PoseTalk - An open-source project that generates talking head animations driven by text and audio.
PoseTalk is an open-source project based on text and audio-based gesture control and motion refinement methods for generating talking head videos in a single pass. It synthesizes talking face videos from images, driving audio, and driving gestures, providing users with...
What is PoseTalk?
PoseTalk is an open-source project based on text and audio-based pose control and motion refinement methods for generating talking head videos in a single pass. It synthesizes talking face videos from images, driving audio, and driving poses, providing users with an efficient and convenient way to generate head animations. At its core, PoseTalk generates motion potentials in the pose potential space based on text prompts and audio cues, achieving natural and realistic head movement effects. Through the Pose Latent Diffusion (PLD) model and cascaded networks CoarseNet and RefineNet, it achieves high-quality lip synchronization and head pose generation, suitable for various applications such as virtual anchors, online education, and social media.
PoseTalk's main functions
- Text and audio driven gesture generationPoseTalk generates head poses based on text prompts and audio input, reflecting the long-term semantics and short-term changes in head movements.
- Pose Potential Diffusion Model (PLD)PoseTalk generates motion potentials in the pose potential space, enabling natural and realistic head movements.
- Cascaded Network Refinement StrategyBased on two cascaded networks, CoarseNet and RefineNet, we first estimate the coarse motion to generate an animated image of the new pose, and then refine the lip motion to improve lip synchronization performance.
- High lip synchronization qualityThrough a motion refinement strategy, PoseTalk generates head animations that are highly synchronized with the audio, especially excelling in lip-sync.
- Diverse pose generationUsers can use different text prompts to guide PoseTalk in generating various poses, increasing the diversity and customizability of the generated animations.
PoseTalk's technical principles
- Pose Latent Diffusion (PLD) ModelWorking within the representation space of a neural parametric head model, it captures fine details of the human head. The PLD model transforms text and audio information into head pose and motion information, providing a foundation for subsequent animation generation.
- Cascaded Network Refinement StrategyPoseTalk uses two cascaded networks, CoarseNet and RefineNet, to synthesize natural speaking videos. CoarseNet is responsible for estimating coarse motion, generating animated images of new poses. RefineNet learns finer lip movements by progressively estimating lip movements from low to high resolution, improving lip synchronization performance.
- Audio feature extractionPoseTalk extracts features from the input audio signal based on a pre-trained audio encoder (such as the Wave2Vec 2.0 model). These audio features are combined with text information to drive the head model's movement, enabling the generated head animation to be perfectly synchronized with the audio, achieving a high degree of consistency in lip movements, facial expressions, and other aspects.
- Training and reasoningDuring the training phase, PoseTalk uses a variational autoencoder (VAE) to learn a low-dimensional latent space for head pose and eye movements. During the inference phase, a PLD predicts natural pose sequences, and a video generation model is used to synthesize realistic speaking videos from audio features and the generated pose sequences.
PoseTalk project address
- Project official websiteposetalk.github.io/
- arXiv technical paper:https://arxiv.org/pdf/2409.02657
PoseTalk Application Scenarios
- Virtual assistants and digital humansPoseTalk is used to generate realistic head animations for virtual assistants or digital humans, providing a more natural and engaging interactive experience.
- Film and game productionIn the entertainment industry, PoseTalk is used to generate high-quality character animations, making character head movements and expressions more realistic and enhancing audience immersion.
- Online education and trainingIn remote learning environments, PoseTalk is used to generate avatars for teachers or lecturers, providing a more engaging learning experience.
- Social media and content creationUsers can generate personalized animated avatars or emojis based on PoseTalk, increasing the fun and interactivity of their social media content.