Kimodo Motion Diffusion

from kimodo.model import Kimodo
# Initialize model (downloads automatically)model = Kimodo(model_name="Kimodo-SOMA-RP-v1")
# Simple text-to-motion generationresult = model(    prompts=["a person walks forward at a moderate pace"],    duration=4.0,    num_samples=1,    seed=42,)
# Result contains posed joints, rotation matrices, foot contactsprint(result["posed_joints"].shape)       # [T, J, 3]print(result["global_rot_mats"].shape)    # [T, J, 3, 3]print(result["local_rot_mats"].shape)     # [T, J, 3, 3]print(result["foot_contacts"].shape)      # [T, 4]print(result["root_positions"].shape)     # [T, 3]

from kimodo.model import Kimodoimport numpy as np
model = Kimodo(model_name="Kimodo-SOMA-RP-v1")
# Multi-prompt with classifier-free guidance controlresult = model(    prompts=["a person stands", "a person walks forward", "a person sits"],    duration=9.0,    num_samples=3,    diffusion_steps=50,    guidance_scale=7.5,           # classifier-free guidance weight    seed=0,)
# Access per-sample resultsfor i in range(3):    joints = result["posed_joints"][i]   # [T, J, 3]    print(f"Sample {i}: {joints.shape}")

from kimodo.model import Kimodofrom kimodo.constraints import ConstraintSet, FullBodyKeyframe, EndEffectorConstraintimport numpy as np
model = Kimodo(model_name="Kimodo-SOMA-RP-v1")
# Create constraint setconstraints = ConstraintSet()
# Add a full-body keyframe at frame 30 (1 second at 30fps)# keyframe_pose: [J, 3] joint positionskeyframe_pose = np.zeros((model.num_joints, 3))  # replace with actual poseconstraints.add_full_body_keyframe(frame=30, joint_positions=keyframe_pose)
# Add end-effector constraints for right handconstraints.add_end_effector(    joint_name="right_hand",    frame_start=45,    frame_end=60,    position=np.array([0.5, 1.2, 0.3]),   # [x, y, z] in meters    rotation=None,                           # optional rotation matrix [3,3])
# Add 2D waypoints for root pathconstraints.add_root_waypoints(    waypoints=np.array([[0, 0], [1, 0], [1, 1], [0, 1]]),  # [N, 2] in meters)
# Generate with constraintsresult = model(    prompts=["a person walks in a square"],    duration=6.0,    constraints=constraints,    num_samples=2,)

from kimodo.model import Kimodofrom kimodo.constraints import ConstraintSetimport json
model = Kimodo(model_name="Kimodo-SOMA-RP-v1")
# Load constraints saved from web demowith open("constraints.json") as f:    constraint_data = json.load(f)
constraints = ConstraintSet.from_dict(constraint_data)
result = model(    prompts=["a person performs a choreographed sequence"],    duration=8.0,    constraints=constraints,)

import numpy as np
# Save resultresult = model(prompts=["a person walks"], duration=4.0)np.savez("walk_motion.npz", **result)
# Load and inspect saved motiondata = np.load("walk_motion.npz")posed_joints = data["posed_joints"]       # [T, J, 3] global joint positionsglobal_rot_mats = data["global_rot_mats"] # [T, J, 3, 3]local_rot_mats = data["local_rot_mats"]   # [T, J, 3, 3]foot_contacts = data["foot_contacts"]     # [T, 4] [L-heel, L-toe, R-heel, R-toe]root_positions = data["root_positions"]   # [T, 3] actual root joint trajectorysmooth_root_pos = data["smooth_root_pos"] # [T, 3] smoothed root from modelglobal_root_heading = data["global_root_heading"]  # [T, 2] heading direction

# mujoco_load.py customization patternimport mujocoimport numpy as np
# Edit these paths in the scriptCSV_PATH = "./robot_walk.csv"MJCF_PATH = "./assets/g1/g1.xml"  # path to G1 MuJoCo model
# Load qpos dataqpos_data = np.loadtxt(CSV_PATH, delimiter=",")
# Standard MuJoCo playback loopmodel = mujoco.MjModel.from_xml_path(MJCF_PATH)data = mujoco.MjData(model)with mujoco.viewer.launch_passive(model, data) as viewer:    for frame_qpos in qpos_data:        data.qpos[:] = frame_qpos        mujoco.mj_forward(model, data)        viewer.sync()

from kimodo.model import Kimodoimport numpy as npfrom pathlib import Path
model = Kimodo(model_name="Kimodo-SOMA-RP-v1")output_dir = Path("./batch_outputs")output_dir.mkdir(exist_ok=True)
prompts = [    "a person walks forward",    "a person runs",    "a person jumps in place",    "a person sits down",    "a person picks up an object from the floor",]
for i, prompt in enumerate(prompts):    result = model(        prompts=[prompt],        duration=4.0,        num_samples=1,        seed=i,    )    out_path = output_dir / f"motion_{i:03d}.npz"    np.savez(str(out_path), **result)    print(f"Saved: {out_path}")

from kimodo.model import Kimodoimport numpy as np
prompt = "a person walks forward"models = ["Kimodo-SOMA-RP-v1", "Kimodo-SOMA-SEED-v1"]
results = {}for model_name in models:    model = Kimodo(model_name=model_name)    results[model_name] = model(        prompts=[prompt],        duration=4.0,        seed=0,    )    print(f"{model_name}: joints shape = {results[model_name]['posed_joints'].shape}")