title: Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled
    Sampling
summary: While recent advancements in text-to-video diffusion models enable
  high-quality short video generation from a single prompt, generating real-world
  long videos in a single pass remains challenging due to limited data and high
  computational costs. To address this, several works propose tuning-free
  approaches, i.e., extending existing models for long video generation,
  specifically using multiple prompts to allow for dynamic and controlled content
  changes. However, these methods primarily focus on ensuring smooth transitions
  between adjacent frames, often leading to content drift and a gradual loss of
  semantic coherence over longer sequences. To tackle such an issue, we propose
  Synchronized Coupled Sampling (SynCoS), a novel inference framework that
  synchronizes denoising paths across the entire video, ensuring long-range
  consistency across both adjacent and distant frames. Our approach combines two
  complementary sampling strategies: reverse and optimization-based sampling,
  which ensure seamless local transitions and enforce global coherence,
  respectively. However, directly alternating between these samplings misaligns
  denoising trajectories, disrupting prompt guidance and introducing unintended
  content changes as they operate independently. To resolve this, SynCoS
  synchronizes them through a grounded timestep and a fixed baseline noise,
  ensuring fully coupled sampling with aligned denoising paths. Extensive
  experiments show that SynCoS significantly improves multi-event long video
  generation, achieving smoother transitions and superior long-range coherence,
  outperforming previous approaches both quantitatively and qualitatively.
paper_authors._id: 67d0ed0877b0c8ac3f304ef1
paper_authors.name: Subin Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:52:03.163Z
paper_authors._id: 63eb83b74dcaaf08763766a9
paper_authors.avatarUrl: /avatars/11e3e00c49a3b74947cce2f1ef624944.svg
paper_authors.fullname: Subin Kim
paper_authors.type: user
paper_authors.user: subin-kim
paper_authors._id: 67d0ed0877b0c8ac3f304ef2
paper_authors.name: Seoung Wug Oh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:51:09.316Z
paper_authors._id: 668c85990a3764d053282c81
paper_authors.avatarUrl: /avatars/d2023b13f623d0fa3db6a6270f2a0fae.svg
paper_authors.fullname: Seoung Wug Oh
paper_authors.type: user
paper_authors.user: Seoungwug
paper_authors._id: 67d0ed0877b0c8ac3f304ef3
paper_authors.name: Jui-Hsien Wang
paper_authors._id: 67d0ed0877b0c8ac3f304ef4
paper_authors.name: Joon-Young Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:51:28.738Z
paper_authors._id: 64b76c7990b38df83381824b
paper_authors.avatarUrl: /avatars/0a5bec2ea480fb3f43c4b24d59c50e81.svg
paper_authors.fullname: Joon-Young Lee
paper_authors.type: user
paper_authors.user: joonyounglee
paper_authors._id: 67d0ed0877b0c8ac3f304ef5
paper_authors.name: Jinwoo Shin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:51:37.086Z
paper_authors._id: 66aae00a3952795c769d4586
paper_authors.avatarUrl: /avatars/9226afdc35cf3adfc19dfa9328bb03a1.svg
paper_authors.fullname: Jinwoo Shin
paper_authors.type: user
paper_authors.user: jshinsci2024
paper_title: Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled
    Sampling
paper_summary: While recent advancements in text-to-video diffusion models enable
  high-quality short video generation from a single prompt, generating real-world
  long videos in a single pass remains challenging due to limited data and high
  computational costs. To address this, several works propose tuning-free
  approaches, i.e., extending existing models for long video generation,
  specifically using multiple prompts to allow for dynamic and controlled content
  changes. However, these methods primarily focus on ensuring smooth transitions
  between adjacent frames, often leading to content drift and a gradual loss of
  semantic coherence over longer sequences. To tackle such an issue, we propose
  Synchronized Coupled Sampling (SynCoS), a novel inference framework that
  synchronizes denoising paths across the entire video, ensuring long-range
  consistency across both adjacent and distant frames. Our approach combines two
  complementary sampling strategies: reverse and optimization-based sampling,
  which ensure seamless local transitions and enforce global coherence,
  respectively. However, directly alternating between these samplings misaligns
  denoising trajectories, disrupting prompt guidance and introducing unintended
  content changes as they operate independently. To resolve this, SynCoS
  synchronizes them through a grounded timestep and a fixed baseline noise,
  ensuring fully coupled sampling with aligned denoising paths. Extensive
  experiments show that SynCoS significantly improves multi-event long video
  generation, achieving smoother transitions and superior long-range coherence,
  outperforming previous approaches both quantitatively and qualitatively.
paper_ai_summary: SynCoS, a novel inference framework for text-to-video diffusion models, enhances long video generation by synchronizing denoising paths for smooth transitions and global coherence.
paper_ai_keywords: text-to-video diffusion models denoising paths long-range consistency reverse sampling optimization-based sampling grounded timestep fixed baseline noise SynCoS multi-event long video generation smooth transitions long-range coherence