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Accelerating video diffusion models via distribution matching

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

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citation-polarity summary

years

2026 2 2025 1

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UNVERDICTED 3

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representative citing papers

Efficient Video Diffusion Models: Advancements and Challenges

cs.CV · 2026-04-17 · unverdicted · novelty 7.0

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.

ReSim: Reliable World Simulation for Autonomous Driving

cs.CV · 2025-06-11 · unverdicted · novelty 6.0

ReSim is a controllable video world model trained on heterogeneous real and simulated driving data that achieves higher fidelity and controllability for both expert and non-expert actions, plus a Video2Reward module for estimating action quality from simulated futures.

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Showing 3 of 3 citing papers.

  • Efficient Video Diffusion Models: Advancements and Challenges cs.CV · 2026-04-17 · unverdicted · none · ref 203

    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.

  • Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms eess.IV · 2026-03-30 · unverdicted · none · ref 63

    Video generation models can function as world simulators if efficiency gaps in spatiotemporal modeling are bridged via organized paradigms, architectures, and algorithms.

  • ReSim: Reliable World Simulation for Autonomous Driving cs.CV · 2025-06-11 · unverdicted · none · ref 119

    ReSim is a controllable video world model trained on heterogeneous real and simulated driving data that achieves higher fidelity and controllability for both expert and non-expert actions, plus a Video2Reward module for estimating action quality from simulated futures.