FAMILIAR RESEARCH
Measured, then published.
THE DUBBING BENCHMARKS · THREE STUDIES · UPDATED 2026-08-08
Familiar vs ElevenLabs, YouTube, and human translators.
418 + 111 paired outputs vs ElevenLabs Dubbing v1 and Dubbing v2 (Alpha); a frozen 50-pair cohort vs YouTube auto-dubbing; scored blind against first-pass expert professionals.
Open the benchmarks →MEASURED
Familiar vs ElevenLabs Dubbing: Two Paired Studies
418 paired outputs across 11 languages on the ElevenLabs Dubbing v1, 111 more on its newest Dubbing v2 (Alpha): meaning, voice, scene audio, and price, measured on the same clips.
Familiar vs YouTube Auto-Dubbing: A Frozen-Cohort Study
50 paired dubs across Arabic, Spanish, French, Hindi, and Korean, frozen August 8, 2026: speaker resemblance +182.9%, 70.3% fewer important ideas changed or lost, and the scene kept, with six synchronized listening examples.
AI vs Human Translation: Tested Against Professionals (2026)
Familiar vs first-pass expert professional translators, scored blind against expert reference edits: 100.3% of the professional's quality, averaged across French, Chinese, Hindi, and Indonesian.
ElevenLabs Dubbing Review: What We Measured
A measured review of ElevenLabs Dubbing, Dubbing v1 and Dubbing v2 (Alpha): meaning errors, voice drift, scene audio loss, workflow limits, and price, with full data.
YouTube Auto-Dubbing Review: What We Measured (2026)
A measured review of YouTube's auto-dubbing on a frozen 50-pair cohort: a stranger's voice (0.141 resemblance), 27.5% of important ideas changed or lost, the background scene flattened, and availability on 25 of 43 clips.
The Best AI Dubbing in the World: How to Measure It
"Best dubbing" is measurable: meaning preserved, voice cloning, scene audio, timing, coverage, with the current numbers on each.
PLAYBOOKS
What Is the Best AI Dubbing for Movies?
The best AI dubbing for a movie keeps the performance: each actor's own voice, a mouth that matches the words, and the score intact, for every speaker in the scene. Measured.
Why the Dub Feels Off Abroad, and How Studios Fix It
A stranger's voice, a mouth speaking the wrong language, a flattened scene: why dubs feel off abroad, and how a studio fixes all three, measured.
How Streaming Platforms Close the Local-Language Gap
Dub on upload, review per language, release when it clears: how streaming platforms give every region the same quality, in 30 languages.
Dubbing Micro-Dramas on a Daily Release Schedule
Locked episode in, every language out the same day: dubbing micro-dramas at daily cadence with the actors' own voices and names that never drift.
Multi-Country Launches: Dubbing Every Market at Once
One request, every target language, each market released on its own clock: dubbing for simultaneous launches without adding a vendor per country.
Dubbing for Creator Networks: One Workflow, Every Creator
One profile per creator, one workflow for the network: how multi-channel networks dub many creators' videos with per-creator Do Not Translate lists and context.
How to Run an AI Dubbing Pilot (2026)
Paired clips, native reviewers, acceptance criteria written first: how to run an AI dubbing pilot on your own footage, and what to measure.
GUIDES
Human to Human, Not AI to AI
Why quality content will stay human: you can't empathize with an AI human, star power doesn't transfer, and a dub carries the performance. The CEO's view.
Best Alternatives to ElevenLabs for Dubbing (2026)
Why dubbing buyers outgrow ElevenLabs (audio-only output, an 11-second minimum, 20-item batches, 30 concurrent requests), and the alternatives, compared with measurements.
Familiar vs Flawless TrueSync: Full AI Dubbing vs Assistive Visual Dubbing (2026)
One shot, not weeks: Flawless TrueSync is assistive visual dubbing, a tool a team of VFX artists operates for weeks per title and per language, while Familiar renders the whole dub from the finished title in one shot, up to 4K in true 5.1.
Assistive AI Visual Dubbing vs Full AI Dubbing: How Titles Get Localized (2026)
One shot, not weeks: assistive AI visual dubbing syncs an actor's face to a dub you recorded and needs a team of VFX artists for weeks per project, while full AI dubbing renders the translation, the actor's own voice, the scene audio and the face from the finished title.
Video Translation: The Complete Guide (2026)
Subtitles, voice-over, dubbing, and voice + lip-sync dubbing compared: quality, cost per minute, turnaround, and when each is the right call, with the full dub back up to 4K in true 5.1.
AI Video Dubbing, Explained (2026)
What AI video dubbing is, what voice + lip-sync dubbing delivers up to 4K in true 5.1, why most dubs sound like a stranger, and what to check before you trust one with your catalog.
AI Lip Sync: What It Is, What It Misses, What Beats It (2026)
AI lip sync moves the mouth; dubbing that holds up moves the voice, the whole face, and the scene: what lip sync is, where it falls short, and the measured numbers for the full approach.
How Much Does Dubbing Cost in 2026?
Published rates, compared: full human dubbing at $70 to $150 per finished minute, out of reach for most of the world; ElevenLabs at a $3 checkout quote; what one Familiar render includes, up to 4K in true 5.1.
What Is a Multimodal Localization Decision Model (MLDM)?
MLDM is one model that decides what to localize and how, across text, audio and visual, and keeps the person exactly who they are. The definition, what it decides, and why one model.
The First Foundation Model for Multimodal Translation
What a foundation model for multimodal translation is, why dubbing went through separate tools until now, and what one model that carries the words, the voice, the lips and the scene at once changes.
Audio-Visual Translation at Once, Not Cascaded
Machine dubbing was a cascade: transcription, then translation, then a voice, then lip-sync. What changes when one model makes every decision together: the translation knows the mouth, the voice knows the scene, the person never changes.
What a Foundation Model for Multimodal Translation Means for Studios and Platforms
For the buyer: every language from one upload, the translation reviewed first, every decision recorded, the scene audio kept, up to 4K with a true 5.1 mix, private inference on our own cluster, subtitles to the Netflix style guide.
The Decision Record: How Creative Directors Direct the MLDM
Every decision the MLDM makes is recorded with the dub: the renderings reviewed today, the audio and visual choices in the works, the override, and the choice that follows the next episode.
Why Familiar Is Not Traditional Generative AI
Generative AI makes new content from a prompt; Familiar has no prompt, and your video comes back as the same performance in another language.
Translation Is Not Generation
Generate means a prompt in and new content out; translate means a performance in and the same performance out, in another language.
Another Tool for the Creative Process
How a creative team works with Familiar: the tedious part automated, every creative decision kept with the team.
Why Most of the World Was Never Dubbed
Human dubbing costs too much, needs too many people and takes too long for most of the world; what changes when the tedious part is machine learning.
The Same Performance for Every Audience
Every region deserves what the original audience got: the same actor, the same performance, the same timing, in its own language.
Machine Learning vs Generative AI: A Plain-English Guide for Studios
Plain definitions, the questions to ask any vendor, and Familiar's answers: no prompt, nothing invented, the actor's own voice and face with consent.
THE LAB
What Matters in Video Translation
Natural translation, natural audio, on-screen text, lip-sync: four problems that are one problem -- the same performance, in another language -- down to the McGurk effect.
The Research Behind Familiar: The Published Papers
22 papers (18 peer-reviewed) from the team behind Familiar: joint audio-visual human models, fast video models and video editing, grouped by what each area contributes to multimodal translation.
Pioneering Multimodal Translation: From Joint Audio-Visual Research to the First Foundation Model
The research lineage behind Familiar, the team that started the field of joint audio-visual human models, and the step from the papers to the first foundation model for multimodal translation.