OmniHuman: Human Video Generation with Multimodal Conditioning

In the evolving landscape of AI-driven video synthesis, OmniHuman, an advanced end-to-end multimodality-conditioned human video generation framework. Unlike previous approaches that struggled with data scarcity and quality limitations, OmniHuman excels at generating highly realistic human videos from minimal input signals, such as a single image and motion cues (audio, video, or a combination of both).

OmniHuman incorporates a groundbreaking multimodality motion conditioning mixed training strategy, which enables the model to leverage an extensive dataset of diverse conditioning inputs. This innovative training methodology enhances the model's ability to generalize across various scenarios, ensuring superior realism in generated videos. The system effectively overcomes the constraints faced by earlier models, offering lifelike motion synthesis, accurate texture representation, and natural lighting effects.

OmniHuman significantly outperforms existing methods, producing hyper-realistic human videos even from weak input signals—particularly audio. The model is designed to accommodate images of any aspect ratio, including portrait, half-body, and full-body, while maintaining a high degree of detail and authenticity in motion reproduction.

Generated Videos with OmniHuman

OmniHuman supports a broad range of visual and audio styles, ensuring realistic human video generation across different body proportions. The realism stems from a comprehensive understanding of motion dynamics, lighting, and intricate texture details. Whether generating singing, talking, or expressive gestures, the system consistently delivers top-tier results.

Singing Videos: Expressive and Lifelike Performance

OmniHuman can generate singing videos with remarkable accuracy, handling diverse music styles, body poses, and singing forms. It adapts seamlessly to variations in pitch and movement, ensuring the generated video aligns naturally with the input audio.

Talking Videos: Enhanced Gesture Control

Speech-based video generation has traditionally struggled with natural gesture reproduction. OmniHuman significantly improves in this area, producing fluid, expressive hand and facial movements synchronized with speech input. The model supports input images of any aspect ratio, making it highly versatile for different use cases.

Diversity: Beyond Human Videos

Beyond realistic human figures, OmniHuman extends its capabilities to include cartoons, artificial objects, animals, and unique body poses. This ensures that the generated motion aligns with the distinctive characteristics of each style, expanding the possibilities for creative content generation.

More Portrait Cases: Celebrity-Grade Quality

For those seeking high-quality portrait aspect ratio videos, OmniHuman leverages test samples from CelebV-HQ datasets to deliver studio-level realism. These results showcase the model’s ability to preserve facial integrity, emotion, and micro-expressions with impressive accuracy.

Half-Body Cases with Detailed Gesture Movements

OmniHuman’s advanced gesture handling, provides examples featuring detailed hand movements and expressive gestures. Many of these inputs are sourced from TED, Pexels, and AIGC, further validating the model’s adaptability to various input styles and formats.

Conclusion

OmniHuman sets a new benchmark in AI-driven human video generation, seamlessly integrating multimodal conditioning, high-quality motion synthesis, and diverse input adaptability. Whether for singing performances, realistic conversations, or creative content generation, OmniHuman is redefining the future of video AI with unmatched realism and versatility.

Checkout the research paper of OmniHuman: https://omnihuman-lab.github.io/