VLOGGER - A model launched by Google for converting images into synthetic human-shaped dynamic videos.
VLOGGER AI is a multimodal diffusion model developed by Google's research team, specifically designed to generate realistic, coherent human-motion videos from a single input image and audio sample. The model's main function is to utilize artificial intelligence...
What is a VLOGGER?
VLOGGER AI is a multimodal diffusion model developed by Google's research team, specifically designed to generate realistic, coherent, dynamic videos of people from a single input image and audio sample. The model's main function is to use artificial intelligence to transform a static image into a dynamic video character while maintaining the lifelike appearance of the person in the photo. Furthermore, VLOGGER can control the person's movements based on audio, not only facial movements and lip synchronization, but also head movements, gaze, blinking, and upper body and hand gestures, thus pushing audio-driven video synthesis to a new level.
VLOGGER's official website entrance
- Official project homepage:https://enriccorona.github.io/vlogger/
- arXiv research paper:https://arxiv.org/abs/2403.08764v1
VLOGGER Features
- Image and audio driven video generationVLOGGER can generate videos of speaking humans from a single image of a person and corresponding audio input. Users only need to provide a picture and an audio clip, and VLOGGER will generate a video of a person whose facial expressions, lip movements, and body language are synchronized with the audio.
- Diversity and authenticityVLOGGER generates highly diverse videos that can showcase different actions and expressions of the original subject while maintaining background consistency and video authenticity.
- Video editingVLOGGER can be used to edit existing videos, such as changing the facial expressions of people in the video to keep them consistent with the original video's unchanged pixels.
- Generate moving and talking charactersVLOGGER can generate videos of speaking faces from a single input image and driving audio, even without the original video footage of the person in the video.
- Video translationVLOGGER can convert videos in one language to another by editing lip and facial areas to match new audio, achieving cross-language video content adaptation.
How VLOGGER works
VLOGGER works primarily based on a two-stage process that combines audio-driven motion generation with temporally coherent video generation.
Phase 1: Audio-Driven Motion Generation
- Audio processingVLOGGER first receives an audio input, which can be speech or music. If the input is text, it will also be converted into an audio waveform using a text-to-speech (TTS) model.
- 3D motion predictionNext, the system uses a network based on a Transformer architecture to process the audio input. This network is trained to predict 3D facial expressions and body poses synchronized with the audio. The network uses multi-step attention layers to capture the temporal features of the audio and generate a series of 3D pose parameters.
- Generate control representationThe network outputs a series of predicted facial expressions (θe i) and residuals of body poses (∆θb i). These parameters are then used to generate a 2D representation that controls the video generation process.
Phase Two: Time-Continuous Video Generation
- Video generation modelThe second stage of VLOGGER is a time-diffusion model that receives the 3D motion control generated in the first stage and a reference image (i.e., a single image of a person as input).
- Conditional video generationThe video generation model is a diffusion-based image-to-image translation model that uses predicted 2D controls to generate a series of frames that are animated according to the input audio and 3D motion parameters.
- Super-resolutionTo improve video quality, VLOGGER also includes a super-resolution diffusion model that upscales the base video resolution from 128×128 to higher resolutions, such as 256×256 or 512×512.
- Time extensionVLOGGER uses temporal outpainting to generate videos of arbitrary length. It first generates a certain number of frames, and then iteratively generates new frames based on information from the previous frame, thus extending the length of the video.
Dataset and Training
- VLOGGER was trained on a large dataset called MENTOR (2200 hours and 800,000 identities), which contains a large number of identities and dynamic gestures. During training, the model learned how to generate coherent, high-quality video sequences based on 3D pose parameters and input images.