First, you should perform Canny edge detection on the any video to obtain the corresponding Control Video. Then, use ControlNet-Canny to redraw the first frame of the video to obtain the Reference Image. Finally, use the Control Video and Reference Image as conditions to generate a stylistically different video by PP-VCtrl-Canny-I2V.
First, you are expected to select the subject to be edited from the your video and use SAM2 for subject segmentation to obtain the control video. Then, use Image Inpainting method to edit the first frame of the video to obtain the reference image. Finally, use the control video and reference image as conditions to edit the entire video by PP-VCtrl-I2V-Mask.
First, you can use a pose detection model to obtain the sequence of poses from the video you provide. Then, use ControlNet-Pose to regenerate the first frame of the video, obtaining the reference image. Finally, use the control video and reference image as conditions to perform style transfer and redraw the video by PP-VCtrl-I2V-Pose.
In recent years, text-to-video diffusion models have transformed the landscape of video generation, yet they often struggle with fine-grained control over spatiotemporal dynamics. This paper introduces PP-VCtrl, a novel architecture that enhances existing text-to-video models by integrating a unified conditional encoder, enabling versatile control through auxiliary conditioning signals such as Canny edges, human poses, and segmentation masks. Our approach maintains the integrity of the original generator while allowing for efficient incorporation of diverse control inputs. We demonstrate that PP-VCtrl achieves enhanced performance across various video generation tasks, significantly improving control fidelity and visual quality compared to previous methods. Comprehensive experiments validate the effectiveness of our framework, showcasing its potential for practical applications in controllable video generation.
We propose a unified control encoder capable of handling diverse control types, including but not limited to Canny edges, human poses, and segmentation masks. In our approach, we achieve this unification by using control videos as the raw input for control signals. The control encoder in PP-VCtrl is designed to process these various control signals in a unified manner.
@article{molad2023dreamix,
title={PP-VCtrl: Enabling Versatile Controls for Text-to-Video Diffusion Models},
author={\\\\},
journal={\\\},
year={2025}
}