tusosos/blender-motion-capture

Blender Motion Capture

Overview

Import, process, and retarget motion capture data in Blender using Python. Work with BVH/FBX mocap files, track camera and object motion from video footage, solve 3D camera paths, and clean up animation data — all scriptable from the terminal.

Instructions

1. Import BVH motion capture files

import bpy

bpy.ops.import_anim.bvh(
    filepath="/path/to/mocap.bvh",
    target='ARMATURE',
    global_scale=1.0,
    frame_start=1,
    use_fps_scale=False,
    rotate_mode='NATIVE',
    axis_forward='-Z', axis_up='Y'
)

armature = bpy.context.active_object
action = armature.animation_data.action
print(f"Imported: {armature.name}, Bones: {len(armature.data.bones)}, Frames: {action.frame_range}")

2. Import FBX with animation

bpy.ops.import_scene.fbx(
    filepath="/path/to/mocap.fbx",
    use_anim=True,
    ignore_leaf_bones=True,
    automatic_bone_orientation=True,
    primary_bone_axis='Y', secondary_bone_axis='X'
)

3. Retarget motion between armatures

from mathutils import Matrix

def retarget_motion(source_armature, target_armature, bone_mapping):
    """Retarget animation using a bone name mapping: {target_bone: source_bone}"""
    source_action = source_armature.animation_data.action
    frame_start, frame_end = int(source_action.frame_range[0]), int(source_action.frame_range[1])

    if not target_armature.animation_data:
        target_armature.animation_data_create()
    new_action = bpy.data.actions.new(f"{source_action.name}_retarget")
    target_armature.animation_data.action = new_action

    for frame in range(frame_start, frame_end + 1):
        bpy.context.scene.frame_set(frame)
        for tgt_name, src_name in bone_mapping.items():
            src = source_armature.pose.bones.get(src_name)
            tgt = target_armature.pose.bones.get(tgt_name)
            if not src or not tgt:
                continue
            tgt.rotation_quaternion = src.rotation_quaternion
            tgt.keyframe_insert(data_path="rotation_quaternion", frame=frame)
            # Copy location for root bone only
            if src_name == list(bone_mapping.values())[0]:
                tgt.location = src.location
                tgt.keyframe_insert(data_path="location", frame=frame)

# Example Mixamo → Rigify mapping
mapping = {
    "spine": "mixamorig:Hips", "spine.001": "mixamorig:Spine",
    "spine.004": "mixamorig:Neck", "spine.006": "mixamorig:Head",
    "upper_arm.L": "mixamorig:LeftArm", "forearm.L": "mixamorig:LeftForeArm",
    "upper_arm.R": "mixamorig:RightArm", "forearm.R": "mixamorig:RightForeArm",
    "thigh.L": "mixamorig:LeftUpLeg", "shin.L": "mixamorig:LeftLeg",
    "thigh.R": "mixamorig:RightUpLeg", "shin.R": "mixamorig:RightLeg",
}

4. Clean up motion capture data

def decimate_fcurve(fcurve, factor=0.5):
    """Remove keyframes to reduce data while keeping shape."""
    points = fcurve.keyframe_points
    total = len(points)
    keep_every = max(1, int(1.0 / factor))
    remove_indices = [i for i in range(total) if i % keep_every != 0 and i != 0 and i != total - 1]
    for i in reversed(remove_indices):
        points.remove(points[i])

armature = bpy.context.active_object
action = armature.animation_data.action
for fcurve in action.fcurves:
    decimate_fcurve(fcurve, factor=0.5)
    fcurve.update()

5. Video motion tracking and camera solve

# Load footage
clip = bpy.data.movieclips.load("/path/to/footage.mp4")
scene = bpy.context.scene
scene.active_clip = clip

# Configure tracking
tracking = clip.tracking
settings = tracking.settings
settings.default_pattern_size = 21
settings.default_search_size = 71
settings.default_motion_model = 'AFFINE'

# Camera settings for solving
camera = tracking.camera
camera.sensor_width = 36.0
camera.focal_length = 50.0

# Solve camera motion
bpy.ops.clip.solve_camera()
solve_error = tracking.reconstruction.average_error
print(f"Solve error: {solve_error:.4f} px ({'Good' if solve_error < 0.5 else 'Needs refinement'})")

# Set up scene from solved data
bpy.ops.clip.setup_tracking_scene()

6. Apply tracked motion to objects

obj = bpy.data.objects["MyObject"]
constraint = obj.constraints.new(type='FOLLOW_TRACK')
constraint.clip = clip
constraint.track = tracking.tracks["Marker_01"]
constraint.use_3d_position = True
constraint.camera = scene.camera

# Bake constraint to keyframes
bpy.context.view_layer.objects.active = obj
obj.select_set(True)
bpy.ops.nla.bake(
    frame_start=1, frame_end=clip.frame_duration,
    only_selected=True, visual_keying=True,
    clear_constraints=True, bake_types={'OBJECT'}
)

7. Export animation data

# Export as BVH
bpy.ops.export_anim.bvh(
    filepath="/tmp/output_mocap.bvh",
    frame_start=int(action.frame_range[0]),
    frame_end=int(action.frame_range[1]),
    rotate_mode='NATIVE'
)

# Export as FBX with baked animation
bpy.ops.export_scene.fbx(
    filepath="/tmp/output_anim.fbx",
    use_selection=True, bake_anim=True,
    bake_anim_use_all_bones=True, add_leaf_bones=False
)

Examples

Example 1: Batch scan mocap library

User request: "Import all BVH files from a folder, list bone counts and frame ranges"

import bpy, glob, os

for filepath in sorted(glob.glob("/path/to/mocap_library/*.bvh")):
    bpy.ops.object.select_all(action='SELECT')
    bpy.ops.object.delete()
    bpy.ops.import_anim.bvh(filepath=filepath, target='ARMATURE', global_scale=0.01, frame_start=1)
    arm = bpy.context.active_object
    if arm and arm.animation_data:
        action = arm.animation_data.action
        duration = (action.frame_range[1] - action.frame_range[0]) / bpy.context.scene.render.fps
        print(f"{os.path.basename(filepath)}: {len(arm.data.bones)} bones, {duration:.1f}s")

Run: blender --background --python scan_mocap.py

Example 2: Apply mocap to character and render

User request: "Import a BVH file, apply it to my rigged character, and render a preview"

import bpy

bpy.ops.wm.open_mainfile(filepath="/path/to/character.blend")
char_armature = bpy.data.objects["Armature"]

bpy.ops.import_anim.bvh(filepath="/path/to/walk_cycle.bvh", target='ARMATURE', global_scale=0.01)
mocap_armature = bpy.context.active_object
mocap_action = mocap_armature.animation_data.action

# Transfer action (works when bone names match)
if not char_armature.animation_data:
    char_armature.animation_data_create()
char_armature.animation_data.action = mocap_action

# Remove temp armature, set frame range, add camera, render
bpy.data.objects.remove(mocap_armature)
scene = bpy.context.scene
scene.frame_start, scene.frame_end = int(mocap_action.frame_range[0]), int(mocap_action.frame_range[1])
scene.render.filepath = "/tmp/mocap_preview/frame_"
bpy.ops.render.render(animation=True)

Guidelines

• BVH is simplest (plain text with hierarchy + motion). FBX supports richer data (blend shapes, multiple takes).
• Scale matters: BVH files often use centimeters. Set global_scale=0.01 for cm-based files.
• Bone name matching is critical for retargeting. Build a mapping dictionary for each source format.
• For retargeting, copy rotations for all bones but only location for the root/hip bone.
• Clean up imported mocap by decimating keyframes — raw mocap has every-frame keys, making editing difficult.
• Camera solve quality depends on marker count and distribution. Use 8+ well-distributed markers, keep error below 0.5px.
• Use bpy.ops.nla.bake() to convert constraints to keyframes for export.
• Always export with bake_anim=True in FBX to flatten NLA strips and constraints.