Kinematics & Motion Capture
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1 Lecture 27: Kinematics & Motion Capture Computer Graphics and Imaging UC Berkeley CS184/284A, Spring 2017
2 Forward Kinematics (Slides with James O Brien)
3 Forward Kinematics Articulated skeleton Topology (what s connected to what) Geometric relations from joints Tree structure (in absence of loops) Joint types Pin (1D rotation) Ball (2D rotation) Prismatic joint (translation)
4 Forward Kinematics Example: simple two segment arm in 2D z x Warning: Z up Coordinate System
5 Forward Kinematics Animator provides angles, and computer determines position p of end-effector z End effector x Warning: Z up Coordinate System
6 Forward Kinematics Animation is described as angle parameter values as a function of time z 1 t x 2 t Warning: Z up Coordinate System
7 Example: Walk Cycle Articulated figure: Watt & Watt Slide credit: Tom Funkhouser
8 Example: Walk Cycle Hip joint angle Watt & Watt Slide credit: Tom Funkhouser
9 Example: Walk Cycle Knee joint angle Watt & Watt Slide credit: Tom Funkhouser
10 Example: Walk Cycle Ankle joint angle Watt & Watt Slide credit: Tom Funkhouser
11 Example Walk Cycle CS184/284A Ren Ng
12 Inverse Kinematics
13 Inverse Kinematics Egon Pasztor
14 Inverse Kinematics Animator provides position of end-effector, and computer must determine joint angles that satisfy constraints z p x p z t x t
15 Inverse Kinematics Direct inverse kinematics: for two-segment arm, can solve for parameters analytically z p x p z t x t
16 Inverse Kinematics Why is the problem hard? Multiple solutions separated in configuration space
17 Inverse Kinematics Why is the problem hard? Multiple solutions connected in configuration space
18 Inverse Kinematics Why is the problem hard? Solutions may not always exist
19 Inverse Kinematics Numerical solution to general N-link IK problem Choose an initial configuration Define an error metric (e.g. square of distance between goal and current position) Compute gradient of error as function of configuration Apply gradient descent (or Newton s method, or other optimization procedure)
20 Style-Based IK Grochow et al., Style Based Inverse Kinematics
21 Kinematics Pros and Cons Strengths Direct control is convenient Implementation is straightforward Weaknesses Animation may be inconsistent with physics Time consuming for artists CS184/284A Ren Ng
22 Rigging
23 Rigging Rigging is a set of higher level controls on a character that allow more rapid & intuitive modification of pose, deformations, expression, etc. Important Like strings on a puppet Captures all meaningful character changes Varies from character to character Expensive to create Manual effort Requires both artistic and technical training CS184/284A Ren Ng
24 Types of Rigging Procedural Rigging Skeletal Rigging Anatomical Rigging Al Barr. Global and Local Deformations of Solid Primitives. SIGGRAPH Skeleton CS184/284A Skinning on top Anatomy-Based Modeling of the Human Musculature. Scheepers et al. SIGGRAPH Ren Ng
25 Posing Use the rigging controls to put the character into a given pose. CS184/284A Ren Ng
26 Rigging Example Courtesy Matthew Lailler via Keenan Crane CS184/284A Ren Ng
27 Blend Shapes Instead of skeleton, interpolate directly between surfaces E.g., model a collection of facial expressions: Simplest scheme: take linear combination of vertex positions Spline used to control choice of weights over time CS184/284A Courtesy Félix Ferrand Ren Ng
28 Blend Shapes Courtesy Félix Ferrand
29 Motion Capture
30 Motion Capture Data-driven approach to creating animation sequences Record real-world performances (e.g. person executing an activity) Extract pose as a function of time from the data collected Motion capture room for ShaqFu CS184/284A Ren Ng
31 Motion Capture Pros and Cons Strengths Can capture large amounts of real data quickly Realism can be high Weaknesses Complex and costly set-ups Captured animation may not meed artistic needs, requiring alterations CS184/284A Ren Ng
32 Motion Capture Equipment Optical (More on following slides) CS184/284A Magnetic Sense magnetic fields to infer position / orientation. Tethered. Mechanical Measure joint angles directly. Restricts motion. Ren Ng
33 Optical Motion Capture Retroflective markers attached to subject IR illumination and cameras Markers on subject Positions by triangulation from multiple cameras 8+ cameras, 240 Hz, occlusions are difficult Slide credit: Steve Marschner
34 Optical Motion Capture Source: Ronda Roussey in Electronic Arts motion capture studio CS184/284A Ren Ng
35 Motion Data Subset of motion curves from captured walking motion. From Witkin and Popovic, 1995 CS184/284A Ren Ng
36 Challenges of Facial Animation Uncanny valley In robotics and graphics As artificial character appearance approaches Cartoon. Brave, Pixar human realism, our emotional response goes negative, until it achieves a sufficiently convincing level of realism in expression Semi-realistic. Polar Express, Warner Bros. CS184/284A Ren Ng
37 Challenges of Facial Motion Capture Final Fantasy Spirits Within CS184/284A Ren Ng
38 Facial Motion Capture Discovery, Avatar: Motion Capture Mirrors Emotions, CS184/284A Ren Ng
39 Things to Remember Principles of animation Computer character animation Rigging, posing, keyframes, interpolation Forward and inverse kinematics Motion capture: data driven animation CS184/284A Ren Ng
40 Acknowledgments Thanks to Keenan Crane, Mark Pauly, James O Brien, Steve Marschner and Tom Funkhouser for presentation resources. CS184/284A Ren Ng
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