EchoMimicV3 - Ant open source multimodal digital human animation generation model

What is EchoMimicV3

EchoMimicV3 is a multimodal digital human video generation model introduced by Ant Group, with 1.3 billion parameters, capable of processing multiple inputs such as audio, text, and images to generate high-quality digital human animations. The model uses task-mixing and modal-mixing paradigms, combined with optimized training and inference strategies, to achieve fast, efficient and generalizable animation generation.EchoMimicV3 can be used in a variety of fields, such as virtual character animation, special effects production, virtual spokespersons, virtual teachers, and virtual social networking, which will bring a major breakthrough in the field of digital human animation.

Features of EchoMimicV3

• Multi-modal input support: The model can handle multiple modal inputs such as audio, text, image, etc., so that the generated digital human animation is richer and more natural, adapting to the needs of different scenes.
• Integrated Framework for Multitasking: Integrate multiple tasks such as audio-driven facial animation, text-to-motion generation, and image-driven pose prediction into a single model for multifunctional integration and efficiency.
• Efficient Reasoning and Training: Based on optimized training strategies and inference mechanisms, it enables fast model training and animation generation while maintaining high performance, saving time and resources.
• High quality animation generation: The generated digital human animation is rich in details, coherent and natural, meeting the high-quality needs of film and television, games, education and other fields, and enhancing the visual experience.
• strong generalization ability: The model has good generalization and can adapt to different input conditions and task requirements with high adaptability and flexibility.

Core Benefits of EchoMimicV3

• Multi-modal fusion capability: EchoMimicV3 can handle multiple modal inputs, including audio, text, images, etc. It supports the effective blending of modal information to generate high-quality human animations.
• Integrated Framework for Multitasking: Through the task blending paradigm, EchoMimicV3 integrates multiple tasks (e.g., audio-driven facial animation, text-to-motion generation, image-driven pose prediction, etc.) into a single model, increasing the efficiency of the model and reducing the complexity and computational cost associated with multiple models.
• Efficient Training and Reasoning: A series of optimized training strategies, such as negative direct preference optimization and phase-aware negative classifier free bootstrapping, are used to ensure the stability and efficiency of the model during training and inference. This enables the model to achieve fast animation generation while maintaining high performance.
• High quality animation generationEchoMimicV3 generates high-quality, natural and smooth human animations with the help of advanced model architecture and training methods. The generated animations excel in detail and coherence, meeting the needs of various application scenarios.
• strong generalization ability: EchoMimicV3 has good generalization ability to adapt to different input conditions and task requirements.
• Small models with big capabilities: EchoMimicV3 has only 1.3 billion parameters and achieves comparable or even better performance than larger models through efficient model design and optimization strategies.

Technical principles of EchoMimicV3

• task-hybrid paradigm: Based on the multi-task mask input and counter-intuitive task allocation strategy, the model can learn multiple tasks simultaneously during the training process to realize the synergistic gain of multi-tasks and avoid the common task conflict problem in traditional multi-task learning.
• modal hybrid paradigm: A coupled-decoupled multimodal cross-attention module is introduced, which combines the time-step phase-aware multimodal allocation mechanism to dynamically adjust the fusion of multimodal information, so that the model can better deal with the complex relationship between different modes.
• Optimization of training mechanisms: Using negative direct preference optimization and phase-aware negative classifier free bootstrapping techniques to ensure the stability of the model and the high quality of the generated results during the training and inference process, and to avoid instability during the training process and degradation of the generated results.
• Transformer Architecture: Based on the powerful sequence modeling capabilities of the Transformer architecture, the model is able to effectively capture long-distance dependencies in the input data to generate more natural and coherent animations.
• Pre-training and fine-tuning strategies: Learning generalized feature representations and knowledge by pre-training on large-scale datasets and fine-tuning on specific tasks allows the model to take full advantage of the large amount of unsupervised data to improve generalization and performance.

What is EchoMimicV3's official website?

• Project website:: https://antgroup.github.io/ai/echomimic_v3/
• GitHub repository:: https://github.com/antgroup/echomimic_v3
• HuggingFace Model Library:: https://huggingface.co/BadToBest/EchoMimicV3
• arXiv Technical Paper:: https://arxiv.org/pdf/2507.03905

People for whom EchoMimicV3 is intended

• Film, television and animation producers: Film and TV animators quickly generate high-quality animations, reduce manual modeling time and improve production efficiency.
• game developer: Game designers generate vivid animations for game characters to enhance game immersion and optimize the development process.
• Advertising and marketing staff: Ad creators create virtual spokespersons and animated ads to enhance brand appeal and user engagement.
• educator: Online education platform developers generate virtual teacher animations to make teaching more lively and interesting and increase students' interest in learning.
• Virtual Reality (VR) and Augmented Reality (AR) Developers: VR/AR developers generate realistic virtual images and animations to enhance user experience and immersion.