Building Garfield’s Characters with Ziva Real Time

01. Range of motion

The animation we create to run the simulations on is crucial. This is where we’d need to define the full range of poses expected from the characters. We found providing a detailed animation produced much more stable results in the rigs.

We started by animating the individual joints like the elbows and wrists across the whole chain, then moved on to combinations of joints. The idea was to provide a dataset that encapsulated as much variety as possible. Once we had our first passes, we evaluated the results, identified any weaknesses, and added extra poses to the training to improve the results.

02. Ziva simulation

Next we created sim meshes and set up the Ziva simulation. We used internal bone meshes and flesh volumes, nothing that resembled anatomical muscles. Once we’d optimised the attachments and material layers, our work was almost done. We experimented with splitting the character into regions, which reduced sim times and simplified the setups. We continued splitting into regions for the animation and final Ziva RT deformers, then combined them in a final rig. The regions were split up like so:

• Torso: Spine/head/pelvis – Full simulation

• Left Arm: Clavicle/shoulder/elbow/wrist – Full simulation

• Right Arm: Mirror cache result from left arm simulation

• Left Leg: Hip/knee/ankle – Full simulation

• Right leg: Mirror cache result from left leg simulation

03. Sculpt

In some cases, we ran additional sculpting passes if we needed to art direct the results more than the simulation would allow. It was a minimal amount, but for characters like Vic there was a need to clean up the elbows and shoulders in some poses. As we developed sim setups, the need to clean up the sim cache reduced. We found that by adding more complexity to the sim, we could solve the issues more efficiently.

04. Create a ZRT solver

To create the Ziva RT solver we provided the trainer with a skeleton, anim ROM and sim cache. This produced the zrtPlayer node that runs inside Maya. Our workflow needs front-of-chain (FOC) blendShapes, which is where the face rig is connected. As such, we disabled the skinCluster element of the solver and were left with just the deltas. A solver for each region was produced and connected via geometry layers into the FOC blendShape. One advantage of this workflow was the ability to paint the blendShape weights, which gave us further control over the implementation of Ziva RT within the rig.

05. Wrap up The final rig

At this stage, the completed rig with Ziva RT embedded was now ready to use. As we treat the Ziva RT data as just typical front-of-chain corrective deltas, there was no change to our rigging setups, and all our existing deformation layers and setups worked with this addition of Ziva RT.

When the final builds were passed over, the animation team was thrilled with the fleshiness and volume preservation in the characters. The results had great appeal and looked very organic, and the animation artists felt they got the shapes and lines they had asked for in their draw-overs.