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HuMo - A multimodal video generation framework jointly launched by Tsinghua University and ByteDance

HuMo is a multimodal video generation framework jointly proposed by Tsinghua University and ByteDance's Intelligent Creation Lab, focusing on human-centered video generation. It can generate high-quality, detailed videos from multiple modal inputs, including text, images, and audio...

What is HuMo?

HuMo is a multimodal video generation framework jointly proposed by Tsinghua University and ByteDance's Intelligent Creation Lab, focusing on human-centered video generation. It can generate high-quality, detailed, and controllable human-like videos from multiple modal inputs, including text, images, and audio. HuMo supports powerful text cue following capabilities, consistent subject preservation, and audio-driven motion synchronization. It supports video generation from text-image, text-audio, and text-image-audio sources, providing users with greater customization and control. HuMo's model is open-source on Hugging Face, providing detailed installation guides and model preparation steps. It supports 480P and 720P video generation, with 720P offering higher quality. HuMo provides configuration files to customize generation behavior and output, including generation length, video resolution, and the balance between text, image, and audio inputs.

HuMo's main functions

  • Text-image driven video generationCombine text prompts and reference images to customize the character's appearance, clothing, makeup, props, and setting to generate personalized videos.
  • Text-audio driven video generationIt generates audio-synchronized videos using only text and audio input, without the need for image references, providing greater creative freedom.
  • Text-Image-Audio Driven Video GenerationIt integrates text, images, and audio guidance to achieve the highest level of customization and control, generating high-quality videos.
  • Multimodal collaborative processingIt supports strong text prompts, preservation of subject consistency, and audio-driven action synchronization, enabling collaborative driving of multiple modal inputs.
  • High-resolution video generationIt is compatible with 480P and 720P resolutions, with 720P producing higher quality output to meet the needs of different scenarios.
  • Customized configurationBy modifyinggenerate.yamlThe configuration file allows you to adjust the output length, video resolution, and the balance of text, image, and audio inputs to achieve personalized output.

HuMo's technical principles

  • Multimodal collaborative inputHuMo can process input in three modalities simultaneously: text, images, and audio. Text provides specific descriptions and instructions, images serve as references to define the character's appearance, and audio drives the character's movements and expressions, making the generated video content more natural and vivid.
  • Unified Generative FrameworkThe framework generates human-centric videos by coordinating multimodal conditions (text, images, and audio). It integrates information from different modalities to achieve richer and more refined video generation effects, rather than simply generating videos from a single modality.
  • Powerful text following capabilitiesHuMo can precisely follow text prompts, translating 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 consistency of the generated video.
  • Consistent subject retentionDuring video generation, HuMo maintains consistency in the subject. Even across multiple frames, the appearance and features of the character remain stable, avoiding the inconsistencies that often occur between frames in common generative models.
  • Audio-driven motion synchronizationAudio input is used to generate background sound, which can drive the character's movements and expressions. For example, the character can make corresponding movements or expressions based on elements such as rhythm and tone in the audio, making the video content more vivid and realistic.
  • High-quality dataset supportHuMo's training relies on high-quality datasets containing rich text, image, and audio samples. High-quality datasets help the model learn more accurate relationships between modalities, generating higher-quality video content.
  • Customizable build configurationThrough configuration files, users can adjust various parameters of the generated video, such as frame rate, resolution, and the intensity of text and audio guidance. This customizability allows 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 libraryhttps://huggingface.co/bytedance-research/HuMo
  • arXiv technical paper: https://arxiv.org/pdf/2509.08519

HuMo's application scenarios

  • Content creationIt is used to generate high-quality video content, such as animations, advertisements, and short videos, helping creators quickly realize their creative ideas.
  • Virtual Reality and Augmented RealityTo create immersive virtual environments and provide users with a more realistic and vivid experience.
  • Education and TrainingGenerate educational videos that use vivid animations and audio explanations to help students better understand and learn complex concepts.
  • Entertainment and GamesGenerate character animations in game development, or create personalized virtual characters in entertainment applications.
  • social mediaGenerate personalized and engaging video content for social media platforms to increase user engagement.
  • Advertising and MarketingCreate personalized advertising videos and generate customized content based on the preferences of the target audience to improve advertising effectiveness.
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