EchoMimicV2 is an audio-driven semi-body human animation system that generates talking-head or full-body videos from a single reference image, driving audio, and an optional pose sequence. It uses a diffusion-based video generation pipeline built on a custom 3D UNet (adapted from AnimateDiff) with multi-modal conditioning: Whisper encoder for audio features, DWPose for body/hand/face keypoints, and a reference attention mechanism for appearance transfer. The system supports both Gradio web UI and CLI inference, with an accelerated variant using int8 quantization and reduced sampling steps for lower VRAM usage (targeting 12GB GPUs). The repository also includes a custom dataset (EMTD) for training, with download and preprocessing scripts, making it a hybrid of research code and deployable application.
LiveTalking is a real-time interactive streaming digital human engine that synchronizes lip movements with audio generated from text or speech input. It functions as a production-grade web server (Flask + aiohttp) supporting WebRTC, RTMP, and virtual camera output, enabling applications like live streaming, virtual assistants, and conversational avatars. The system integrates a pluggable pipeline: user input → optional LLM (OpenAI-compatible) → TTS (8+ providers including EdgeTTS, CosyVoice, GPT-SoVITS) → lip-sync model inference (Wav2Lip, MuseTalk, Ultralight Digital Human) → video frame compositing → streaming output. It also includes ASR via a local FunASR/SenseVoice WebSocket server and a web admin UI for session management and avatar generation. Architecturally, it follows a plugin-based design using a decorator registry (registry.py) that allows extending TTS, Avatar, and Output modules via registration. The core rendering loop in BaseAvatar manages three concurrent threads (TTS, inference, frame processing) with queue-based data flow. WebRTC integration uses aiortc for peer connections, and the system supports multi-session concurrency with session limits. While primarily an application, it embeds research-derived model code (Whisper, face detection, face parsing) and includes a benchmark script for ASR latency comparison. The codebase has some technical debt: duplicated face detection utilities, stale Dockerfile (Python 3.10/torch 1.12 vs README's newer requirements), and commented-out legacy code. However, the core architecture is solid, and the plugin system and audio utilities are well-crafted and reusable.
This repository is a curated 'awesome list' of papers, datasets, and resources for talking head generation, covering image-driven, audio-driven, and NeRF/3D-based approaches. It contains no executable code, data pipelines, or models—only a single README.md with organized tables of references. The repo serves as a research bibliography, not a software project. There are no assets to reproduce or reuse programmatically.