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.
Od-vae: An omni-dimensional video compressor for improving latent video diffusion model
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NeuroQuant is a modality-aware 3D VQ-VAE that uses dual-stream encoding, a shared anatomical codebook, and FiLM to achieve superior multi-modal brain MRI reconstruction.
ChopGrad truncates backpropagation to local frame windows in video diffusion models, reducing memory from linear in frame count to constant while enabling pixel-wise loss fine-tuning.
TOAU compresses human motion videos to 9 bits per frame with pose estimation and VQ-VAE, then aligns the tokens to a vision-language model via a lightweight projector, achieving 1% transmission payload and 20% latency of video codecs while maintaining comparable action understanding accuracy.
PV-VAE improves video latent spaces for generation by unifying reconstruction with future-frame prediction, reporting 52% faster convergence and 34.42 FVD gain over Wan2.2 VAE on UCF101.
HunyuanVideo presents a 13B-parameter open-source video generative model with integrated data, architecture, training, and inference systems whose professional evaluations show it outperforming prior SOTA models including Runway Gen-3 and Luma 1.6.
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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Modality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI
NeuroQuant is a modality-aware 3D VQ-VAE that uses dual-stream encoding, a shared anatomical codebook, and FiLM to achieve superior multi-modal brain MRI reconstruction.
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ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation
ChopGrad truncates backpropagation to local frame windows in video diffusion models, reducing memory from linear in frame count to constant while enabling pixel-wise loss fine-tuning.
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Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference
TOAU compresses human motion videos to 9 bits per frame with pose estimation and VQ-VAE, then aligns the tokens to a vision-language model via a lightweight projector, achieving 1% transmission payload and 20% latency of video codecs while maintaining comparable action understanding accuracy.
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Video Generation with Predictive Latents
PV-VAE improves video latent spaces for generation by unifying reconstruction with future-frame prediction, reporting 52% faster convergence and 34.42 FVD gain over Wan2.2 VAE on UCF101.
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HunyuanVideo: A Systematic Framework For Large Video Generative Models
HunyuanVideo presents a 13B-parameter open-source video generative model with integrated data, architecture, training, and inference systems whose professional evaluations show it outperforming prior SOTA models including Runway Gen-3 and Luma 1.6.