IMTalker

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ACM MM | IMTalker: Efficient Audio-driven Talking Face Generation with Implicit Motion Transfer

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<p align="center">
  <h1>IMTalker: Efficient Audio-driven Talking Face Generation with Implicit Motion Transfer</h1>

[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2511.22167)
  [![Hugging Face Model](https://img.shields.io/badge/Model-HuggingFace-yellow?logo=huggingface)](https://huggingface.co/cbsjtu01/IMTalker)
  [![Hugging Face Space](https://img.shields.io/badge/Space-HuggingFace-blueviolet?logo=huggingface)](https://huggingface.co/spaces/chenxie95/IMTalker)
  [![demo](https://img.shields.io/badge/GitHub-Demo-orange.svg)](https://cbsjtu01.github.io/IMTalker/)

</p>
</div>

## 📖 Overview
IMTalker accepts diverse portrait styles and achieves 40 FPS for video-driven and 42 FPS for audio-driven talking-face generation when tested on an NVIDIA RTX 4090 GPU at 512 × 512 resolution. It also enables diverse controllability by allowing precise head-pose and eye-gaze inputs alongside audio

## 📢 News
- **[2025.12.16]** 🚀 The training code are released!
- **[2025.11.27]** 🚀 The inference code and pretrained weights are released!
## 🛠️ Installation

### 1. Environment Setup

```bash
conda create -n IMTalker python=3.10
conda activate IMTalker
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
conda install -c conda-forge ffmpeg 
```

**2. Install with pip:**

```bash
git clone https://github.com/cbsjtu01/IMTalker.git
cd IMTalker
pip install -r requirement.txt
```
## ⚡ Quick Start

You can simply run the Gradio demo to get started. The script will **automatically download** the required pretrained models to the `./checkpoints` directory if they are missing.

```bash
python app.py
```

## 📦 Model Zoo

Please download the pretrained models and place them in the `./checkpoints` directory.

```text
./checkpoints
├── renderer.ckpt                     # The main renderer
├── generator.ckpt                    # The main generator
└── wav2vec2-base-960h/               # Audio encoder folder
    ├── config.json
    ├── pytorch_model.bin
    └── ...
```

## 🚀 Inference

### 1. Audio-driven Inference
Generate a talking face from a source image and an audio file.

```bash
python generator/generate.py \
    --ref_path "./assets/source_image.jpg" \
    --aud_path "./assets/input_audio.wav" \
    --res_dir "./results/" \
    --generator_path "./checkpoints/generator.ckpt" \
    --renderer_path "./checkpoints/renderer.ckpt" \
    --a_cfg_scale 2 \
    --crop
```
### 2. Video-driven Inference
Generate a talking face from a source image and an driving video file.

```bash
python renderer/inference.py \
    --source_path "./assets/source_image.jpg" \
    --driving_path "./assets/driving_video.mp4" \
    --save_path "./results/" \
    --renderer_path "./checkpoints/renderer.ckpt" \
    --crop
```

## 🚀 Train

### 1. Train the renderer
#### Data Preparation
You can follow the dataset processing pipeline in [talkingfaceprocess](https://github.com/liutaocode/talking_face_preprocessing) to crop the raw video data into 512×512 resolution videos where the face occupies the main region, and to extract landmarks for each video. Ensure your dataset directory is organized as follows.
```text
/path/to/renderer_dataset
├── video_frame
    ├── video_0001
      ├── image_001.jpg
      ├── image_002.jpg
      ├── ...
    ├── video_0002
    ├── ...
├── lmd
    ├── video_0001.txt
    ├── video_0002.txt
    ├── ...
```
#### Training Command
Then you can execute the following command to train our renderer. In our experiments, we used 4 × A100 (80 GB) GPUs; with a batch size of 4, the GPU memory usage did not exceed 50 GB, and each iteration took approximately 1 second. You can adjust the batch size and learning rate according to your hardware configuration.
```text
python renderer/train.py \
    --dataset_path /path/to/renderer_dataset \
    --exp_name renderer_exp \
    --batch_size 4 \
    --iter 7000000 \
    --lr 1e-4 \
```
### 2. Train the generator
#### Data Preparation
In the second step, you need to train our motion generator to enable speech-driven animation. To accelerate training, we pre-extract and store all required features, including: motion latents obtained by feeding each video frame into the motion encoder in the renderer; final-layer features extracted from audio WAV files using [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h); 6D pose parameters for each frame extracted with [SMIRK](https://github.com/georgeretsi/smirk); and gaze directions extracted using [L2CS-Net](https://github.com/Ahmednull/L2CS-Net). Ensure your dataset directory is organized as follows.
```text
/path/to/generator_dataset
├── motion
    ├── video_0001.pt
    ├── video_0002.pt
    ├── ...
├── audio
    ├── video_0001.npy
    ├── video_0002.npy
    ├── ...
├── smirk
    ├── video_0001.pt
    ├── video_0002.pt
    ├── ...
├── gaze
    ├── video_0001.npy
    ├── video_0002.npy
    ├── ...
```
#### Training Command
Then you can execute the following command to train the generator. In our experiments, we used 4 × A100 (80 GB) GPUs; with a batch size of 16, the GPU memory usage did not exceed 20 GB, achieving approximately 10 iterations per second, and the model converged within a few hours. You can adjust the batch size and learning rate according to your hardware configuration.
```text
python generator/train.py \
    --dataset_pat /path/to/generator_dataset \
    --exp_name generator_exp \
    --batch_size 16 \
    --iter 5000000 \
    --lr 1e-4
```
## 💡 Best Practices

To obtain the highest quality generation results, we recommend following these guidelines:

1.  **Input Image Composition**: 
    Please ensure the input image features the person's head as the primary subject. Since our model is explicitly trained on facial data, it does not support full-body video generation. 
    * The inference pipeline automatically **crops the input image** to focus on the face by default.
    * **Note on Resolution**: The model generates video at a fixed resolution of **512×512**. Using extremely high-resolution inputs will result in downscaling, so prioritize facial clarity over raw image dimensions.

2.  **Audio Selection**: 
    Our model was trained primarily on **English datasets**. Consequently, we recommend using **English audio** inputs to achieve the best lip-synchronization performance and naturalness.

3.  **Background Quality**: 
    We strongly recommend using source images with **solid colored** or **blurred (bokeh)** backgrounds. Complex or highly detailed backgrounds may lead to visual artifacts or jitter in the generated video.

## 📝 To-Do List
- [x] Release inference code and pretrained models.
- [x] Launch Hugging Face online demo.
- [x] Release training code.

## 📜 Citation
If you find our work useful for your research, please consider citing:

```bibtex
@article{imtalker2025,
  title={IMTalker: Efficient Audio-driven Talking Face Generation with Implicit Motion Transfer},
  author={Bo, Chen and Tao, Liu and Qi, Chen and  Xie, Chen and  Zilong Zheng}, 
  journal={arXiv preprint arXiv:2511.22167},
  year={2025}
}
```

## 🙏 Acknowledgement

We express our sincerest gratitude to the excellent previous works that inspired this project:

- **[IMF](https://github.com/ueoo/IMF)**: We adapted the framework and training pipeline from IMF and its reproduction code [IMF](https://github.com/johndpope/IMF).
- **[FLOAT](https://github.com/deepbrainai-research/float)**: We referenced the model architecture and implementation of Float for our generator.
- **[Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)**: We utilized Wav2Vec as our audio encoder.
- **[Face-Alignment](https://github.com/1adrianb/face-alignment)**: We used FaceAlignment for cropping images and videos.

IMTalker

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