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HuMo - A Multimodal Video Generation Framework Jointly Launched by Tsinghua University and ByteDance

Author: AI小集 · Original source: AI工具集

What is HuMo

HuMo is a multimodalvideo generationframework jointly proposed by Tsinghua University and the ByteDance Intelligent Creation Laboratory, focusing on human-centric video generation. It can generate high-quality, detailed, and controllable human videos from multiple modal inputs such as text, images, and audio. HuMo supports strong text prompt following capability, consistent subject preservation, and audio-driven motion synchronization. It supports video generation from text-image, text-audio, and text-image-audio, providing users with higher customization and control capabilities. HuMo's model is open-sourced on Hugging Face, providing detailed installation guides and model preparation steps, supporting video generation at 480P and 720P resolutions, with 720P offering higher generation quality. HuMo provides configuration files to customize generation behavior and output, including generation length, video resolution, and the balance of text, image, and audio inputs.

HuMo

Main Features of HuMo

  • Text-Image Driven Video Generation: Combines text prompts and reference images to customize the character's appearance, clothing, makeup, props, and scenes, generating personalized videos.
  • Text-Audio Driven Video Generation: Generates videos synchronized with audio using only text and audio inputs, without the need for image references, providing greater creative freedom.
  • Text-Image-Audio Driven Video Generation: Integrates text, image, and audio guidance to achieve the highest level of customization and control, generating high-quality videos.
  • Multimodal Collaborative Processing: Supports strong text prompt following, subject consistency preservation, and audio-driven motion synchronization, enabling collaborative driving of multiple modal inputs.
  • High-Resolution Video Generation: Compatible with 480P and 720P resolutions, with 720P offering higher generation quality, meeting the needs of different scenarios.
  • Customized Configuration: By modifying thegenerate.yamlconfiguration files, you can adjust the generation length, video resolution, and the balance of text, image, and audio inputs to achieve personalized output.

Technical Principles of HuMo

  • Multimodal Collaborative Input: HuMo can simultaneously process inputs from three modalities: text, image, and audio. Text is used to provide specific descriptions and instructions, images serve as references to define the character's appearance features, and audio is used to drive the character's actions and expressions, making the generated video content more natural and vivid.
  • Unified Generation Framework: The framework generates human-centric videos by coordinating multimodal conditions (text, image, audio). It integrates information from different modalities to achieve richer and more detailed video generation effects, rather than simple generation from a single modality.
  • Strong Text Following Capability: HuMo can accurately follow text prompts and transform the content described in the text into visual elements in the video. This means users can control the content and style of the video through detailed text descriptions, improving the accuracy and conformity of the generated video.
  • Consistent Subject Preservation: During the video generation process, HuMo can maintain the consistency of the subject. Even in multi-frame videos, the character's appearance and features remain stable, avoiding the common problem in generation models where the subject appears inconsistent across different frames.
  • Audio-Driven Motion Synchronization: Audio input is used to generate background sound and can drive a character's actions and expressions. For example, the character can make corresponding actions or expressions based on elements such as rhythm and intonation in the audio, making the video content more vivid and realistic.
  • High-Quality Dataset Support: HuMo's training relies on high-quality datasets, which contain rich text, image, and audio samples. High-quality datasets help the model learn more accurate relationships between modalities and generate higher-quality video content.
  • Customizable Generation Configuration: Through configuration files, users can adjust various parameters for generating videos, such as the number of frames, resolution, and text and audio guidance strength. Customizability enables HuMo to adapt to different application scenarios and user needs.

HuMo's Project Address

  • Project Official Website：https://phantom-video.github.io/HuMo/
  • HuggingFace Model Library：https://huggingface.co/bytedance-research/HuMo
  • arXiv Technical Paper：https://arxiv.org/pdf/2509.08519

HuMo's Application Scenarios

  • Content Creation: Used to generate high-quality video content, such as animations, advertisements, short videos, etc., helping creators quickly realize creative ideas.
  • Virtual Reality and Augmented Reality: Create immersive virtual environments to provide users with a more realistic and vivid experience.
  • Education and Training: Generate educational videos that, through vivid animations and audio explanations, help students better understand and learn complex concepts.
  • Entertainment and Gaming: Generate character animations in game development, or create personalized virtual characters in entertainment applications.
  • Social Media: Generate personalized and attractive video content for social media platforms to increase user engagement.
  • Advertising and Marketing: Produce personalized advertising videos, generate customized content according to the preferences of the target audience, and improve advertising effectiveness.