Ouroboros-Diffusion: Exploring Consistent Content Generation in
  Tuning-free Long Video Diffusion
The first-in-first-out (FIFO) video diffusion, built on a pre-trained
text-to-video model, has recently emerged as an effective approach for
tuning-free long video generation. This technique maintains a queue of video
frames with progressively increasing noise, continuously producing clean frames
at the queue's head while Gaussian noise is enqueued at the tail. However,
FIFO-Diffusion often struggles to keep long-range temporal consistency in the
generated videos due to the lack of correspondence modeling across frames. In
this paper, we propose Ouroboros-Diffusion, a novel video denoising framework
designed to enhance structural and content (subject) consistency, enabling the
generation of consistent videos of arbitrary length. Specifically, we introduce
a new latent sampling technique at the queue tail to improve structural
consistency, ensuring perceptually smooth transitions among frames. To enhance
subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA)
mechanism, which aligns subjects across frames within short segments to achieve
better visual coherence. Furthermore, we introduce self-recurrent guidance.
This technique leverages information from all previous cleaner frames at the
front of the queue to guide the denoising of noisier frames at the end,
fostering rich and contextual global information interaction. Extensive
experiments of long video generation on the VBench benchmark demonstrate the
superiority of our Ouroboros-Diffusion, particularly in terms of subject
consistency, motion smoothness, and temporal consistency.
678893e40a465aa0613dc9b2
Jingyuan Chen
678893e40a465aa0613dc9b3
Fuchen Long
claimed_verified
2025-01-16T09:41:40.611Z
6449f2dfeb7db8f70fb990f8
/avatars/72c02e754bb35dab05c4a5f1e69c95f1.svg
Fuchen
user
FireCRT
678893e40a465aa0613dc9b4
Jie An
claimed_verified
2025-01-17T10:25:29.353Z
6267fb1387d7f9040e031d39
/avatars/c7170aa3d28d2cb851e075b98ca5f5b1.svg
Jie An
user
pkuanjie
678893e40a465aa0613dc9b5
Zhaofan Qiu
admin_assigned
2025-01-16T08:54:51.062Z
64ac099294ae6b609d0f0713
/avatars/7f50e3e1597fb5fd0dad9fe7fd2e26ec.svg
Zhaofan Qiu
user
qiudavy
678893e40a465aa0613dc9b6
Ting Yao
678893e40a465aa0613dc9b7
Jiebo Luo
678893e40a465aa0613dc9b8
Tao Mei
admin_assigned
2025-01-16T08:55:14.020Z
66a8d386fbde2b9cb1120393
/avatars/ccfb6e72764e547be2b3528713151693.svg
Tao Mei
user
GiantBision
Ouroboros-Diffusion: Exploring Consistent Content Generation in
  Tuning-free Long Video Diffusion
The first-in-first-out (FIFO) video diffusion, built on a pre-trained
text-to-video model, has recently emerged as an effective approach for
tuning-free long video generation. This technique maintains a queue of video
frames with progressively increasing noise, continuously producing clean frames
at the queue's head while Gaussian noise is enqueued at the tail. However,
FIFO-Diffusion often struggles to keep long-range temporal consistency in the
generated videos due to the lack of correspondence modeling across frames. In
this paper, we propose Ouroboros-Diffusion, a novel video denoising framework
designed to enhance structural and content (subject) consistency, enabling the
generation of consistent videos of arbitrary length. Specifically, we introduce
a new latent sampling technique at the queue tail to improve structural
consistency, ensuring perceptually smooth transitions among frames. To enhance
subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA)
mechanism, which aligns subjects across frames within short segments to achieve
better visual coherence. Furthermore, we introduce self-recurrent guidance.
This technique leverages information from all previous cleaner frames at the
front of the queue to guide the denoising of noisier frames at the end,
fostering rich and contextual global information interaction. Extensive
experiments of long video generation on the VBench benchmark demonstrate the
superiority of our Ouroboros-Diffusion, particularly in terms of subject
consistency, motion smoothness, and temporal consistency.
Ouroboros-Diffusion improves long video generation by introducing latent sampling, Subject-Aware Cross-Frame Attention (SACFA), and self-recurrent guidance to enhance structural and content consistency.
FIFO video diffusion pre-trained text-to-video model frame queue Gaussian noise Ouroboros-Diffusion latent sampling Subject-Aware Cross-Frame Attention (SACFA) self-recurrent guidance VBench benchmark motion smoothness temporal consistency