NEWSLETTER
Text-based video editing aims to create new videos from text prompts and existing video material without any manual work. This technology has the potential to substantially impact various industries, including social media content, marketing, and advertising. Modified movies must accurately reflect the original video content, maintain temporal consistency between frames created, and align with target cues to be successful in this process. However, it can be challenging to meet all of these demands simultaneously. It takes a lot of computing power to train a text-to-video model using only large amounts of text and video data.
Zero-take and single-take text-based video editing approaches have utilized recent developments in large-scale text-to-image broadcast models and programmable image editing. Without the need for additional video data, these advances have shown a good ability to alter movies in response to a variety of textual commands. However, empirical data reveals that current techniques still fail to properly and adequately manage output while maintaining temporal consistency, despite tremendous advances in aligning work with text keys. Researchers from Tsinghua University, Renmin University of China, ShengShu, and Pazhou Laboratory present ControlVideo, a state-of-the-art method based on a pre-trained text-to-image diffusion model for faithful and reliable text-based video editing.
Taking inspiration from ControlNet, ControlVideo amplifies the direction of the source video by including visual conditions such as Canny edge maps, HED edges, and depth maps for all frames as additional inputs. A ControlNet pretrained on the broadcast model handles these visual circumstances. When comparing such circumstances to the text and attention-based tactics now used in text-based video editing approaches, it’s worth noting that they offer a more precise and adaptable form of video control. Furthermore, to improve fidelity and temporal consistency and avoid overfitting, the attention modules in both the diffusion model and ControlNet have been carefully built and tuned.
To be more precise, they change the initial spatial auto-attention in both models to keyframe attention, aligning all frames to a chosen one. The diffusion model also includes temporary attention modules as additional branches, followed by a zero convolutional layer to preserve the output before fine tuning. They use the original spatial self-attention weights as initialization for temporal and keyframe attention in the corresponding network because different attention mechanisms have been observed to model relationships between different positions but consistently model relationships between image features.
To guide future research on the backbones of the broadcast video model for one-shot tuning, they perform a thorough empirical investigation of the essential elements of ControlVideo. This paper investigates key and value designs, parameters for self-care fine-tuning, initialization techniques, and includes local and global locations for introducing temporary care. According to their findings, the main UNet, except the middle block, can be trained to operate in the best way by choosing a keyframe as key and value, fine-tuning WO, and combining temporal attention with self-attention (keyframe attention in this case). case). study).
They also carefully examine the contributions of each component, as well as the overall impact. After work, they assemble 40 video-text pairs to examine, including Davis’s dataset and others from the Internet. By many measures, they compare to text-driven video editing techniques SOTA and Stable Diffusion by frames. In particular, they use the SSIM score to measure fidelity and the CLIP to assess text alignment and temporal consistency. They also conduct user research comparing ControlVideo against all baselines.
Numerous findings show that ControlVideo performs comparably to text alignment, while significantly exceeding all of these baselines with respect to fidelity and temporal consistency. Their empirical results, in particular, highlight ControlVideo’s compelling ability to create movies with stunningly lifelike visual quality and to maintain the source material while reliably adhering to written instructions. For example, ControlVideo succeeds where all other cosmetic technologies fail, while preserving a person’s distinctive facial features.
In addition, ControlVideo allows for a customizable trade-off between video fidelity and editability by using a variety of control types that incorporate different amounts of information from the original video (see Figure 1). The HED borderline, for example, offers fine details of the original video borders and is appropriate for tight control such as facial video editing. Pose includes the motion data from the original video, giving the user more freedom to modify the subject and background while preserving motion transfer. In addition, they show how you can mix multiple controls to take advantage of multiple control types.
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Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
