MusePose: a Pose-Driven Image-to-Video Framework for Virtual Human Generation

MusePose: A Pose-Driven Image-to-Video Framework for Virtual Humans

Introduction MusePose is a diffusion-based, pose-guided framework designed to generate virtual humans in video form. By taking a reference image of a person and a sequence of target poses, MusePose creates plausible, coherent motion that preserves the character and appearance of the reference. Built as part of the Muse open-source ecosystem, MusePose stands alongside MuseV and MuseTalk in a broader vision: enabling end-to-end generation of virtual humans with natural, full-body movement and interactive capabilities. The project credits the broader AIGC community, drawing inspiration from AnimateAnyone and Moore-AnimateAnyone while delivering its own improvements and practical tooling.

What MusePose Offers

• Pose-driven image-to-video synthesis: A capable system that maps pose sequences to dynamic video output conditioned on a reference image.
• Pose alignment algorithm: A key efficiency and usability enhancement that aligns arbitrary dance video motion to arbitrary reference images, dramatically improving inference performance and user experience.
• Build and contribution ethos: The project emphasizes openness, with train codes released and a trajectory toward more advanced architectures and broader demonstrations.

Foundational Context and Acknowledgments MusePose is not built from scratch in isolation. It leverages established components and prior work, including the AnimateAnyone framework and Moore-AnimateAnyone, reusing their ideas and code as a foundation while implementing improvements specific to MusePose’s diffusion-based generation pipeline. The project also acknowledges the broader diffusion and open-source communities, including diffusers, Stable Diffusion, and related tools, whose work underpins MusePose’s capabilities. The intent is to accelerate experimentation, enable community contributions, and push toward a seamless end-to-end virtual human creation workflow.

Demos, News, and Progress MusePose maintains an active development and release cadence, with milestones that include:

• Release of MusePose and pretrained models, offering a practical starting point for researchers and developers.
• Integration efforts such as ComfyUI-MusePose, enabling more accessible crafting of prompts and workflows.
• Bug fixes and stability improvements to the inference pipeline.
• A future plan to release training codes, broader demonstrations, and enhanced architectures.

Todo and Milestones

• Completed:
• Release of trained models and inference codes for MusePose.
• Release of the pose alignment algorithm.
• ComfyUI integration for MusePose.
• Training guidelines to help researchers onboard quickly.
• In progress or planned:
• An improved architecture and model (potentially longer development time).
• HuggingFace Gradio demos to broaden accessibility and demo quality.

Getting Started: A Guided Entry into MusePose The MusePose project provides a practical, end-to-end pathway for new users to install, configure, and run the system. The guidance is designed to reduce barriers to entry while offering room for advanced users to tailor the pipeline to their datasets and hardware.

Installation and Environment Setup

• Recommended software stack:
• Python version: 3.10 or higher.
• CUDA version: 11.7.
• Building the environment:
• Start by installing core Python dependencies via a requirements file. This step ensures you have the necessary libraries for diffusion, image processing, and model utilities.
• Key packages for machine learning and perception:
• OpenMIM, mim, and related package managers to install core components such as mmengine, mmcv, mmdet, and mmpose. These packages deliver essential model components for detection, pose estimation, and diffusion-based operations.
• Weights and model assets:
• MusePose weights and auxiliary components are distributed across HuggingFace repositories and similar hosting platforms. You will download:
  • MusePose trained weights
  • sd-image-variations-diffusers, sd-vae-ft-mse
  • dwpose, yolox model weights, image_encoder weights
  • controlv11psd15_openpose for training
  • animatediff for training
• After downloading, organize weights under a pretrained_weights directory with a clear, hierarchical structure that mirrors MusePose’s repository layout.

Quickstart: Running Inference with MusePose A well-structured workflow is provided to guide users from preparation to finished output. The quickstart covers preparation, pose alignment, inference, resource considerations, and optional face enhancement.

Preparation: Organizing Assets

• Prepare reference and motion data:
• A reference image (the character’s appearance) stored under assets/images/ref.png.
• A dance video that drives motion, stored under assets/videos/dance.mp4.
• The assets directory serves as the standard input location so the pipeline can locate the image and motion data during pose alignment and inference.

Pose Alignment: Aligning Motion to the Reference

• The pose alignment step uses a dedicated script to align the pose from a motion video to the reference image, generating a new aligned pose sequence that can be consumed by MusePose during inference.
• Command pattern:
• Run a pose_align script with the reference image and the dance video as input:
  • Example: posealign.py --imgfnrefer ./assets/images/ref.png --vidfn ./assets/videos/dance.mp4
• Output:
• Aligned pose sequences appear in the assets/poses directory, including:
  • align/imgrefvideo_dance.mp4 (the aligned pose video)
  • aligndemo/imgrefvideodance.mp4 (debug visualization for quick checks)

Inference: Generating the Virtual Human Video

• The test configuration file provides a bridge between the prepared data and the model’s inference logic.
• Steps:
• Update the test configuration (for example, configs/teststage2.yaml) to point to:
  • The reference image path: "./assets/images/ref.png"
  • The aligned pose path: "./assets/poses/align/imgrefvideo_dance.mp4"
• Run the inference script with the configuration:
  • Example: python teststage2.py --config ./configs/teststage2.yaml
• Output:
• Generated results appear in the output directory, offering a direct look at the created video frames and sequences.

VRAM Management and Resolution Trade-offs

• If VRAM is a constraint, MusePose supports reducing the inference resolution to manage memory usage.
• Example adjustment: run teststage2.py with width and height parameters such as -W 512 -H 512.
• The pipeline first renders at reduced resolution (512x512) and then resizes to match the original pose video resolution.
• Practical memory references:
• Typical runs at 512x512x48 may require around 16GB VRAM.
• Higher resolution runs (768x768x48) can require around 28GB VRAM.
• Note about quality:
• Scaling down can affect final results, particularly in facial regions and fine details. Face fidelity may degrade with reduced resolution; users seeking higher fidelity should balance memory capabilities with desired output quality.

Face Enhancement Options

• For improved facial consistency, MusePose can integrate with external face enhancement tools.
• A commonly used option is to leverage FaceFusion to perform face swapping or refinement, potentially improving identity preservation and expression accuracy in the generated video.

Training: From Data to Model MusePose also outlines a clear path for training the model in two stages, including data preparation, accelerator configuration, and script-level commands.

Data Preparation for Training

• Organize datasets: place all target dance videos into a single folder (e.g., ./xxx).
• Extract pose keypoints:
• Run a script to extract dwpose keypoints from the videos:
  • Example: python extractdwposekeypoints.py --video_dir ./xxx
• The extracted keypoints are stored in a companion directory (e.g., ./xxxdwposekeypoints).
• Visualize pose sequences:
• A script can render the dwpose visualization to verify motion and motion boundaries:
  • Example: python drawdwpose.py --videodir ./xxx
• Rendered outputs may be stored in:
  • ./xxxdwposewithoutface (if drawface=False)
  • ./xxxdwpose (if drawface=True)
• Metadata consolidation:
• A final script builds a dataset JSON (e.g., meta/xxx.json) that records paths to all data samples. This meta file guides subsequent training phases.

Configuring Distributed Training

• Accelerate and DeepSpeed:
• Install the accelerate library and use accelerate config to tailor DeepSpeed settings to your hardware.
• MusePose describes a setup that uses Zero Redundancy Optimizer (Zero 2) without offloading on a machine with multiple GPUs, such as 8x80GB GPUs.
• YAML configuration:
• Training is driven by two stages:
  • trainstage1.yaml
  • trainstage2.yaml
• You’ll configure these files to reference the prepared data, the model components, and the diffusion pipeline.

Launching Training

• Stage 1:
• Command: accelerate launch trainstage1multiGPU.py --config configs/trainstage_1.yaml
• Stage 2:
• Command: accelerate launch trainstage2multiGPU.py --config configs/trainstage_2.yaml

Acknowledgements and Community Ethos MusePose acknowledges the influence of AnimateAnyone’s technical report and Moore-AnimateAnyone’s code base, both of which accelerated early development. The project also expresses gratitude toward the broader open-source ecosystem: AnimateDiff, DWPose, Stable Diffusion, and other foundational projects that have paved the way for diffusion-based image-to-video work. The authors invite continued community engagement and collaboration as the Muse open-source series evolves.

Limitations: What to Expect and Where to Improve

• Detail fidelity limitations:
• Some fine-grained character details may not be perfectly preserved, especially in the face region and highly complex clothing. This is a known challenge in pose-driven video generation where identity-specific details can drift during long sequences.
• Visual noise and flickering:
• In scenes with complex backgrounds or challenging lighting, the system may exhibit noise or flickering artifacts. These issues are an active area of improvement and can often be mitigated through higher-resolution inputs, improved alignment, or post-processing.
• Generalization:
• While MusePose performs well on many reference images and pose sequences, extreme pose configurations or significant variations in wardrobe, accessories, or prop usage may require additional data and targeted training to maintain robustness.

Citations and Scholarship For researchers and readers who want to trace the scholarly context and formalizes MusePose’s contributions, a citation is provided:

• Tong, Zhengyan; Li, Chao; Chen, Zhaokang; Wu, Bin; Zhou, Wenjiang. MusePose: a Pose-Driven Image-to-Video Framework for Virtual Human Generation. arXiv, 2024. This reference anchors MusePose within the architectural and diffusion-based literature, while recognizing the practical engineering efforts that underpin the released code and pretrained assets.

License, Usage, and Responsible Deployment

• Code license:
• MusePose code is released under the MIT License, enabling broad academic and commercial experimentation with minimal restriction.
• Model usage:
• Trained models are available for non-commercial research purposes only.
• Open-source components:
• Other open-source models used within MusePose must conform to their license terms, such as ft-mse-vae, dwpose, and related assets.
• Data policy:
• Test data used in demonstrations are sourced from the internet and are intended for non-commercial research. When deploying or sharing outputs, users should respect privacy, consent, and copyright considerations.
• AIGC responsibility:
• While MusePose enables creative video generation, users are encouraged to comply with local laws and ethical guidelines. The developers do not assume responsibility for misuse and encourage responsible use.

Imagery and Visuals in MusePose Note on imagery in this blog post:

• The input provided for MusePose did not include embedded image files or media assets. As a result, this description does not embed actual pictures. When you prepare your own deployment or documentation, consider including:
• Architecture diagrams showing the diffusion pipeline and pose-guided conditioning.
• Screenshots of reference images and corresponding generated videos to illustrate identity preservation and motion fidelity.
• Visualizations of the pose alignment process, including before-and-after alignment frames.
• If you can supply image assets, you may place them in a dedicated imagery section, with alt text describing each figure to help readers understand the visual narrative of MusePose’s workflow.

Conclusion: MusePose as a Flexible, Community-Driven Tool MusePose offers a practical, robust pathway from a single reference image and a sequence of poses to a coherent video of a virtual human performing the target motion. It highlights a key innovation—the pose align algorithm—that enhances performance and usability, allowing users to adapt arbitrary motion sources to any reference image with greater efficiency. By releasing training codes, pre-trained models, and integration hooks for tools like ComfyUI, MusePose invites the community to iterate, optimize, and expand the capabilities of pose-driven virtual humans.

As the Muse open-source series progresses with MuseV and MuseTalk, the project aims to build a more complete ecosystem where end-to-end generation, interaction, and multi-modal control become increasingly accessible. The roadmap signals ongoing improvements in architecture, training protocols, and user-facing demos, with a strong emphasis on responsible AI usage and community contribution.

If you are exploring virtual human generation, MusePose offers a concrete, well-documented entry point that balances technical depth with practical steps. Whether you are a researcher investigating diffusion-based video synthesis or a developer seeking a ready-to-run pipeline for creative projects, MusePose provides a solid foundation, clear guidance, and an active community to support your journey.