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20242026
most citedMobile Video Diffusion

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2026

Where Compute Matters: Heterogeneous Attention for Efficient Video Diffusion

Olga Zatsarynna, Denis Korzhenkov, Juergen Gall +2

Efficient video generation requires reducing the quadratic cost of self-attention over long spatio-temporal token sequences. Existing efficient-attention methods typically apply th…

cs.CV2026

SQuad: Sub-Quadratic Attention Distillation for Efficient Video Generation

Animesh Karnewar, Denis Korzhenkov, Amirhossein Habibian +1

Video Diffusion Transformers (DiTs) spend most of their compute inside the Self-Attention operation, whose cost grows quadratically, , with the number of latent t…

cs.CV2026

MobileWan: Closing the Quality Gap for Mobile Video Diffusion

Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv +9

Recent advances in video diffusion have been driven by scaling transformer-based architectures to billions of parameters, substantially improving visual fidelity and motion coheren…

cs.CV2026

PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference

Denis Korzhenkov, Adil Karjauv, Animesh Karnewar +2

Recently proposed pyramidal models decompose the conventional forward and backward diffusion processes into multiple stages operating at varying resolutions. These models handle in…

cs.CV2025

Neodragon: Mobile Video Generation using Diffusion Transformer

Animesh Karnewar, Denis Korzhenkov, Ioannis Lelekas +10

We introduce Neodragon, a text-to-video system capable of generating 2s (49 frames @24 fps) videos at the 640x1024 resolution directly on a Qualcomm Hexagon NPU in a record 6.7s (7…

cs.CV2025

MoAlign: Motion-Centric Representation Alignment for Video Diffusion Models

Aritra Bhowmik, Denis Korzhenkov, Cees G. M. Snoek +2

Text-to-video diffusion models have enabled high-quality video synthesis, yet often fail to generate temporally coherent and physically plausible motion. A key reason is the models…

cs.CV2025

Attention Surgery: An Efficient Recipe to Linearize Your Video Diffusion Transformer

Mohsen Ghafoorian, Denis Korzhenkov, Amirhossein Habibian

Transformer-based video diffusion models (VDMs) deliver state-of-the-art video generation quality but are constrained by the quadratic cost of self-attention, making long sequences…

cs.CV2024★ 1 cited

Mobile Video Diffusion

Haitam Ben Yahia, Denis Korzhenkov, Ioannis Lelekas +2

Video diffusion models have achieved impressive realism and controllability but are limited by high computational demands, restricting their use on mobile devices. This paper intro…