FCVSR is a frequency-aware deep model for compressed video super-resolution using MGAA and MFFR modules plus contrastive loss, achieving up to 0.14 dB PSNR gain on three public datasets.
LaMD: Latent Motion Diffusion for Video Generation
3 Pith papers cite this work. Polarity classification is still indexing.
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LetsTalk combines a multimodal diffusion transformer, noise-regularized memory bank, deep compression autoencoder, and symbiotic/direct fusion schemes to achieve state-of-the-art quality and efficiency in long-duration talking video generation.
ActDiff-VC partitions video into segments, transmits adaptive keyframes and budget-aware point trajectories, and reconstructs frames via conditional diffusion, reporting up to 64.6% bitrate reduction at matched NIQE on UVG and MCL-JCV.
citing papers explorer
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FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution
FCVSR is a frequency-aware deep model for compressed video super-resolution using MGAA and MFFR modules plus contrastive loss, achieving up to 0.14 dB PSNR gain on three public datasets.
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Multimodal Diffusion Transformer with Memory Bank for Scalable Long-Duration Talking Video Generation
LetsTalk combines a multimodal diffusion transformer, noise-regularized memory bank, deep compression autoencoder, and symbiotic/direct fusion schemes to achieve state-of-the-art quality and efficiency in long-duration talking video generation.
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Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
ActDiff-VC partitions video into segments, transmits adaptive keyframes and budget-aware point trajectories, and reconstructs frames via conditional diffusion, reporting up to 64.6% bitrate reduction at matched NIQE on UVG and MCL-JCV.