Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled
  Sampling
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.
67d0ed0877b0c8ac3f304ef1
Subin Kim
admin_assigned
2025-03-12T15:52:03.163Z
63eb83b74dcaaf08763766a9
/avatars/11e3e00c49a3b74947cce2f1ef624944.svg
Subin Kim
user
subin-kim
67d0ed0877b0c8ac3f304ef2
Seoung Wug Oh
admin_assigned
2025-03-12T15:51:09.316Z
668c85990a3764d053282c81
/avatars/d2023b13f623d0fa3db6a6270f2a0fae.svg
Seoung Wug Oh
user
Seoungwug
67d0ed0877b0c8ac3f304ef3
Jui-Hsien Wang
67d0ed0877b0c8ac3f304ef4
Joon-Young Lee
admin_assigned
2025-03-12T15:51:28.738Z
64b76c7990b38df83381824b
/avatars/0a5bec2ea480fb3f43c4b24d59c50e81.svg
Joon-Young Lee
user
joonyounglee
67d0ed0877b0c8ac3f304ef5
Jinwoo Shin
admin_assigned
2025-03-12T15:51:37.086Z
66aae00a3952795c769d4586
/avatars/9226afdc35cf3adfc19dfa9328bb03a1.svg
Jinwoo Shin
user
jshinsci2024
Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled
  Sampling
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.
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.
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