OmniHuman-1 , an advanced AI-driven human video generation model, has been introduced, marking a significant leap in multimodal animation technology. OmniHuman-1 enables the creation of highly lifelike human videos using minimal input, such as a single image and motion cues like audio or video. The model’s innovative mixed-conditioning training strategy allows it to utilize diverse data sources effectively, overcoming previous limitations in human animation research. At the core of OmniHuman-1 is its DiT (Diffusion Transformer)-based architecture , which enables high-fidelity motion synthesis by leveraging a spatiotemporal diffusion model . This framework consists of two key components: The Omni-Conditions Training Strategy – a progressive, multi-stage training approach that organizes data based on the motion-related extent of the conditioning signals. This mixed-condition training enables the model to scale effectively with diverse […]
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