Efficient Video Diffusion Models: Advancements and Challenges

• URL: http://arxiv.org/abs/2604.15911v1
• Date: Fri, 17 Apr 2026 10:11:39 GMT
• Title: Efficient Video Diffusion Models: Advancements and Challenges
• Abstract summary: Video synthesis compounds computation across spatial-temporal token growth and iterative denoising.<n>This survey provides a systematic and deployment-oriented review of efficient video diffusion models.
• Score: 24.838386291735024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative video synthesis, but their practical deployment remains constrained by severe inference costs. Compared with image generation, video synthesis compounds computation across spatial-temporal token growth and iterative denoising, making attention and memory traffic major bottlenecks in real-world settings. This survey provides a systematic and deployment-oriented review of efficient video diffusion models. We propose a unified categorization that organizes existing methods into four classes of main paradigms, including step distillation, efficient attention, model compression, and cache/trajectory optimization. Building on this categorization, we respectively analyze algorithmic trends of these four paradigms and examine how different design choices target two core objectives: reducing the number of function evaluations and minimizing per-step overhead. Finally, we discuss open challenges and future directions, including quality preservation under composite acceleration, hardware-software co-design, robust real-time long-horizon generation, and open infrastructure for standardized evaluation. To the best of our knowledge, our work is the first comprehensive survey on efficient video diffusion models, offering researchers and engineers a structured overview of the field and its emerging research directions.

Related papers

• Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective [91.23306722968509]
  Generalizable feed-forward 3D reconstruction has witnessed rapid development in recent years.<n>Existing feed-forward approaches share similar high-level architectural patterns.<n>We propose a novel taxonomy centered on model design strategies that are agnostic to the output format.
  arXiv  Detail & Related papers  (2026-04-15T16:07:18Z)
• Demystifing Video Reasoning [71.53763299316041]
  We show that reasoning in video models instead primarily emerges along the diffusion denoising steps.<n>We identify several emergent reasoning behaviors critical to model performance.<n>Motivated by these insights, we present a training-free strategy as a proof-of-concept.
  arXiv  Detail & Related papers  (2026-03-17T17:59:55Z)
• Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models [78.32948112203228]
  Video understanding represents the most challenging frontier in computer vision.<n>Recent emergence of Video-Large Multitemporal Models has demonstrated remarkable capabilities in video understanding tasks.<n>Survey aims to provide researchers and practitioners with a unified framework for advancing Video-LMM capabilities.
  arXiv  Detail & Related papers  (2025-10-06T17:10:44Z)
• SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution [46.311223206965934]
  We study key design principles for latter cascaded video super-resolution models, which are underexplored currently.<n>First, we propose two strategies to generate training pairs that better mimic the output characteristics of the base model, ensuring alignment between the VSR model and its upstream generator.<n>Second, we provide critical insights into VSR model behavior through systematic analysis of (1) timestep sampling strategies, (2) noise augmentation effects on low-resolution (LR) inputs.
  arXiv  Detail & Related papers  (2025-06-24T17:57:26Z)
• RAGME: Retrieval Augmented Video Generation for Enhanced Motion Realism [73.38167494118746]
  We propose a framework to improve the realism of motion in generated videos.<n>We advocate for the incorporation of a retrieval mechanism during the generation phase.<n>Our pipeline is designed to apply to any text-to-video diffusion model.
  arXiv  Detail & Related papers  (2025-04-09T08:14:05Z)
• AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset [55.82208863521353]
  We propose AccVideo to reduce the inference steps for accelerating video diffusion models with synthetic dataset.<n>Our model achieves 8.5x improvements in generation speed compared to the teacher model.<n>Compared to previous accelerating methods, our approach is capable of generating videos with higher quality and resolution.
  arXiv  Detail & Related papers  (2025-03-25T08:52:07Z)
• Raccoon: Multi-stage Diffusion Training with Coarse-to-Fine Curating Videos [15.781862060265519]
  CFC-VIDS-1M is a high-quality video dataset constructed through a systematic coarse-to-fine curation pipeline.<n>We develop RACCOON, a transformer-based architecture with decoupled spatial-temporal attention mechanisms.
  arXiv  Detail & Related papers  (2025-02-28T18:56:35Z)
• Video Diffusion Models: A Survey [3.7985353171858045]
  Diffusion generative models have recently become a powerful technique for creating and modifying high-quality, coherent video content. This survey provides an overview of the critical components of diffusion models for video generation, including their applications, architectural design, and temporal dynamics modeling.
  arXiv  Detail & Related papers  (2024-05-06T04:01:42Z)
• An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement [132.60976158877608]
  We propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate-temporal-information across frames and reduces the need for high complexity networks.
  arXiv  Detail & Related papers  (2020-12-24T00:03:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.