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MoCha - Meta, in collaboration with the University of Waterloo, presents an end-to-end dialogue role-playing video generation model.

MoCha AI is an end-to-end dialogue-based character video generation model jointly developed by Meta and the University of Waterloo. It can generate complete character animations with synchronized speech and natural movements based on text or speech input. MoCha employs a speech-view...

What is MoCha?

MoCha AI is an end-to-end dialogue-based character video generation model jointly developed by Meta and the University of Waterloo. It can generate complete character animations with synchronized speech and natural movements based on text or speech input. MoCha employs a speech-video window attention mechanism to solve the problems of audio resolution mismatch and lip movement misalignment during video compression. It supports multi-character turn-based dialogue and can generate character animations with emotional expression and full-body movements.

MoCha's main functions

  • Voice-driven character animation generationWhen a user inputs voice, MoCha can generate lip movements, facial expressions, gestures, and body movements that are synchronized with the voice content.
  • Text-driven character animation generationUsers only need to input a text script, and MoCha will automatically synthesize the speech first, and then drive the character to perform complete lip movements and actions.
  • Full-body animation generationUnlike traditional models that only generate facial expressions or mouth shapes, MoCha can generate natural full-body movements, including lip synchronization, gestures, and interactions between multiple characters.
  • Multi-character turn-based dialogue generationMoCha provides structured prompt templates and character tags, automatically recognizing dialogue turns and enabling natural back-and-forth conversations between characters. In multi-character scenarios, users only need to define character information once and can reference these characters in different scenarios using simple tags (such as "Character 1" and "Character 2"), without needing to repeat descriptions.

MoCha's technical principles

  • Diffusion Transformer (DiT) ArchitectureMoCha, based on the Diffusion Transformer architecture, effectively captures semantic and temporal dynamics by sequentially incorporating text and speech conditions into the model through a cross-attention mechanism. It can generate realistic and expressive full-body movements while ensuring precise synchronization between character animation and input speech.
  • Voice-video window attention mechanismTo address the speech-video alignment issues arising from video compression and parallel generation, MoCha introduces a speech-video window attention mechanism. This mechanism restricts each video marker to focusing only on audio markers that are temporally adjacent to it, improving the accuracy of lip-syncing and speech-video alignment. It simulates the operation of human speech, ensuring precise matching between the character's lip movements and the dialogue content.
  • Joint training strategyMoCha employs a joint training strategy, training on both speech-annotated and text-annotated video data. This enhances the model's generalization ability across diverse character actions, enabling fine-grained control over character expressions, movements, interactions, and the environment through natural language cues.
  • Structured prompt templateTo simplify the text description of multi-character dialogues, MoCha designed a structured cue template. This template assigns a unique label to each character, which is then used in the text to describe their actions and interactions. This reduces redundancy and improves the model's generation performance in multi-character scenarios.
  • Multi-stage training frameworkMoCha employs a multi-stage training framework, classifying data according to shot type (such as close-up and medium shot) and gradually introducing more complex tasks. This ensures the model's performance on tasks of varying difficulty while improving training efficiency.

MoCha's project address

MoCha Application Scenarios

  • Virtual streamerMoCha can automatically generate daily vlogs, Q&A sessions, and other content. Through voice or text input, it generates synchronized lip movements, facial expressions, gestures, and body movements, making the virtual anchor more vivid and natural.
  • Animation and film creationMoCha supports AI-powered automatic dubbing and animation generation, reducing production costs for animated films and television shows. It can generate full-body animations, making character movements more natural and approaching cinematic digital human performance.
  • Educational content creationMoCha can function as an AI teacher, delivering lessons and engaging in interactive activities. Through text-driven methods, it generates character animations that match the teaching content, enhancing the fun and appeal of educational materials.
  • Digital Human Customer ServiceMoCha can be used to anthropomorphize enterprise customer service and consultation roles. It generates natural and fluid customer service dialogue animations through voice or text input, enhancing the user experience.
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