Frame Guidance: Training-Free Guidance for Frame-Level Control in Video
  Diffusion Models
Advancements in diffusion models have significantly improved video quality,
directing attention to fine-grained controllability. However, many existing
methods depend on fine-tuning large-scale video models for specific tasks,
which becomes increasingly impractical as model sizes continue to grow. In this
work, we present Frame Guidance, a training-free guidance for controllable
video generation based on frame-level signals, such as keyframes, style
reference images, sketches, or depth maps. For practical training-free
guidance, we propose a simple latent processing method that dramatically
reduces memory usage, and apply a novel latent optimization strategy designed
for globally coherent video generation. Frame Guidance enables effective
control across diverse tasks, including keyframe guidance, stylization, and
looping, without any training, compatible with any video models. Experimental
results show that Frame Guidance can produce high-quality controlled videos for
a wide range of tasks and input signals.
6849036342e4f9106973f32a
Sangwon Jang
6849036342e4f9106973f32b
Taekyung Ki
claimed_verified
2025-06-11T08:34:42.228Z
66b57c77778c98d29446c8ec
/avatars/c176bb7c072f3093f6a0786c87d384d8.svg
Taekyung Ki
user
taekyungki
6849036342e4f9106973f32c
Jaehyeong Jo
6849036342e4f9106973f32d
Jaehong Yoon
claimed_verified
2025-06-11T08:34:44.146Z
652066649004117947e46ed6
/avatars/972c97df6f26d2c3d6ce71ec579984bb.svg
Jaehong Yoon
user
jaehong31
6849036342e4f9106973f32e
Soo Ye Kim
6849036342e4f9106973f32f
Zhe Lin
6849036342e4f9106973f330
Sung Ju Hwang
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video
  Diffusion Models
Advancements in diffusion models have significantly improved video quality,
directing attention to fine-grained controllability. However, many existing
methods depend on fine-tuning large-scale video models for specific tasks,
which becomes increasingly impractical as model sizes continue to grow. In this
work, we present Frame Guidance, a training-free guidance for controllable
video generation based on frame-level signals, such as keyframes, style
reference images, sketches, or depth maps. For practical training-free
guidance, we propose a simple latent processing method that dramatically
reduces memory usage, and apply a novel latent optimization strategy designed
for globally coherent video generation. Frame Guidance enables effective
control across diverse tasks, including keyframe guidance, stylization, and
looping, without any training, compatible with any video models. Experimental
results show that Frame Guidance can produce high-quality controlled videos for
a wide range of tasks and input signals.
Frame Guidance offers a training-free method for controlling video generation using frame-level signals, reducing memory usage and enhancing globally coherent video output.
diffusion models frame-level signals keyframes style reference images sketches depth maps latent processing latent optimization globally coherent video generation video models keyframe guidance stylization looping