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LICENSE

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+========================================================================
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+Any redistribution of this sample code, including in binary form, must retain or reproduce the above copyright notice, conditions and disclaimer.

README.md

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+<div align="center">
+
+# FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control
+
+<a href="https://arxiv.org/abs/2602.xxxxx"><img src="https://img.shields.io/badge/arXiv-2602.xxxxx-b31b1b.svg" alt="arXiv"></a>
+<a href="https://huggingface.co/SandwichZ/Wan2.2-Fun-5B-FLEXAM"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow" alt="Hugging Face"></a>
+<a href="assets/flexam_workflow.json"><img src="https://img.shields.io/badge/ComfyUI-Download_Workflow-4fd63d" alt="ComfyUI"></a>
+
+<br>
+<br>
+
+Mingzhi Sheng<sup>1*</sup>, Zekai Gu<sup>2*</sup>, Peng Li<sup>2</sup>, Cheng Lin<sup>3</sup>, Hao-Xiang Guo<sup>4</sup>, Ying-Cong Chen<sup>1,2†</sup>, Yuan Liu<sup>2†</sup>
+
+<br>
+
+<sup>1</sup>HKUST(GZ), <sup>2</sup>HKUST, <sup>3</sup>MUST, <sup>4</sup>Tsinghua University
+<br>
+<small><sup>*</sup>Equal Contribution, <sup>†</sup>Corresponding Authors</small>
+
+</div>
+
+<br>
+
+![teaser](assets/teaser.gif)
+
+## 📰 News
+- **[2026.02.13]** 🚀 We have released the inference code and **ComfyUI** support!
+- **[2026.02.14]** 📄 The paper is available on arXiv.
+
+
+## 🛠️ Installation  
+> 📢 **System Requirements**: Both the official Python inference code and the ComfyUI workflow were tested on **Ubuntu 20.04** with **Python 3.10**, **PyTorch 2.5.1**, and **CUDA 12.1** on an **NVIDIA A800** GPU.
+
+Before running any inference (Python or ComfyUI), please setup the environment and download the checkpoints.  
+
+### 1. Create environment
+ Clone the repository and create conda environment: 
+
+```
+git clone https://github.com/IGL-HKUST/FlexAM
+conda create -n flexam python=3.10
+conda activate flexam
+```
+
+Install pytorch, we recommend `Pytorch 2.5.1` with `CUDA 12.1`:
+
+ ```
+pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121
+```
+```
+pip install -r requirements.txt
+```
+### 2. Download Submodules
+ We rely on several external modules (MoGe, Pi3, etc.).
+ 
+```
+mkdir -p submodules
+git submodule update --init --recursive
+pip install -r requirements.txt
+```
+<details> 
+<summary><em>(Optional) Manual clone if submodule update fails</em></summary>
+```
+# DELTA
+git clone https://github.com/snap-research/DELTA_densetrack3d.git submodules/MoGe
+# Pi3
+git clone https://github.com/yyfz/Pi3.git submodules/Pi3
+# MoGe
+git clone https://github.com/microsoft/MoGe.git submodules/MoGe
+# VGGT
+git clone https://github.com/facebookresearch/vggt.git submodules/vggt
+```
+</details>
+
+### 3. Download checkpoints
+Download the FlexAM checkpoint and place it in the`checkpoints/` directory.
+
+- HuggingFace Link: [Wan2.2-Fun-5B-FLEXAM](https://huggingface.co/SandwichZ/Wan2.2-Fun-5B-FLEXAM)
+
+
+
+## 🚀 Inference
+We provide two ways to use FlexAM: Python Script and ComfyUI.
+
+### Option A: ComfyUI Integration
+We provide a native node for seamless integration into ComfyUI workflows.
+> ⚠️ **Note**: Currently, the ComfyUI node supports **Motion Transfer**, **Foreground Edit**, and **Background Edit**. For *Camera Control* and *Object Manipulation*, please use the Python script.
+#### 1. Install Node
+Since we are not yet in the Manager, please install manually:
+```
+cd ComfyUI/custom_nodes/
+git clone https://github.com/IGL-HKUST/FlexAM
+cd FlexAM
+pip install -r requirements.txt
+```
+#### 2. Run Workflow
+- Step 1: Download the workflow JSON: [workflow.json](assets/flexam_workflow.json)
+- Step 2: Drag and drop it into ComfyUI.
+- Step 3: Ensure checkpoints are in `ComfyUI/models/checkpoints`.
+
+### Option B: Python Script
+
+We provide a inference script for our tasks. Please refer to `run_demo.sh` to run the `demo.py` script.
+
+Or you can run these tasks one by one as follows.
+
+#### 1. Motion Transfer 
+![motion_transfer](assets/motion_transfer.gif)
+---
+
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --output_dir <output_dir> \ # output directory
+    --input_path <input_path> \ # the reference video path
+    --repaint <True/repaint_path > \ # the repaint first frame image path of input source video or use FLUX to repaint the first frame \
+    --video_length=97 \ 
+    --sample_size 512 896 \
+    --generate_type='full_edit' \
+    --density 10 \ # Control the sparsity of tracking points
+    --gpu <gpu_id> \ # the gpu id
+```
+
+
+#### 2. foreground edit
+![fg_edit](assets/fg_edit.gif)
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --output_dir <output_dir> \ # output directory
+    --input_path <input_path> \ # the reference video path
+    --repaint <True/repaint_path > \ # the repaint first frame image path of input source video or use FLUX to repaint the first frame \
+    --mask_path <mask_path> \ # White (255) represents the foreground to be edited, and black (0) remains unchanged
+    --video_length=97 \ 
+    --sample_size 512 896 \
+    --generate_type='foreground_edit' \ 
+    --dilation_pixels=30 \ # Dilation pixels for mask processing in foreground_edit mode
+    --density 10 \ # Control the sparsity of tracking points
+    --gpu <gpu_id> \ # the gpu id
+```
+
+#### 3. background edit
+![bg_edit](assets/bg_edit.gif)
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --output_dir <output_dir> \ # output directory
+    --input_path <input_path> \ # the reference video path
+    --repaint <True/repaint_path > \ # the repaint first frame image path of input source video or use FLUX to repaint the first frame \
+    --mask_path <mask_path> \ # White (255) represents the unchanged foreground, while the background indicates the area to be edited
+    --video_length=97 \ 
+    --sample_size 512 896 \
+    --generate_type='background_edit' \
+    --density 10 \ # Control the sparsity of tracking points
+    --gpu <gpu_id> \ # the gpu id
+```
+
+#### 4. Camera Control
+![camera_ctrl](assets/camera_ctrl.gif)
+
+We provide three camera control methods: 1. Use predefined templates; 2. Use a pose text file (pose txt); 3. Input another video, where the "Pi3" automatically estimates the camera pose from it and applies it to the video to be generated.
+
+##### 1. Use predefined templates
+
+We provide several template camera motion types, you can choose one of them. In practice, we find that providing a description of the camera motion in prompt will get better results.
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --output_dir <output_dir> \ # output directory
+    --input_path <input_path> \ # the reference image or video path
+    --camera_motion <camera_motion> \ # the camera motion type, see examples below
+    --tracking_method <tracking_method> \ # the tracking method (moge, DELTA). For image input, 'moge' is necessary.
+    --override_extrinsics <override/append> \ # how to apply camera motion: "override" to replace original camera, "append" to build upon it
+    --video_length=97 \ 
+    --sample_size 512 896 \    
+    --density 5 \ # Control the sparsity of tracking points
+    --gpu <gpu_id> \ # the gpu id
+```
+
+Here are some tips for camera motion:
+- trans: translation motion, the camera will move in the direction of the vector (dx, dy, dz) with range [-1, 1]
+  - Positive X: Move left, Negative X: Move right
+  - Positive Y: Move down, Negative Y: Move up
+  - Positive Z: Zoom in, Negative Z: Zoom out
+  - e.g., 'trans -0.1 -0.1 -0.1' moving right, down and zoom in
+  - e.g., 'trans -0.1 0.0 0.0 5 45' moving right 0.1 from frame 5 to 45
+- rot: rotation motion, the camera will rotate around the axis (x, y, z) by the angle
+  - X-axis rotation: positive X: pitch down, negative X: pitch up
+  - Y-axis rotation: positive Y: yaw left, negative Y: yaw right
+  - Z-axis rotation: positive Z: roll counter-clockwise, negative Z: roll clockwise
+  - e.g., 'rot y 25' rotating 25 degrees around y-axis (yaw left)
+  - e.g., 'rot x -30 10 40' rotating -30 degrees around x-axis (pitch up) from frame 10 to 40
+- spiral: spiral motion, the camera will move in a spiral path with the given radius
+  - e.g., 'spiral 2' spiral motion with radius 2
+  - e.g., 'spiral 2 15 35' spiral motion with radius 2 from frame 15 to 35
+
+Multiple transformations can be combined using semicolon (;) as separator:
+- e.g., "trans 0 0 -0.5 0 30; rot x -25 0 30; trans -0.1 0 0 30 48"
+  This will:
+  1. Zoom in (z-0.5) from frame 0 to 30
+  2. Pitch up (rotate -25 degrees around x-axis) from frame 0 to 30
+  3. Move right (x-0.1) from frame 30 to 48
+
+Notes:
+- If start_frame and end_frame are not specified, the motion will be applied to all frames (0-48)
+- Frames after end_frame will maintain the final transformation
+- For combined transformations, they are applied in sequence
+
+
+##### 2. Use a pose text file (pose txt)
+
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --output_dir <output_dir> \ # output directory
+    --input_path <input_path> \ # the reference image or video path
+    --camera_motion "path" \ # if camera motion type is "path", --pose_file is needed
+    --pose_file <pose_file_txt> \ # txt file of camera pose, Each line corresponds to one frame
+    --tracking_method <tracking_method> \ # the tracking method (moge, DELTA). For image input, 'moge' is necessary.
+    --override_extrinsics <override/append> \ # how to apply camera motion: "override" to replace original camera, "append" to build upon it
+    --video_length=97 \ 
+    --sample_size 512 896 \    
+    --density 5 \ # Control the sparsity of tracking points
+    --gpu <gpu_id> \ # the gpu id
+```
+
+##### 3. Input another video for extract camera pose
+
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --output_dir <output_dir> \ # output directory
+    --input_path <input_path> \ # the reference image or video path
+    --camera_motion "path" \ # if camera motion type is "path", --pose_file is needed
+    --pose_file <pose_file_mp4> \ # "Pi3" automatically estimates the camera pose from this video file 
+    --tracking_method <tracking_method> \ # the tracking method (moge, DELTA). For image input, 'moge' is necessary.
+    --override_extrinsics <override/append> \ # how to apply camera motion: "override" to replace original camera, "append" to build upon it
+    --video_length=97 \ 
+    --sample_size 512 896 \    
+    --density 5 \ # Control the sparsity of tracking points
+    --gpu <gpu_id> \ # the gpu id
+```
+
+
+#### 5. Object Manipulation
+![object](assets/object.gif)
+We provide several template object manipulation types, you can choose one of them. In practice, we find that providing a description of the object motion in prompt will get better results.
+```python
+python demo.py \
+    --prompt <"prompt text"> \ # prompt text
+    --checkpoint_path <model_path> \ # FlexAM checkpoint path (e.g checkpoints/Diffusion_Transformer/Wan2.2-Fun-5B-FLEXAM)
+    --input_path <input_path> \ # the reference image path
+    --object_motion <object_motion> \ # the object motion type (up, down, left, right)
+    --object_mask <object_mask_path> \ # the object mask path
+    --tracking_method <tracking_method> \ # the tracking method (moge, DELTA). For image input, 'moge' is nesserary.
+    --sample_size 512 896 \
+    --video_length=49 \
+    --density 30 \    
+    --gpu <gpu_id> \ # the gpu id
+```
+It should be noted that depending on the tracker you choose, you may need to modify the scale of translation.
+
+
+## 🙏 Acknowledgements
+
+This project builds upon several excellent open source projects:
+
+* [VideoX-Fun](https://github.com/aigc-apps/VideoX-Fun)
+
+* [DELTA](https://github.com/snap-research/DELTA_densetrack3d)
+
+* [MoGe](https://github.com/microsoft/MoGe)
+
+* [vggt](https://github.com/facebookresearch/vggt)
+
+* [Pi3](https://github.com/yyfz/Pi3)
+
+We thank the authors and contributors of these projects for their valuable contributions to the open source community!
+
+## 🌟 Citation
+If you find FlexAM useful for your research, please cite our paper:
+```
+
+```
+
+

assets/flexam_workflow.json

@@ -0,0 +1 @@
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