base on AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation # AniPortrait **AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animations** Author: Huawei Wei, Zejun Yang, Zhisheng Wang Organization: Tencent Games Zhiji, Tencent  Here we propose AniPortrait, a novel framework for generating high-quality animation driven by audio and a reference portrait image. You can also provide a video to achieve face reenacment. <a href='https://arxiv.org/abs/2403.17694'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/ZJYang/AniPortrait/tree/main'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-orange'></a> <a href='https://huggingface.co/spaces/ZJYang/AniPortrait_official'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-green'></a> ## Pipeline  ## Updates / TODO List - ✅ [2024/03/27] Now our paper is available on arXiv. - ✅ [2024/03/27] Update the code to generate pose_temp.npy for head pose control. - ✅ [2024/04/02] Update a new pose retarget strategy for vid2vid. Now we support substantial pose difference between ref_image and source video. - ✅ [2024/04/03] We release our Gradio [demo](https://huggingface.co/spaces/ZJYang/AniPortrait_official) on HuggingFace Spaces (thanks to the HF team for their free GPU support)! - ✅ [2024/04/07] Update a frame interpolation module to accelerate the inference process. Now you can add -acc in inference commands to get a faster video generation. - ✅ [2024/04/21] We have released the audio2pose model and [pre-trained weight](https://huggingface.co/ZJYang/AniPortrait/tree/main) for audio2video. Please update the code and download the weight file to experience. ## Various Generated Videos ### Self driven <table class="center"> <tr> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/82c0f0b0-9c7c-4aad-bf0e-27e6098ffbe1" muted="false"></video> </td> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/51a502d9-1ce2-48d2-afbe-767a0b9b9166" muted="false"></video> </td> </tr> </table> ### Face reenacment <table class="center"> <tr> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/d4e0add6-20a2-4f4b-808c-530a6f4d3331" muted="false"></video> </td> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/849fce22-0db1-4257-a75f-a5dc655e6b9e" muted="false"></video> </td> </tr> </table> Video Source: [鹿火CAVY from bilibili](https://www.bilibili.com/video/BV1H4421F7dE/?spm_id_from=333.337.search-card.all.click) ### Audio driven <table class="center"> <tr> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/63171e5a-e4c1-4383-8f20-9764524928d0" muted="false"></video> </td> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/6fd74024-ba19-4f6b-b37a-10df5cf2c934" muted="false"></video> </td> </tr> <tr> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/9e516cc5-bf09-4d45-b5e3-820030764982" muted="false"></video> </td> <td width=50% style="border: none"> <video controls autoplay loop src="https://github.com/Zejun-Yang/AniPortrait/assets/21038147/7c68148b-8022-453f-be9a-c69590038197" muted="false"></video> </td> </tr> </table> ## Installation ### Build environment We recommend a python version >=3.10 and cuda version =11.7. Then build environment as follows: ```shell pip install -r requirements.txt ``` ### Download weights All the weights should be placed under the `./pretrained_weights` direcotry. You can download weights manually as follows: 1. Download our trained [weights](https://huggingface.co/ZJYang/AniPortrait/tree/main), which include the following parts: `denoising_unet.pth`, `reference_unet.pth`, `pose_guider.pth`, `motion_module.pth`, `audio2mesh.pt`, `audio2pose.pt` and `film_net_fp16.pt`. You can also download from [wisemodel](https://wisemodel.cn/models/zjyang8510/AniPortrait). 2. Download pretrained weight of based models and other components: - [StableDiffusion V1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) - [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) - [image_encoder](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/tree/main/image_encoder) - [wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) Finally, these weights should be orgnized as follows: ```text ./pretrained_weights/ |-- image_encoder | |-- config.json | `-- pytorch_model.bin |-- sd-vae-ft-mse | |-- config.json | |-- diffusion_pytorch_model.bin | `-- diffusion_pytorch_model.safetensors |-- stable-diffusion-v1-5 | |-- feature_extractor | | `-- preprocessor_config.json | |-- model_index.json | |-- unet | | |-- config.json | | `-- diffusion_pytorch_model.bin | `-- v1-inference.yaml |-- wav2vec2-base-960h | |-- config.json | |-- feature_extractor_config.json | |-- preprocessor_config.json | |-- pytorch_model.bin | |-- README.md | |-- special_tokens_map.json | |-- tokenizer_config.json | `-- vocab.json |-- audio2mesh.pt |-- audio2pose.pt |-- denoising_unet.pth |-- film_net_fp16.pt |-- motion_module.pth |-- pose_guider.pth `-- reference_unet.pth ``` Note: If you have installed some of the pretrained models, such as `StableDiffusion V1.5`, you can specify their paths in the config file (e.g. `./config/prompts/animation.yaml`). ## Gradio Web UI You can try out our web demo by the following command. We alse provide online demo <a href='https://huggingface.co/spaces/ZJYang/AniPortrait_official'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-green'></a> in Huggingface Spaces. ```shell python -m scripts.app ``` ## Inference Kindly note that you can set -L to the desired number of generating frames in the command, for example, `-L 300`. **Acceleration method**: If it takes long time to generate a video, you can download [film_net_fp16.pt](https://huggingface.co/ZJYang/AniPortrait/tree/main) and put it under the `./pretrained_weights` direcotry. Then add `-acc` in the command. Here are the cli commands for running inference scripts: ### Self driven ```shell python -m scripts.pose2vid --config ./configs/prompts/animation.yaml -W 512 -H 512 -acc ``` You can refer the format of animation.yaml to add your own reference images or pose videos. To convert the raw video into a pose video (keypoint sequence), you can run with the following command: ```shell python -m scripts.vid2pose --video_path pose_video_path.mp4 ``` ### Face reenacment ```shell python -m scripts.vid2vid --config ./configs/prompts/animation_facereenac.yaml -W 512 -H 512 -acc ``` Add source face videos and reference images in the animation_facereenac.yaml. ### Audio driven ```shell python -m scripts.audio2vid --config ./configs/prompts/animation_audio.yaml -W 512 -H 512 -acc ``` Add audios and reference images in the animation_audio.yaml. Delete `pose_temp` in `./configs/prompts/animation_audio.yaml` can enable the audio2pose model. You can also use this command to generate a pose_temp.npy for head pose control: ```shell python -m scripts.generate_ref_pose --ref_video ./configs/inference/head_pose_temp/pose_ref_video.mp4 --save_path ./configs/inference/head_pose_temp/pose.npy ``` ## Training ### Data preparation Download [VFHQ](https://liangbinxie.github.io/projects/vfhq/) and [CelebV-HQ](https://github.com/CelebV-HQ/CelebV-HQ) Extract keypoints from raw videos and write training json file (here is an example of processing VFHQ): ```shell python -m scripts.preprocess_dataset --input_dir VFHQ_PATH --output_dir SAVE_PATH --training_json JSON_PATH ``` Update lines in the training config file: ```yaml data: json_path: JSON_PATH ``` ### Stage1 Run command: ```shell accelerate launch train_stage_1.py --config ./configs/train/stage1.yaml ``` ### Stage2 Put the pretrained motion module weights `mm_sd_v15_v2.ckpt` ([download link](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt)) under `./pretrained_weights`. Specify the stage1 training weights in the config file `stage2.yaml`, for example: ```yaml stage1_ckpt_dir: './exp_output/stage1' stage1_ckpt_step: 30000 ``` Run command: ```shell accelerate launch train_stage_2.py --config ./configs/train/stage2.yaml ``` ## Acknowledgements We first thank the authors of [EMO](https://github.com/HumanAIGC/EMO), and part of the images and audios in our demos are from EMO. Additionally, we would like to thank the contributors to the [Moore-AnimateAnyone](https://github.com/MooreThreads/Moore-AnimateAnyone), [majic-animate](https://github.com/magic-research/magic-animate), [animatediff](https://github.com/guoyww/AnimateDiff) and [Open-AnimateAnyone](https://github.com/guoqincode/Open-AnimateAnyone) repositories, for their open research and exploration. ## Citation ``` @misc{wei2024aniportrait, title={AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animations}, author={Huawei Wei and Zejun Yang and Zhisheng Wang}, year={2024}, eprint={2403.17694}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```", Assign "at most 3 tags" to the expected json: {"id":"8930","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"