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Recently, I read an article about the Oscar awards and got to know that in the film “Everything Everywhere All at Once,” a five-person VFX crew used an AI-based editor for green screen removals and other visual effects, which drastically cut laborious work down.

That urged me to learn what AI video editors are- apps that automate scene cuts, follow faces, and even reframe shots, without needing a giant studio budget.

AI Video Editor is a software that uses machine learning and computer vision to automate or assist in editing tasks that typically require human input. It can include trimming scenes, balancing footage, creating B-rolls, or adding cinematic effects.

Before proceeding, remember that these tools aren’t video generators. While generators produce new content from scratch, editors retouch or manipulate existing material using its automated algorithms.

AI Video Editing Pipeline

Fundamentally, AI video editors integrate computer vision, deep learning, and audio‑visual processing into a multi‑stage, streamlined pipeline. Depending on the toolkit and its purpose- an app or a film studio- it performs each step end‑to‑end or provides each module as a separate microservice. Below is a simplified explanation of its process:

1. Ingestion and Preprocessing- This process decodes raw footage into individual frames. Metadata, such as audio tracks, motion vectors, and timestamps, are extracted for downstream processing.

2. Vision & Audio Analysis- CNN models like YOLO (You Only Look Once) and Mask R-CNN (Region-based) are used for object detection and tracking, allowing for identifying people and objects, as per every frame. Facial recognition is used to identify faces and enable emotion-driven cuts or automatic reframing. Additionally, Optical flow analyzes pixel movement between frames to stabilize video.

3. Semantic Understanding- This involves RNNs or Transformers that segment scenes based on narrative context using algorithms like SceneDetect. Speech Recognition & NLP methods are also used to transcribe speech to highlight key dialogues.

4. Task‑Specific Improvements- These include auto-trimming, which removes stagnant segments by analyzing audio and motion. Style transfer and color grading apply cinematic styles using GANs or diffusion models. Tools like Stable Video Diffusion or GEN-2 from RunwayML enable easy inpainting and background removal. Finally, super-resolution and deblurring techniques sharpen details in low-resolution videos.

5. Rendering & Export- The tool then processes and blends the frames, adds transitions and subtitles, and encodes the final video in the target format.

These refined outputs with little to no human intervention make editing efficient and flexible. But what builds a strong foundation to develop such tools? It is Convolutional Neural Networks (CNNs).

CNN: Brain Behind AI Video Editors

“Yann LeCun’s strategy provides a good example of a much more general notion: the exploitation of innate knowledge. Convolutional neural networks learn better and faster than other types of neural networks because they do not learn everything. They incorporate, in their very architecture, a strong hypothesis: what I learn in one place can be generalized everywhere else.”- Stanislas Dehaene (Neuroscientist)

CNNs, inspired by the human visual cortex, are the backbone of frame-level perception in AI video editors today.

How CNNs Work?

• Convolutional layers employ filters (kernels) that move across video frames to identify key features like edges, shapes, and colors.

• Pooling layers then reduce the data size while maintaining important visual patterns.

• Fully connected layers use the extracted features to label objects or predict behaviors, such as identifying a car or recognizing someone waving.

• Finally, 3D convolutions apply CNNs in the time domain, enabling motion perception across stacked frames, which is useful for tasks like tracking and action recognition.

Through these features, AI editors can perform advanced tasks like smart cropping, background removal, motion stabilization for smooth playback, scene classification for labeling clips by visual themes, and privacy filters that blur faces or license plates to protect privacy.

Why do AI video editors use CNNs?

Three reasons: accuracy, efficiency, and hierarchical learning. They can achieve pixel-perfect precision for tasks like green-screen removal while reducing computational demands by sharing parameters. CNNs also build complex objects from simple edges and can automatically generate B-roll based on scene detection, such as “sunset” or “cityscape,” as well as blur moving license plates, transforming raw video into editable stories.

While Transformer models currently dominate long-range video understanding, CNNs are still utilized for their speed, accuracy, and capacity to learn visual hierarchies.

Architectures in AI Video Editors

Along with CNN at its core, these editors also implement advanced models to enhance video editing, focusing on recognizing, segmenting, and transforming scenes. These are bifurcated as follows:

• Scene Detection: Tools such as PySceneDetect identify cuts and fades by comparing frames, which helps break long videos into meaningful sections.

• Segmentation Models: Vision models like DeepCut and Meta’s SAM isolate objects in frames. SAM allows for precise edits like selective blur and color correction.

• Video-to-Video Generators: Unlike models that create new frames, NVIDIA’s Vid2Vid uses semantic maps and past frames to maintain the visual structure without distortion.

• Tracking & NLP Modules: Models like RoughCut sync transcripts with video and tag sequences for actions or emotions, assisting in smart cuts.

These models are shown to improve editing by using recognition for precise control instead of free form, like AI video generators.

Study Spotlight- Adobe AI Video Editor

Adobe’s AI features in Premiere Pro and Adobe Express illustrate how automation enhances creative control. By using deep learning techniques like CNNs, Adobe Sensei, and Firefly Video provides targeted improvements without needing to rework entire frames.

Its core capabilities are-

• Auto-Reframing: Detects key subjects and automatically adjusts videos to landscape, portrait, or square formats.

• Scene Edit Detection: Identifies cuts, fades, and dissolves to help you jump to important parts.

• Smart Color Matching: Adjusts exposure and white balance for consistent colors with minimal user input.

• Audio Workflows: Offers real-time captions and clear dialogue by using speech-to-text and noise cancellation to remove filler words.

With its Firefly video & generative extensions, it offers-

• Clip Extension- New frames are generated by Firefly to extend existing footage while maintaining the current motion paths and lighting conditions through temporal analysis.

• Object Addition and Removal- AI-generated masks can track moving objects, enabling you to remove distractions such as microphones or boom poles, or naturally add graphics to your scenes.

• Text-to-Video B-Roll- Transform simple prompts (e.g., “add a misty forest background”) into short animations that align with your scene’s aesthetic.

Adobe also allows you to connect third-party models, like Runway and OpenAI Sora, directly into your timeline. The tool adds content credentials with clear metadata, so you can track and prove all AI changes. Adobe’s AI video editor thereby enables creators to focus more on pacing, narrative, and artistic vision.

Final Scene:

Understanding complex technology requires time and patience. I hope my attempt to explain the basics of AI-based video editors was sufficient.

These automated editors are constantly evolving, but they reduce repetitive tasks and ease manual effort. While concerns about creativity and originality persist, these tools offer precision and efficiency- empowering creators to tell sharper stories with fewer hurdles.

A takeaway question is- Will AI just enhance our stories, or share its story at the center stage?

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Suggestions:

1. Convolutional Networks for Images Speech and Time Series (Yann LeCun, Yoshua Bengio, 1995)

2. AI video editing tools. What editors want and how far is AI from delivering? (Than Htut Soe, 2021)

Disclaimer:

Backlinks provided within this blog are intended for the reader’s further understanding only. My personal experiences and research serve as the base for the content of this blog. Despite my best efforts to keep the content current and correct, not every situation may necessarily benefit from it. Images utilized in this blog are created using Canva and SocialBlu. While making any crucial life decisions, please consult professional advice or conduct independent research. This blog does not intend to be a substitute for expert guidance; it is solely meant to be informative.