EchoMimic is a novel approach for generating lifelike portrait animations using both audio signals and facial landmarks. The method is trained concurrently using audio and facial landmarks, enabling the generation of portrait videos through audio, facial landmarks, or a combination of both. EchoMimic has been evaluated on various public datasets and a collected dataset, demonstrating superior performance in both quantitative and qualitative assessments. The method integrates a Denoising U-Net architecture with specialized modules for reference images, facial landmarks, and audio inputs. These modules work together to ensure a comprehensive and contextually rich encoding process, crucial for generating high-fidelity video content. The model also incorporates temporal attention layers to capture the intricate dependencies between successive frames, ensuring smooth and harmonious transitions in the synthesized frames. Additionally, a timestep-aware spatial loss is proposed to learn the face structure directly in the pixel space, enhancing the realism and expressiveness of the output. The method also includes random landmark selection and audio augmentation techniques to improve the training process. EchoMimic has been shown to produce high-quality, temporally coherent animations with precise lip synchronization, outperforming existing methods in terms of visual quality and temporal consistency. The method is capable of generating talking head videos using audio-driven, landmark-driven, or audio+selected landmark-driven approaches, demonstrating its versatility and effectiveness in creating realistic and expressive animations. The results show that EchoMimic achieves the lowest scores in FID and FVD, indicating a marked improvement in visual quality and temporal consistency over existing techniques. The method also demonstrates enhanced capabilities in addressing substantial variations in pose and accurately reproducing nuanced expressions. The ablation study further confirms the effectiveness of the proposed method in generating high-fidelity animations. Despite its success, the method has limitations, including the need for further research to improve video processing frameworks and accelerate the generation process. Overall, EchoMimic represents a significant advancement in the field of portrait image animation, offering a robust and effective solution for generating high-quality, lifelike animations.EchoMimic is a novel approach for generating lifelike portrait animations using both audio signals and facial landmarks. The method is trained concurrently using audio and facial landmarks, enabling the generation of portrait videos through audio, facial landmarks, or a combination of both. EchoMimic has been evaluated on various public datasets and a collected dataset, demonstrating superior performance in both quantitative and qualitative assessments. The method integrates a Denoising U-Net architecture with specialized modules for reference images, facial landmarks, and audio inputs. These modules work together to ensure a comprehensive and contextually rich encoding process, crucial for generating high-fidelity video content. The model also incorporates temporal attention layers to capture the intricate dependencies between successive frames, ensuring smooth and harmonious transitions in the synthesized frames. Additionally, a timestep-aware spatial loss is proposed to learn the face structure directly in the pixel space, enhancing the realism and expressiveness of the output. The method also includes random landmark selection and audio augmentation techniques to improve the training process. EchoMimic has been shown to produce high-quality, temporally coherent animations with precise lip synchronization, outperforming existing methods in terms of visual quality and temporal consistency. The method is capable of generating talking head videos using audio-driven, landmark-driven, or audio+selected landmark-driven approaches, demonstrating its versatility and effectiveness in creating realistic and expressive animations. The results show that EchoMimic achieves the lowest scores in FID and FVD, indicating a marked improvement in visual quality and temporal consistency over existing techniques. The method also demonstrates enhanced capabilities in addressing substantial variations in pose and accurately reproducing nuanced expressions. The ablation study further confirms the effectiveness of the proposed method in generating high-fidelity animations. Despite its success, the method has limitations, including the need for further research to improve video processing frameworks and accelerate the generation process. Overall, EchoMimic represents a significant advancement in the field of portrait image animation, offering a robust and effective solution for generating high-quality, lifelike animations.