The term “real-time live AI video” is often used imprecisely, creating confusion for use cases in which only true real-time AI video will do. This article explains the differences between genuine live streaming AI video and pale imitations of it, and gives an overview of some of the few companies that actually deliver it.

What You Will Learn

• True real-time video is vital for use cases like gaming, adding special effects to live event broadcasts, live avatars, and physical AI training.
• Genuine live AI streaming video must be continuous, lag-free, and at least 30 FPS.
• Many companies that claim real-time AI video actually only provide near-real time. These include Pika, HeyGen, and Synthesia.
• Decart is one of the very few companies to deliver true live, continuous, latency-free AI video. DeepFaceLive, NVIDIA (News - Alert) and Viggle have likewise created impressive models in this context.

Real-time AI video is the latest AI-powered achievement that’s fizzing up excitement in the frontier tech ecosystem. But like many buzzwords, there’s a lot of misunderstanding about what it entails, to the extent that some disillusioned AI devotees have concluded that it isn’t really a thing.

It’s not a surprising conclusion, given that companies like Pika and Synthesia trumpet their live AI video offering without actually delivering it. However, Decart and Viggle Live demonstrate that there is such a thing as real-time, consumer-accessible AI streaming video, and Kling and HeyGen are very close to mirroring their achievements.

Let’s take a closer look at what experts mean when they talk about live or real-time AI video, the mistakes that creep into the topic, and the solutions that truly deliver continuous, low-latency AI video streams.

When Only Real-Time AI-Powered Video Will Do

Some situations require truly real-time, continuous AI video. Hyper-fast “near-real-time” AI video isn’t good enough for use cases like:

• Real-time changes to live avatars and backgrounds, lighting, eye contact, or style correction in conferences and online classes
• Instantly rendering players as characters and enhanced characterization in gaming; enriched visuals in AR, VR, and mixed reality; and hyper-personalized, responsive game experiences
• Immediate special effects, stylization, filters, or replacing objects and backgrounds for live streaming and virtual try-on experiences
• Realistic, adaptive learning scenarios for medical, military, industrial and other physical AI model training
• Live accessibility augmentations and synced lip movement and facial expressions for real-time translation and dubbing
• Convincing eye contact, face, body, or character rendering for video conferencing and virtual presenters in entertainment contexts

What Real-Time AI Video Actually Is

The term “real-time video” is frequently abused by marketers. Many companies apply it to extra-fast video editing, buffered video with delayed frames, turn-based video generation, and/or prerendered and event-triggered video.

Some tools that tout “live AI video” actually offer only real-time subtitles or captions, or just real-time collaboration. Few of them can deliver continuous streaming without gaps, frame-synchronous video without lags, and low-latency processing without buffering.

Real-time AI video requires:

• Processing times of at least ~30 FPS (frames per second)
• Under 33ms per frame processing speed, to keep up with video running at 30 FPS
• Low end-to-end latency with no perceivable sense of drag

Common pseudo-real-time AI video claims include:

• Near-real-time generation with low latency of a few seconds, delivering only 1-10 FPS. For example, Runway and Pika Labs support rapid prompting for quick video output in seconds, but not continuous streaming.
• Buffered “live” AI video streams where platforms add a few seconds delay so that AI can process the frames before displaying them.
• Frame chunking or micro-batch processing, where video is processed in chunks instead of continuously. NVIDIA video analytics pipelines often do this, despite presenting as real-time.
• Pre-rendered or stitched AI avatars. Synthesia and HeyGen are among platforms that use prebuilt segments to create “live” video personas, but they can’t respond dynamically to user input.
• Pregenerated video responses triggered in real time, where systems select prerendered clips in response to inputs.
• Human-assisted AI video pipelines, with humans in the loop for content moderation, avatar responses, or live dubbing.
• Segment-based AI video. Platforms like YouTube (News - Alert) and Twitch generate “live highlights” which are processed very quickly, but only after the segment is completed, not as it is being aired.
• Asynchronous audio-video AI processing that processes audio and video separately, then recombines them. It’s common in lip-sync, dubbing, or translation systems.

These kinds of claims cause many people to consider that real-time AI video has no objective meaning. But it does. In real-time AI video, three stages run concurrently:

• Decoding the compressed video stream and converting it to tensor format
• Analyzing the decoded frames, using ML models for detection, classification, segmentation, and tracking the video images
• Acting to process the model outputs and store the results

Between them, these three stages require high CPU and GPU availability and high speeds. It’s crucial that no stage blocks the pipeline, because work must be completed on each frame before the next one arrives ~33ms later. Current systems often fall short due to compute constraints, but some do succeed.

The Real Live AI Video Solutions

• Decart’s Lucy and Oasis. Lucy 2 is a real-time video-to-video AI editing system that changes specific elements like outfits, backgrounds, props, and character details on a live video feed while preserving lighting, composition, motion, and structure. Oasis is a world model that generates persistent, consistent, dynamic 3D environments in response to user prompts and interactions.
• NVIDIA’s Maxine delivers continuous, natural-looking eye contact, animating static images into live avatars in real time. It also edits audio and video content live, including noise reduction, denoising, and super resolution.
• DeepFaceLive can swap one person’s face with another face instantly and convincingly during live video streaming, maintaining consistency and realism.
• Viggle Live turns any webcam into a live AI video generator, transforming images into animated characters that mirror the user’s movements and facial expressions. It integrates directly with Twitch, YouTube, and Kick to support live meme generation.
• xpression camera is a virtual camera app that replaces the user’s face with any image they like, including other people’s faces, cartoon characters, dolls, and more. It serves as a camera source to deliver live AI video to other apps.

Real-Time AI Video Generation Isn’t a Myth

The truth is that continuous streaming video generation does exist. But it’s crucial to choose carefully when you select a real-time AI video solution, because many companies that dangle the promise of live AI video aren’t offering the real deal. 

FAQs

What does “real-time AI video” actually mean in practice?

In practice, real-time AI video means AI video that is generated continuously, frame by frame, at speeds of 30 FPS or no slower than approximately 30ms. For example, Decart’s Lucy 2 live AI video editor performs at near-zero latency and ~30 FPS.

How is real-time AI video different from fast or near-real-time video generation?

Real-time AI video doesn’t have any perceptible lags, and it’s rendered immediately and continuously rather than in chunks. Fast or near-real-time video generation can be batch rendered, involve human input, include gaps or skips, and have lags that the viewer can perceive.

What technical requirements define true real-time AI video?

Technically, true real-time AI video runs at 30 FPS or faster, with processing speeds of under 16ms. Each frame must be processed in order, without batching or chunking, and end-to-end latency must be very low.

Why is real-time AI video so difficult to achieve?

Delivering real-time AI video requires solving all these challenges at the same time:

• Hyper-low latency under ~16ms
• Infinite (News - Alert) temporal consistency
• Continuous generation
• Interactive input

Obstacles include model inference, a long processing pipeline, geographic distance, and congested infrastructure. Decart’s models, for example, deliver output with slashed latency by partnering with Comcast (News - Alert) and NVIDIA on a distributed, dedicated AI grid.