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--- |
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pipeline_tag: image-to-video |
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language: |
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- en |
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--- |
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<!-- ## **HunyuanVideo-Avatar** --> |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/646d7592bb95b5d4001e5a04/HDZpvr8F-UaHAHlsF--fh.png" height=100> |
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</p> |
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<div align="center"> |
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<a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-Avatar"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-Avatar%20Code&message=Github&color=blue"></a> |
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<a href="https://HunyuanVideo-Avatar.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a> |
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<a href="https://hunyuan.tencent.com/modelSquare/home/play?modelId=126"><img src="https://img.shields.io/static/v1?label=Playground&message=Web&color=green"></a> |
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<a href="https://arxiv.org/pdf/2505.20156"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv&color=red"></a> |
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<a href="https://huggingface.co/tencent/HunyuanVideo-Avatar"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-Avatar&message=HuggingFace&color=yellow"></a> |
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</div> |
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> [**HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters**](https://arxiv.org/pdf/2505.20156) <be> |
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## **Abstract** |
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Recent years have witnessed significant progress in audio-driven human animation. However, critical challenges remain in (i) generating highly dynamic videos while preserving character consistency, (ii) achieving precise emotion alignment between characters and audio, and (iii) enabling multi-character audio-driven animation. To address these challenges, we propose HunyuanVideo-Avatar, a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-controllable, and multi-character dialogue videos. Concretely, HunyuanVideo-Avatar introduces three key innovations: (i) A character image injection module is designed to replace the conventional addition-based character conditioning scheme, eliminating the inherent condition mismatch between training and inference. This ensures the dynamic motion and strong character consistency; (ii) An Audio Emotion Module (AEM) is introduced to extract and transfer the emotional cues from an emotion reference image to the target generated video, enabling fine-grained and accurate emotion style control; (iii) A Face-Aware Audio Adapter (FAA) is proposed to isolate the audio-driven character with latent-level face mask, enabling independent audio injection via cross-attention for multi-character scenarios. These innovations empower HunyuanVideo-Avatar to surpass state-of-the-art methods on benchmark datasets and a newly proposed wild dataset, generating realistic avatars in dynamic, immersive scenarios. The source code and model weights will be released publicly. |
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## **HunyuanVideo-Avatar Overall Architecture** |
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We propose **HunyuanVideo-Avatar**, a multi-modal diffusion transformer(MM-DiT)-based model capable of generating **dynamic**, **emotion-controllable**, and **multi-character dialogue** videos. |
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## 🎉 **HunyuanVideo-Avatar Key Features** |
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### **High-Dynamic and Emotion-Controllable Video Generation** |
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HunyuanVideo-Avatar supports animating any input **avatar images** to **high-dynamic** and **emotion-controllable** videos with simple **audio conditions**. Specifically, it takes as input **multi-style** avatar images at **arbitrary scales and resolutions**. The system supports multi-style avatars encompassing photorealistic, cartoon, 3D-rendered, and anthropomorphic characters. Multi-scale generation spanning portrait, upper-body and full-body. It generates videos with high-dynamic foreground and background, achieving superior realistic and naturalness. In addition, the system supports controlling facial emotions of the characters conditioned on input audio. |
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### **Various Applications** |
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HunyuanVideo-Avatar supports various downstream tasks and applications. For instance, the system generates talking avatar videos, which could be applied to e-commerce, online streaming, social media video production, etc. In addition, its multi-character animation feature enlarges the application such as video content creation, editing, etc. |
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## 🚀 Parallel Inference on Multiple GPUs |
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For example, to generate a video with 8 GPUs, you can use the following command: |
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```bash |
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cd HunyuanVideo-Avatar |
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JOBS_DIR=$(dirname $(dirname "$0")) |
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export PYTHONPATH=./ |
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export MODEL_BASE="./weights" |
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checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt |
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torchrun --nnodes=1 --nproc_per_node=8 --master_port 29605 hymm_sp/sample_batch.py \ |
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--input 'assets/test.csv' \ |
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--ckpt ${checkpoint_path} \ |
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--sample-n-frames 129 \ |
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--seed 128 \ |
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--image-size 704 \ |
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--cfg-scale 7.5 \ |
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--infer-steps 50 \ |
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--use-deepcache 1 \ |
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--flow-shift-eval-video 5.0 \ |
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--save-path ${OUTPUT_BASEPATH} |
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``` |
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## 🔑 Single-gpu Inference |
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For example, to generate a video with 1 GPU, you can use the following command: |
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```bash |
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cd HunyuanVideo-Avatar |
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JOBS_DIR=$(dirname $(dirname "$0")) |
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export PYTHONPATH=./ |
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export MODEL_BASE=./weights |
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OUTPUT_BASEPATH=./results-single |
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checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt |
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export DISABLE_SP=1 |
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CUDA_VISIBLE_DEVICES=0 python3 hymm_sp/sample_gpu_poor.py \ |
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--input 'assets/test.csv' \ |
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--ckpt ${checkpoint_path} \ |
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--sample-n-frames 129 \ |
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--seed 128 \ |
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--image-size 704 \ |
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--cfg-scale 7.5 \ |
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--infer-steps 50 \ |
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--use-deepcache 1 \ |
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--flow-shift-eval-video 5.0 \ |
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--save-path ${OUTPUT_BASEPATH} \ |
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--use-fp8 \ |
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--infer-min |
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``` |
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### Run with very low VRAM |
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```bash |
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cd HunyuanVideo-Avatar |
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JOBS_DIR=$(dirname $(dirname "$0")) |
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export PYTHONPATH=./ |
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export MODEL_BASE=./weights |
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OUTPUT_BASEPATH=./results-poor |
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checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt |
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export CPU_OFFLOAD=1 |
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CUDA_VISIBLE_DEVICES=0 python3 hymm_sp/sample_gpu_poor.py \ |
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--input 'assets/test.csv' \ |
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--ckpt ${checkpoint_path} \ |
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--sample-n-frames 129 \ |
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--seed 128 \ |
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--image-size 704 \ |
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--cfg-scale 7.5 \ |
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--infer-steps 50 \ |
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--use-deepcache 1 \ |
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--flow-shift-eval-video 5.0 \ |
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--save-path ${OUTPUT_BASEPATH} \ |
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--use-fp8 \ |
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--cpu-offload \ |
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--infer-min |
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``` |
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## Run a Gradio Server |
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```bash |
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cd HunyuanVideo-Avatar |
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bash ./scripts/run_gradio.sh |
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``` |
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## 🔗 BibTeX |
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If you find [HunyuanVideo-Avatar](https://arxiv.org/pdf/2505.20156) useful for your research and applications, please cite using this BibTeX: |
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```BibTeX |
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@misc{hu2025HunyuanVideo-Avatar, |
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title={HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters}, |
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author={Yi Chen and Sen Liang and Zixiang Zhou and Ziyao Huang and Yifeng Ma and Junshu Tang and Qin Lin and Yuan Zhou and Qinglin Lu}, |
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year={2025}, |
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eprint={2505.20156}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/pdf/2505.20156}, |
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} |
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``` |
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## Acknowledgements |
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We would like to thank the contributors to the [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration. |
