LongCat Avatar
AIGenerates realistic, lip-synchronized talking videos from a single photo and audio with natural motion and consistent identity.
-(0 Reviews)
Free Available
Starting from Free
LongCat Avatar
AI
Generates realistic, lip-synchronized talking videos from a single photo and audio with natural motion and consistent identity.
-(0 Reviews)
Free Available
Starting from Free
About LongCat Avatar
LongCat Avatar (part of the LongCat-Video family) produces audio-driven, realistic talking-head and character videos from a reference photo or image and an audio track. It emphasizes near-perfect lip synchronization, natural head/face motion, and identity consistency across frames. The model supports single- and multi-character scenarios and native tasks including Audio-Text-to-Video, Audio-Image-to-Video and Video Continuation. The project provides downloadable model weights and demo scripts (hosted on GitHub and Hugging Face) for local or cloud execution and includes optimizations such as configurable attention backends (FlashAttention variants) to improve runtime performance.
Screenshots
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Key Features
Audio-Driven Video Generation: Converts an input audio track and a reference photo/image into a temporally consistent, lip-synchronized talking-video, preserving the subject's identity across frames.
Multi-Modal Task Support: Natively supports Audio-Text-to-Video, Audio-Image-to-Video, and Video-Continuation tasks, enabling workflows from text prompts + audio to full video or continuing existing video clips.
Single- and Multi-Character Modes: Provides separate model variants and demo scripts for single-character and multi-character audio-driven generation to handle scenarios with one or multiple speaking characters.
High-Fidelity Lip Sync & Natural Motion: Generates precise mouth articulation aligned to audio and produces plausible head and facial motions for expressive, dynamic outputs rather than static lip movement.
Downloadable Weights & Demos: Official model weights and example assets are published on Hugging Face and GitHub with runnable demo scripts (torchrun/Streamlit examples) for local/cloud inference and experimentation.
Performance & Backend Configurability: Model configs support optimized attention implementations (e.g., FlashAttention-2/3 or xformers) to improve memory and runtime efficiency on compatible hardware.
Video Continuation & Long-Video Capabilities: Designed to continue videos and generate longer sequences segment-by-segment while maintaining identity and temporal coherence across segments.
Research-Oriented License & Documentation: Released with code, README, and technical reports describing architectures and evaluations to support reproducibility and further research.
Audio-driven lip-synchronized video generation from a single photo and audio
Supports Audio-Text-to-Video, Audio-Image-to-Video, and Video-Continuation tasks
Single-character and multi-character model variants (Avatar-Single, Avatar-Multi)
High-fidelity identity preservation and natural head/face motion
Model family built on LongCat-Video foundation (reported 13.6B parameter base model)
Available model checkpoints on Hugging Face Hub for local download
Demo/inference scripts included (run_demo_avatar_* and run_demo_image_to_video.py)
PyTorch-based inference with torchrun for multi-GPU execution
Optional acceleration via FlashAttention (enabled by default in config) or xformers
Integrates with Hugging Face Diffusers and Transformers ecosystems
Use Cases
Creating talking-head avatars for marketing videos or social media by providing a single photo and voiceover to produce lip-synced video clips.
Dubbing and localized content: replacing original speech with translated audio while preserving speaker identity and generating synchronized facial motion for new languages.
Virtual presenters and e-learning: generating instructor or narrator videos from scripts and audio to produce scalable educational content without studio shoots.
Interactive characters and virtual assistants: powering avatar-driven interfaces where user audio or TTS is turned into real-time or pre-rendered talking-character videos.
Film and game previsualization: quickly prototyping character dialogue scenes by converting audio and reference images into animated sequences for review.
Research and development: fine-tuning and extending the model for improved realism, multi-speaker interactions, or integration into larger video generation systems.
Generating realistic talking avatars for marketing and social media content
Dubbing and lip-synced video re-creation from audio tracks
Virtual presenters, customer-facing assistants, and educational video synthesis
Character animation for games and virtual production
Video continuation and editing workflows (extending or animating existing clips)
