Tavusv4Version updatev4Feb 18, 2026Phoenix 4
Unifies emotional state control, active listening behavior, and continuous facial motion in one real-time system.
Runs full-duplex, meaning it listens and responds simultaneously instead of taking turns.
Generates behavior continuously with millisecond-level latency for live conversation responsiveness.
Expands explicit control over how emotion is expressed during both speaking and listening states.
Trains on large-scale human conversation data to learn realistic non-verbal conversational behavior at runtime.
Overview
Tavus is a developer-focused AI video research tool that facilitates the creation of AI replicas within applications. Through Tavus easy-to-use APIs, users can generate personalized videos of themselves from text.
This eliminates the need for high-cost, high-complexity recording of studio-grade videos. Tavus also boasts of bypassing traditional methods and generating hyper-realistic talking-head videos with natural face movements and expressions.
Beyond this, Tavus also pays key attention to security and video model handling. In support of a smoother development cycle, Tavus provides comprehensive, easy-to-understand documentation along with responsive support through the build and launch process.
As an AI video tool, Tavus incorporates features like AI voice cloning, AI HD lip sync, unlimited audio variables, dynamic video backgrounds, custom branding, and embeddable CTAs.
This enables more stable and scalable AI video creation. The Tavus APIs can be used across a wide range of industries such as video editing tools, influencer apps, video sales apps, and education sectors among others.
Supported features
Releases
Unifies emotional state control, active listening behavior, and continuous facial motion in one real-time system.
Runs full-duplex, meaning it listens and responds simultaneously instead of taking turns.
Generates behavior continuously with millisecond-level latency for live conversation responsiveness.
Expands explicit control over how emotion is expressed during both speaking and listening states.
Trains on large-scale human conversation data to learn realistic non-verbal conversational behavior at runtime.
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