Runway

Enables multiple users to edit video projects simultaneously with live cursor tracking, synchronized timeline scrubbing, and conflict-free concurrent edits through operational transformation or CRDT-based synchronization. Changes propagate across connected clients with sub-second latency, maintaining a single source of truth for project state while supporting simultaneous modifications to different timeline segments, effects, and metadata.

Generates video sequences from natural language descriptions using diffusion-based video models fine-tuned on cinematic footage, with support for style transfer to match reference videos or predefined aesthetic templates. The system tokenizes text prompts, encodes them through a CLIP-like text encoder, and uses a latent diffusion model to iteratively denoise video frames while conditioning on the encoded prompt and optional style embeddings from reference material.

Provides multi-track audio editing with AI-powered voice isolation using source separation models that decompose audio into speech, music, and ambient noise components. Allows independent editing of each component (e.g., removing background noise, adjusting voice volume, replacing music) with real-time preview. Includes voice enhancement (noise reduction, clarity boost) and automatic audio synchronization across video and audio tracks.

Provides frame-level editing controls with automatic object tracking across frames using optical flow and deep learning-based segmentation. When a user selects and modifies an object in one frame (e.g., removing, recoloring, or repositioning), the system tracks that object's position and appearance across subsequent frames and applies consistent transformations, reducing manual keyframing work. Supports mask propagation, motion interpolation, and automatic inpainting for removed objects.

Uses semantic segmentation models (trained on diverse video/image datasets) to identify and isolate foreground subjects from backgrounds with pixel-level precision. The system can remove backgrounds entirely (transparency), replace with solid colors, blur, or swap with uploaded images or AI-generated backgrounds. Segmentation runs on GPU with real-time preview, supporting both static images and video sequences with temporal consistency to prevent flickering.

Extracts 2D/3D skeletal pose data from video using deep learning-based pose estimation models (e.g., OpenPose-style architectures or transformer-based models). Detects joint positions, bone angles, and movement trajectories across frames, then exports as rigged skeletal data compatible with animation software (BVH, FBX formats). Supports multi-person detection and can drive 3D character rigs or generate animation curves for keyframe-based animation.

Upscales low-resolution video to higher resolutions (e.g., 480p → 1080p, 1080p → 4K) using deep learning-based super-resolution models trained on natural video datasets. Applies temporal consistency constraints across frames to prevent flickering and maintain coherent motion, using optical flow alignment and recurrent neural networks that process frame sequences rather than individual frames. Supports multiple upscaling factors and quality presets.

Applies professional color grading to video using neural style transfer from reference images or predefined cinematic LUTs (Look-Up Tables). The system analyzes color distribution, contrast, and tone curves in reference material, then generates a color transformation that matches the target aesthetic. Can generate custom LUTs compatible with standard video editing software, or apply grading directly to video with adjustable intensity and per-shot customization.

Generates images from text prompts using latent diffusion models with support for style, composition, and aesthetic conditioning through reference images, style codes, or predefined templates. The system encodes text through a CLIP-like encoder, optionally encodes reference images for style guidance, and iteratively denoises a latent representation to produce images. Supports inpainting (editing specific regions) and outpainting (extending image boundaries) with seamless blending.

Processes multiple videos in parallel using distributed cloud GPU infrastructure, queuing jobs and distributing them across available compute resources. Supports batch operations like upscaling, background removal, color grading, or motion capture across hundreds of videos with automatic resource allocation, progress tracking, and error handling. Results are stored in cloud storage with download links or direct integration to external storage (S3, Google Drive).

Converts screenplay or script text into visual storyboards by generating key scene images from scene descriptions, maintaining visual consistency across scenes through character and location embeddings. The system parses script structure, extracts scene descriptions, generates images for each scene using text-to-image models conditioned on character/location consistency tokens, and arranges them in storyboard layout with optional shot descriptions and timing annotations.