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Body Motion Capture Demo

For our body mocap module, we use HMR network architecture, by borrowing the implementation from SPIN with modifications. We trained the model with EFT dataset, showing the SOTA peformance among single-image based methods.

A Quick Start

    # Using a machine with a monitor to show output on screen
    # OpenGL renderer is used by default (--renderer_type opengl)
    # The output images are also saved in ./mocap_output
    python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output

    # Screenless mode (e.g., a remote server)
    xvfb-run -a python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output

    # Set other render_type to use other renderers
    python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output --renderer_type pytorch3d

Run Demo with a Webcam Input

  • Run, 
        python -m demo.demo_bodymocap --input_path webcam

    #or using opengl gui renderer
    python -m demo.demo_bodymocap --input_path webcam --renderer_type opengl_gui
  • See below to see how to control in opengl gui mode

Other Renderer Options

  • While opengl would be faster, it requires a screen connected to your machine. You may try screenless rendering or other rendering options described below.
  • Screenless Opengl Rendering
    • If you do not have a screen attached in your machine (e.g., remote servers), use xvfb-run tool
        # The output images are also saved in ./mocap_output
    xvfb-run -a python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output --renderer_type opengl
  • Pytorch3D
    • We use pytorch3d only for rendering purpose.
    • Run the following command to use pytorch3d renderer
        python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output --renderer_type pytorch3d
  • OpenDR
    • Alternatively, run the following command to use opendr renderer
        python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output --renderer_type opendr

Keys for GUI Mode

  • In OpenGL GUI visualization mode, you can use mouse and keyboard to change view point.
    • This mode requires a screen connected to your machine
    • Keys in OpenGL 3D window
      • mouse-Left: view rotation
      • mouse-Right: view zoom chnages
      • shift+ mouseLeft: view pan
      • C: toggle for image view/3D free view
      • w: toggle wireframe/solid mesh
      • j: toggle skeleton visualization
      • R: automatically rotate views
      • f: toggle floordrawing
      • q: exit program

Run Demo with Precomputed Bboxes

  • You can use the precomputed bboxes without running any detectors. Save bboxes for each image as a json format. Each json should contain the input image path.
  • Assuming your bboxes are /your/bbox_dir/XXX.json 
        python -m demo.demo_bodymocap --input_path /your/bbox_dir --out_dir ./mocap_output
  • Bbox format (json) 
    {"image_path": "xxx.jpg", "hand_bbox_list":[{"left_hand":[x,y,w,h], "right_hand":[x,y,w,h]}], "body_bbox_list":[[x,y,w,h]]}
    • Note that bbox format is [min_x, min_y, width, height]
  • For example 
    {"image_path": "./sample_data/images/cj_dance_01_03_1_00075.png", "body_bbox_list": [[149, 380, 242, 565]], "hand_bbox_list": [{"left_hand": [288.9151611328125, 376.70184326171875, 39.796295166015625, 51.72357177734375], "right_hand": [234.97779846191406, 363.4115295410156, 50.28489685058594, 57.89691162109375]}]}

Options

Input Options

  • --input_path webcam: Run demo for a video file (without using --vPath option)
  • --input_path /your/path/video.mp4: Run demo for a video file (mp4, avi, mov)
  • --input_path /your/dirPath: Run demo for a folder that contains image seqeunces
  • --input_path /your/bboxDirPath: Run demo for a folder that contains bbox json files. See bbox format

Output Options

  • --out_dir ./outputdirname: Save the output images into files
  • --save_pred_pkl: Save the pose reconstruction data (SMPL parameters and vertices) into pkl files (requires --out_dir ./outputdirname)
  • --save_bbox_output: Save the bbox data in json files (bbox_xywh format) (requires --out_dir ./outputdirname)
  • --no_display: Do not visualize output on the screen
  • --save_mesh: Saving vertices and faces when save predicting results (otherwise, only smpl/smplx parameters are saved)

Other Options

  • --use_smplx: Use SMPLX model for body pose estimation (instead of SMPL). This uses a different pre-trainined weights and may have different performance.
  • --start_frame 100 --end_frame 200: Specify start and end frames (e.g., 100th frame and 200th frame in this example)
  • --single_person: To enforce single person mocap (to avoid outlier bboxes). This mode chooses the biggest bbox.

Mocap Output Format (pkl)

As output, the 3D pose estimation data per frame is saved as a pkl file (with ```--pklout`` option). Each person's pose data is saved as follows:

'demo_type': ['body', 'hand', 'frank']
'smpl_type': ['smplx', 'smpl']
'pred_body_pose':  body pose parameters in angle-axis format # (24, 3, 3)
'pred_left_hand_pose': hand pose parameters in angle-axis format # (16, 3, 3)
'pred_betas': shape paramters # (10,)
'pred_camera':  #[cam_scale, cam_offset_x,, cam_offset_y ]
'pred_hand_bbox': bounding box for hand # {left_hand:[x,y,w,h], right_hand:[x,y,w,h]} or None
'pred_body_bbox': bounding box for body # [x,y,w,h]
'pred_vertices_smpl': # Original vertices from SMPL output
'pred_vertices_img': #3D SMPL vertices where X,Y are aligned to input image
'pred_joints_img': #3D joints where X,Y are aligned to input image

Load Saved Mocap Data (pkl file)

  • Run the following code to load and visualize saved mocap data files
#./mocap_output/mocap is the directory where pkl files exist
python -m  demo.demo_loadmocap --pkl_dir ./mocap_output/mocap
  • Note: current version uses GUI mode for the visualization (requiring a screen).
  • The current mocap output is redundant, and there are several options to visualize meshes from them
if False:    #One way to visualize SMPL from saved vertices
    tempMesh = {'ver': pred_vertices_imgspace, 'f':  smpl.faces}
    meshList=[]
    skelList=[]
    meshList.append(tempMesh)
    skelList.append(pred_joints_imgspace.ravel()[:,np.newaxis])  #(49x3, 1)

    visualizer.visualize_gui_naive(meshList, skelList)

elif False: #Alternative way from SMPL parameters
    pred_output = smpl(betas=pred_betas, body_pose=pred_rotmat[:,1:], global_orient=pred_rotmat[:,[0] ], pose2rot=False)
    pred_vertices = pred_output.vertices
    pred_joints_3d = pred_output.joints
    pred_vertices = pred_vertices[0].cpu().numpy()
    
    tempMesh = {'ver': pred_vertices_imgspace, 'f':  smpl.faces}
    meshList=[]
    skelList=[]
    body_bbox_list=[]
    meshList.append(tempMesh)
    skelList.append(pred_joints_imgspace.ravel()[:,np.newaxis])  #(49x3, 1)
    visualizer.visualize_gui_naive(meshList, skelList)

else: #Another alternative way using a funtion
    
    smpl_pose_list =  [ pred_rotmat[0].numpy() ]        #build a numpy array
    visualizer.visualize_gui_smplpose_basic(smpl, smpl_pose_list ,isRotMat=True )       #Assuming zero beta

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