP datasets.
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Listen to Rhythm, Choose Movements: Autoregressive Multimodal Dance Generation via Diffusion and Mamba with Decoupled Dance Dataset
LRCM is a new multimodal diffusion model with audio and text Conformers plus Motion Temporal Mamba for generating long, coherent dance sequences from rhythm and descriptions using a decoupled dataset.