Frame Guidance: Training-Free Guidance for Frame-Level Control in Video
  Diffusion Models
Authors: name: Sangwon Jang, name: Taekyung Ki, name: Jaehyeong Jo, name: Jaehong Yoon, name: Soo Ye Kim, name: Zhe Lin, name: Sung Ju Hwang

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.