Google’s VLOGGER: A New Era of AI-Powered Video Synthesis
Google Research has unveiled VLOGGER, a groundbreaking AI model capable of generating realistic,dynamic videos of human figures from a single input image and audio sample. This innovative technology represents a significant leap forward in the field of AI-powered video synthesis,opening up new possibilities for creative expression, content creation, and even video translation.
VLOGGER leverages a multi-modal diffusion model, trained on amassive dataset of human figures and movements, to generate videos that seamlessly blend real-world imagery with AI-generated animation. The model excels at capturing the nuances of human expression, including facial movements, lip synchronization, head gestures, eye movements,and even hand gestures, all driven by the accompanying audio.
Beyond Lip Sync: A Deeper Understanding of Human Motion
Unlike previous AI video synthesis models that primarily focused on lip synchronization, VLOGGER takes a more comprehensive approach. Themodel’s ability to predict and generate 3D facial expressions and body postures, synchronized with the audio, allows for a more natural and engaging video experience. This opens up exciting possibilities for applications like creating animated avatars for virtual meetings, generating personalized video messages, and even bringing historical figures to life in a more realistic way.
Key Features and Capabilities of VLOGGER:
- Image and Audio-Driven Video Generation: VLOGGER can generate videos of speaking humans from a single image and corresponding audio input. Simply provide a picture and an audio clip, and VLOGGER will create a video with the person in the image moving and speakingin sync with the audio.
- Diversity and Realism: The videos generated by VLOGGER exhibit a high degree of diversity, showcasing various actions and expressions of the original subject while maintaining background consistency and video realism.
- Video Editing: VLOGGER can be used to edit existing videos, such as changing the expressionsof individuals within a video, ensuring consistency with the original, unaltered pixels.
- Generating Moving and Speaking Figures: VLOGGER can create videos of talking faces from a single input image and driving audio, even without access to original video footage of the individual.
- Video Translation: VLOGGER can translate videos fromone language to another by editing the lip and facial areas to match the new audio, enabling cross-language video content adaptation.
How VLOGGER Works: A Two-Stage Process
VLOGGER employs a two-stage process that combines audio-driven motion generation with temporally coherent video generation.
Stage1: Audio-Driven Motion Generation
- Audio Processing: VLOGGER receives an audio input, which can be speech or music. If the input is text, it is converted to an audio waveform using a text-to-speech (TTS) model.
- 3D Motion Prediction: The systemutilizes a transformer-based network to process the audio input. This network is trained to predict 3D facial expressions and body postures synchronized with the audio. It employs multi-step attention layers to capture temporal features in the audio and generate a sequence of 3D pose parameters.
- Generating Control Representations: Thenetwork outputs a series of predicted facial expressions (θe i) and body posture residuals (∆θb i). These parameters are then used to generate 2D representations that control the video generation process.
Stage 2: Temporally Coherent Video Generation
- Video Generation Model: VLOGGER’s second stage is a temporal diffusion model that receives the 3D motion controls generated in the first stage and a reference image (the single input image of the person).
- Conditioned Video Generation: The video generation model is a diffusion-based image-to-image translation model that leverages the predicted2D controls to generate a series of frames animated according to the input audio and 3D motion parameters.
- Super-Resolution: To enhance video quality, VLOGGER incorporates a super-resolution diffusion model that upscales the base video resolution from 128×128 to higher resolutions, suchas 256×256 or 512×512.
- Temporal Outpainting: VLOGGER uses temporal outpainting to generate videos of arbitrary lengths. It first generates a set number of frames and then iteratively generates new frames based on the information from the previous frame, extendingthe video’s duration.
Training Data and Datasets
VLOGGER was trained on a massive dataset called MENTOR, containing 2200 hours and 800,000 identities, encompassing a wide range of identities and dynamic hand gestures. During training, the model learned to generate coherent, high-quality video sequences based on 3D pose parameters and input images.
The Future of AI-Powered Video Synthesis
VLOGGER represents a significant advancement in AI-powered video synthesis, offering a glimpse into a future where creating realistic and engaging video content becomes accessible to everyone. This technology has thepotential to revolutionize various industries, from entertainment and education to marketing and communication. As AI research continues to progress, we can expect even more sophisticated and powerful video synthesis models that push the boundaries of what’s possible in the digital world.
【source】https://ai-bot.cn/google-vlogger-ai-model/
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