Real-Time Inverse Kinematics for Generating Multi-Constrained Movements of Virtual Human Characters
Project description
JAX-IK
Official implementation for the paper: "Real-Time Inverse Kinematics for Generating Multi-Constrained Movements of Virtual Human Characters"
Demo
You can try out the JAX-IK library in this demo app
Install
You can install the library simply using pip:
pip install jax-ik
Example code
This is a small example code to solve the arm position on the smplx skeleton.
Sadly, we don't have the rights to add the SMPL-X skeleton to this repository. Therefore, you will have to obtain a .glb file of the smplx skeleton yourself.
For this install the smplx blender addon in blender, press on the "add" button in the Blender SMPL-X drawer.
Finally, go to File -> Export -> glTF 2.0 and follow the export dialog to save your .glb file.
import numpy as np
from jax_ik.objectives import DistanceObjTraj
from jax_ik.ik import InverseKinematicsSolver
# === Setup ===
hand = "left"
controlled_bones = [f"{hand}_collar", f"{hand}_shoulder", f"{hand}_elbow", f"{hand}_wrist"]
# Define bounds in degrees
angle_bounds_deg = {
"left_collar": ([-10, -10, -10], [10, 10, 10]),
"left_shoulder": ([-120, -140, -65], [70, 50, 25]),
"left_elbow": ([-100, -180, -10], [90, 10, 10]),
"left_wrist": ([-120, -70, -70], [90, 60, 80]),
}
# Flatten and convert to radians
bounds = []
for bone in controlled_bones:
lower, upper = angle_bounds_deg[bone]
for l, u in zip(lower, upper):
bounds.append((np.radians(l), np.radians(u)))
# === Initialize Solver ===
model_file = "smplx.glb" # Path to your SMPL-X .glb file
ik_solver = InverseKinematicsSolver(
model_file=model_file,
controlled_bones=controlled_bones,
bounds=bounds,
threshold=0.005,
num_steps=1000,
)
# === Target Setup ===
end_effector_bone = f"{hand}_wrist"
random_target = np.array([0.3, 0.2, 0.5]) # Some reachable 3D point
mandatory_objectives = [
DistanceObjTraj(
target_points=[random_target],
bone_name=end_effector_bone,
use_head=True,
weight=1.0,
)
]
# === Solve IK ===
initial_angles = np.zeros(len(controlled_bones) * 3, dtype=np.float32)
solved_angles, obj_value, steps = ik_solver.solve(
initial_rotations=initial_angles,
learning_rate=0.2,
mandatory_objective_functions=mandatory_objectives,
ik_points=5,
patience=200,
)
print(f"Solved in {steps} steps. Final objective: {obj_value:.4f}")
# === Render Result ===
ik_solver.render(angle_vector=solved_angles[-1], target_pos=[random_target], interactive=True)
More examples, and some open source models will be added in the following weeks.
LICENSE
Real-Time Inverse Kinematics for Generating Multi-Constrained Movements of Virtual Human Characters © 2025 by Hendric Voss is licensed under CC BY-NC-SA 4.0
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