Sparse Rig Parameter Optimization for Character Animation J. Song, R. Riverba, K. Cho, M. You, J. P. Lewis, B. Choi, J, Noh Johnno De Vos and Joris Brandt

Summary: What are we talking about? A technology to help retarget motion from one rig to another with minimal loss and/or computational power Example: record animation on non-final model, transfer it to new one later Useful for retargeting motion capture data to a rigged model

What is Motion Retargeting? It’s a process by which animation/movement is translated from one rig or mesh to another Can be very challenging with very different target objects Used a lot for motion capture data, used to rebind data from mocap skeleton to the final model Photo credit: Edu.tr

What is Sparse Rig Parameter Optimization Motion retargeting using an intermediate object Animator has to do less manual editing Transferring source motion to the intermediate mesh Optimizing the intermediate object motion to reduce errors Robust handling of various types of input

Input Takes source motion and rigged target character as input Robust system that takes various input Motion Capture Skeletal motions Mesh animation Any combination

Skeletal Motion Retargeting Intermediate skeleton is created based on source motion and target If source and target have skeleton, intermediate skeleton is created Source motion is retargeted to intermediate skeleton Using Lie group representation saves costs Lie group provides linearized manifold

Mesh Deformation Transfer Take corresponding points on source and target respective surfaces Create a mesh using Delaunay tetrahedralization of source points Make intermediate mesh Uses mesh structure of previous created mesh Uses points of the target character Transfer the source motion to intermediate object Transfer of deformation gradients Delaunay tetrahedralization gebruikt cirkels om midden van driehoeken te bepalen en maakt daar een mesh van

Keyframe Extraction Source animations may contain too many frames Multiscale motion saliency is measured for each frame Calculates most important frames Extract keyframes that best describe the motion Saves computation time and editing for animator Motion saliency from Halit and Capin Calculated by taking Gaussian weighted average of each point

Rig Space Optimization Optimization is posed as a minimization problem Trying to minimize the difference between intermediate and target character Minimizing the energy difference in a frame p(c) ≈ p(c(t))+Jit (c(t))W(c(t))(c-c(t)) P = pose C = space rig parameters T = frame Jit = Jacobian of target character rig at iteration step W = csontraint weight matrix (identity matrix if undefined)

Rig Space Optimization Temporal Smoothness and lack of popping is ensured by limiting acceleration energy at a given time-step For further optimization energy is one again limited. Motion errors for each keyframe are compared to a global motion error threshold. For each keyframe motion error count that’s too high a new keyframe is generated taking over the maximum pose error. Prior Jacobian has necessary components updated at each iteration, if required.

Related Work: Optimizing motion in Rig Space (Hahn et al. 2013) Augmentation of existing Keyframe animation procedures: secondary motions generated through physics simulation of movement based on existing rig. Allows keeping of source rig, which saves on work time. Can be further edited by simply modifying existing parameters of the rig space

Related Work: Inverse Rig Mapping (Holden, Saito, and Komura 2015) Using non-linear regression on example animation provided. Learn the motions from the example data to estimate the motions on a different rig. Similar goal: increase productivity by providing system which could remap animation out of the box. Problem: data dependant, quality of results directly proportional to quality and consistency of input data. Does not generalize well to rigs with too many differences.

Results

Results Two teams: one experienced, one slightly less experienced.Asked to make animation with one of three techniques: Keyframing, using commercial software, and using Sparse Rig Parameter Optimization Animation Length in Frames (30 FPS) Working time Sword Fight 1140 Keyframing 6.4 Hours Mocap + Software 3 Hours Mocap + SRPO 1.8 Hours Characters Dancing 2700 6 Hours 2.1 Hours

Results Sample size very small: not necessarily representative Generally less error-prone than other techniques Sparse Rig Parameter Optimization reduced work time in both examples

Conclusion Sparse Rig Parameter Optimization can be a valid technique for creating animations from motion capture data. With correct setup of rig parameters can cut down significantly on working time, and increase productivity. More working examples required before determining if the technique is innately superior to other, commercially available systems and techniques

Thank you for listening! Any questions?

Discussion Is the system’s generalisation actually good if it’s so dependent on inputs? Is it useful for large animation teams if individual preferences or differences in style can generate errors? Perhaps less so, if all time gained is spent making up for issues in production Will this system improve or degrade productivity for smaller animation teams? If it saves time for animators, probably improve