Icon Unrolling Rotations

 

Icon Animation Blend Spaces without Triangulation

 

Icon Quaternion Weighted Average

 

Icon BVHView

 

Icon Dead Blending Node in Unreal Engine

 

Icon Propagating Velocities through Animation Systems

 

Icon Cubic Interpolation of Quaternions

 

Icon Dead Blending

 

Icon Perfect Tracking with Springs

 

Icon Creating Looping Animations from Motion Capture

 

Icon My Favourite Things

 

Icon Inertialization Transition Cost

 

Icon Scalar Velocity

 

Icon Tags, Ranges and Masks

 

Icon Fitting Code Driven Displacement

 

Icon atoi and Trillions of Whales

 

Icon SuperTrack: Motion Tracking for Physically Simulated Characters using Supervised Learning

 

Icon Joint Limits

 

Icon Code vs Data Driven Displacement

 

Icon Exponential Map, Angle Axis, and Angular Velocity

 

Icon Encoding Events for Neural Networks

 

Icon Visualizing Rotation Spaces

 

Icon Spring-It-On: The Game Developer's Spring-Roll-Call

 

Icon Interviewing Advice from the Other Side of the Table

 

Icon Saguaro

 

Icon Learned Motion Matching

 

Icon Why Can't I Reproduce Their Results?

 

Icon Latinendian vs Arabendian

 

Icon Machine Learning, Kolmogorov Complexity, and Squishy Bunnies

 

Icon Subspace Neural Physics: Fast Data-Driven Interactive Simulation

 

Icon Software for Rent

 

Icon Naraleian Caterpillars

 

Icon The Scientific Method is a Virus

 

Icon Local Minima, Saddle Points, and Plateaus

 

Icon Robust Solving of Optical Motion Capture Data by Denoising

 

Icon Simple Concurrency in Python

 

Icon The Software Thief

 

Icon ASCII : A Love Letter

 

Icon My Neural Network isn't working! What should I do?

 

Icon Phase-Functioned Neural Networks for Character Control

 

Icon 17 Line Markov Chain

 

Icon 14 Character Random Number Generator

 

Icon Simple Two Joint IK

 

Icon Generating Icons with Pixel Sorting

 

Icon Neural Network Ambient Occlusion

 

Icon Three Short Stories about the East Coast Main Line

 

Icon The New Alphabet

 

Icon "The Color Munifni Exists"

 

Icon A Deep Learning Framework For Character Motion Synthesis and Editing

 

Icon The Halting Problem and The Moral Arbitrator

 

Icon The Witness

 

Icon Four Seasons Crisp Omelette

 

Icon At the Bottom of the Elevator

 

Icon Tracing Functions in Python

 

Icon Still Things and Moving Things

 

Icon water.cpp

 

Icon Making Poetry in Piet

 

Icon Learning Motion Manifolds with Convolutional Autoencoders

 

Icon Learning an Inverse Rig Mapping for Character Animation

 

Icon Infinity Doesn't Exist

 

Icon Polyconf

 

Icon Raleigh

 

Icon The Skagerrak

 

Icon Printing a Stack Trace with MinGW

 

Icon The Border Pines

 

Icon You could have invented Parser Combinators

 

Icon Ready for the Fight

 

Icon Earthbound

 

Icon Turing Drawings

 

Icon Lost Child Announcement

 

Icon Shelter

 

Icon Data Science, how hard can it be?

 

Icon Denki Furo

 

Icon In Defence of the Unitype

 

Icon Maya Velocity Node

 

Icon Sandy Denny

 

Icon What type of Machine is the C Preprocessor?

 

Icon Which AI is more human?

 

Icon Gone Home

 

Icon Thoughts on Japan

 

Icon Can Computers Think?

 

Icon Counting Sheep & Infinity

 

Icon How Nature Builds Computers

 

Icon Painkillers

 

Icon Correct Box Sphere Intersection

 

Icon Avoiding Shader Conditionals

 

Icon Writing Portable OpenGL

 

Icon The Only Cable Car in Ireland

 

Icon Is the C Preprocessor Turing Complete?

 

Icon The aesthetics of code

 

Icon Issues with SDL on iOS and Android

 

Icon How I learned to stop worrying and love statistics

 

Icon PyMark

 

Icon AutoC Tools

 

Icon Scripting xNormal with Python

 

Icon Six Myths About Ray Tracing

 

Icon The Web Giants Will Fall

 

Icon PyAutoC

 

Icon The Pirate Song

 

Icon Dear Esther

 

Icon Unsharp Anti Aliasing

 

Icon The First Boy

 

Icon Parallel programming isn't hard, optimisation is.

 

Icon Skyrim

 

Icon Recognizing a language is solving a problem

 

Icon Could an animal learn to program?

 

Icon RAGE

 

Icon Pure Depth SSAO

 

Icon Synchronized in Python

 

Icon 3d Printing

 

Icon Real Time Graphics is Virtual Reality

 

Icon Painting Style Renderer

 

Icon A very hard problem

 

Icon Indie Development vs Modding

 

Icon Corange

 

Icon 3ds Max PLY Exporter

 

Icon A Case for the Technical Artist

 

Icon Enums

 

Icon Scorpions have won evolution

 

Icon Dirt and Ashes

 

Icon Lazy Python

 

Icon Subdivision Modelling

 

Icon The Owl

 

Icon Mouse Traps

 

Icon Updated Art Reel

 

Icon Tech Reel

 

Icon Graphics Aren't the Enemy

 

Icon On Being A Games Artist

 

Icon The Bluebird

 

Icon Everything2

 

Icon Duck Engine

 

Icon Boarding Preview

 

Icon Sailing Preview

 

Icon Exodus Village Flyover

 

Icon Art Reel

 

Icon LOL I DREW THIS DRAGON

 

Icon One Cat Just Leads To Another

Simple Two Joint IK

Created on Jan. 18, 2017, 2:29 p.m.

ik

Most animation programmers know that in the case of two joints there exists an analytical solution to the inverse kinematics problem. In short, there is an equation which can give you the required joint angles if you plug in all of the different details of the joints and where you want the end effector to be. But look up exactly what this equation is online and you might be faced with equations that look like this.

This isn't the only way. Two joint IK can be extremely simple and intuitive. Here is a method I think is the easiest to understand and use:

Given the configuration of joints shown to the right, where a is the hip joint, b is the knee joint, c is the heel joint, and t is the target location for the heel, we can solve the IK problem with two simple steps.

The first step is to rotate the joints in the chain such that length of the vector from a to c matches the length of the vector from a to t. This will allow us to perform the second step of simply rotating the hip around until the heel slides into place.

Step 1: Extend/Contract the Joint Chain

Lets start by getting the lengths of the various vectors involved.

float eps = 0.01;
float lab = length(b - a);
float lcb = length(b - c);
float lat = clamp(length(t - a), eps, lab + lcb - eps);

Here we clamp the length between the hip and the target to the maximum extension of the two joints (which equals the sum of the lengths of the joint segments). We can also use some small value eps to limit this extension to some minimum and maximum amount so the leg never appears full extended or fully contracted.

ik0

Next we get the current interior angles of the hip and knee joints. For this we can just use the dot product method:

float ac_ab_0 = acos(clamp(dot(
    normalize(c - a), 
    normalize(b - a)), -1, 1));

float ba_bc_0 = acos(clamp(dot(
    normalize(a - b), 
    normalize(c - b)), -1, 1));

We clamp the output of the dot product in the range -1 to 1 before passing it to acos - this removes the occasional numerical errors caused by floating point arithmetic if the value goes out of the range 1 to -1.

We can now use The cosine rule to get the desired interior angles of these joints which produces a vector from the hip to the heel with the same length as the vector between the hip and the target.

float ac_ab_1 = acos(clamp((lcb*lcb-lab*lab-lat*lat) / (-2*lab*lat), -1, 1));
float ba_bc_1 = acos(clamp((lat*lat-lab*lab-lcb*lcb) / (-2*lab*lcb), -1, 1));

With the current angles and the desired angles, the final angles by which we have to rotate the hip and knee joints are just the difference between these two values, while the rotation axis is just the cross product between the vectors from the hip to the knee and the hip to the heel. Since we are working in the world space we have to first transform this axis into the local space of each joint (since the rotations will be applied locally) with the inverse of the global rotations of the hip a_gr and of the knee b_gr.

vec3 axis0 = normalize(cross(c - a, b - a));
quat r0 = quat_angle_axis(ac_ab_1 - ac_ab_0, quat_mul(quat_inv(a_gr), axis0)));
quat r1 = quat_angle_axis(ba_bc_1 - ba_bc_0, quat_mul(quat_inv(b_gr), axis0)));

Multiply this rotation to the right of the hip joint's local rotation a_lr, as well as the knee joint's local rotation b_lr, and the chain should now be correctly extended/contracted as shown in the gif above.

a_lr = quat_mul(a_lr, r0);
b_lr = quat_mul(b_lr, r1);

Step 2: Rotate the Heel into place

ik1

Now there is just one more rotation left. We must rotate the heel into place by rotating on the axis found by taking the cross product of the vector between the hip and heel, and the hip and target, with the angle found by taking the dot product between these two vectors. As before the axis must be transformed into the hip's local space.

float ac_at_0 = acos(clamp(dot(
    normalize(c - a), 
    normalize(t - a)), -1, 1));

vec3 axis1 = normalize(cross(c - a, t - a));

quat r2 = quat_angle_axis(ac_at_0, 
    quat_mul(quat_inv(a_gr), axis1)));

Just multiply this rotation on the right of the hip joint's local rotation and you're done!

a_lr = quat_mul(a_lr, r2);

Run your forward kinematics again and the heel joint should now be at the target location. That's all - a relatively mathematics free two joint IK. Full code is listed below.

void two_joint_ik(
    vec3 a, vec3 b, vec3 c, vec3 t, float eps,
    quat a_gr, quat b_gr, 
    quat &a_lr, quat &b_lr) {
  
    float lab = length(b - a);
    float lcb = length(b - c);
    float lat = clamp(length(t - a), eps, lab + lcb - eps);

    float ac_ab_0 = acos(clamp(dot(normalize(c - a), normalize(b - a)), -1, 1));
    float ba_bc_0 = acos(clamp(dot(normalize(a - b), normalize(c - b)), -1, 1));
    float ac_at_0 = acos(clamp(dot(normalize(c - a), normalize(t - a)), -1, 1));

    float ac_ab_1 = acos(clamp((lcb*lcb-lab*lab-lat*lat) / (-2*lab*lat), -1, 1));
    float ba_bc_1 = acos(clamp((lat*lat-lab*lab-lcb*lcb) / (-2*lab*lcb), -1, 1));

    vec3 axis0 = normalize(cross(c - a, b - a));
    vec3 axis1 = normalize(cross(c - a, t - a));    
    
    quat r0 = quat_angle_axis(ac_ab_1 - ac_ab_0, quat_mul(quat_inv(a_gr), axis0));
    quat r1 = quat_angle_axis(ba_bc_1 - ba_bc_0, quat_mul(quat_inv(b_gr), axis0));
    quat r2 = quat_angle_axis(ac_at_0, quat_mul(quat_inv(a_gr), axis1));

    a_lr = quat_mul(a_lr, quat_mul(r0, r2));
    b_lr = quat_mul(b_lr, r1);
}

Bonus: Extension/Contraction Axis

The above code works perfectly in most cases, but can occasionally produce small popping artifacts when the leg is already almost fully extended. This is because the cross product used in the line vec3 axis0 = normalize(cross(c - a, b - a)); becomes unstable when the vectors c - a and b - a start to point in almost the same direction.

To make this computation more stable we can instead introduce an additional vector d which controls the axis on which we bend the joints during the first stage of extension/contraction:

if (use_stable_extension) {
    vec3 axis0 = normalize(cross(c - a, d));
}

This axis d can be anything which is close to orthogonal to the vector from the hip to the foot, but it should usually be set to something like the direction in which the knee is pointing. For example, if your joints are oriented such that the Z axis is the forward facing direction you could compute it something like this:

vec3 d = quat_rotate(b_gr, vec3(0, 0, 1));

This will then ensure that the extension and contraction phase always produces a rotation around the axis perpendicular to this and the vector from the hip to the foot - an axis which should remain stable no matter what the extension of the joint chain is like.

github twitter rss