Shape analysis on Lie groups and Homogeneous Manifolds with applications in computer animation

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1 Shape analysis on Lie groups and Homogeneous Manifolds with applications in computer animation Department of Mathematical Sciences, NTNU, Trondheim, Norway joint work with Markus Eslitzbichler and Alexander Schmeding Geometric Numerical Integration Oberwolfach, March 21st 2016

2 Outline Analysis of shapes in vector spaces. Character animation and skeletal animation. Analysis of shapes on Lie groups. Applications in: curve blending, projection from open curves to closed curves, and distances between curves. Examples in computer animation.

3 Analysis of shapes Shapes are unparametrized curves in a vector space or on a manifold.

4 Analysis of shapes Shapes are unparametrized curves in a vector space or on a manifold. Example: object recognition, objects can be represented by their contours, i.e. closed planar curves.

5 Analysis of shapes Shapes are unparametrized curves in a vector space or on a manifold. Example: object recognition, objects can be represented by their contours, i.e. closed planar curves. Image recognition: a tennis player a tennis racket and a ball.

6 Shapes Shapes are unparametrized curves in a vector space or on a manifold. Definition of shapes via an equivalence relation: let I R an interval, consider P = Imm(I, M) = {c C (I, M) ċ(t) 0}, P is called pre-shape space (an infinite dimensional manifold). Let c 0, c 1 P then c 0 c 1 ϕ c 0 = c 1 ϕ with ϕ Diff + (I) a orientation preserving diffeomorphism on I

7 Shapes Shapes are unparametrized curves in a vector space or on a manifold. Definition of shapes via an equivalence relation: let I R an interval, consider P = Imm(I, M) = {c C (I, M) ċ(t) 0}, P is called pre-shape space (an infinite dimensional manifold). Let c 0, c 1 P then c 0 c 1 ϕ c 0 = c 1 ϕ with ϕ Diff + (I) a orientation preserving diffeomorphism on I shape: [c 0 ]

8 Shapes Shapes are unparametrized curves in a vector space or on a manifold. Definition of shapes via an equivalence relation: let I R an interval, consider P = Imm(I, M) = {c C (I, M) ċ(t) 0}, P is called pre-shape space (an infinite dimensional manifold). Let c 0, c 1 P then c 0 c 1 ϕ c 0 = c 1 ϕ with ϕ Diff + (I) a orientation preserving diffeomorphism on I shape: Shape space: [c 0 ] S = Imm(I, M)/

9 Reparametrization invariance of shapes Let Diff + (I) = {ϕ C (I, I) ϕ (t) > 0} be the set of smooth, orientation preserving, invertible maps. Open curves. I = [0, 1] sin(t) 0.5 phi(t) 0.5 sin(phi(t)) cos(t) t cos(phi(t)) (cos(t), sin(t)), ϕ t 3 4 t2 + t, (cos(ϕ(t)), sin(ϕ(t))) 4

10 Reparametrization invariance of shapes Let Diff + (I) = {ϕ C (I, I) ϕ (t) > 0} be the set of smooth, orientation preserving, invertible maps. Open curves. I = [0, 1] sin(t) 0.5 phi(t) 0.5 sin(phi(t)) cos(t) t cos(phi(t)) (cos(t), sin(t)), ϕ t 3 4 t2 + t, (cos(ϕ(t)), sin(ϕ(t))) 4 Closed curves. Diff + (S 1 ) = {ϕ C (S 1, S 1 ) ϕ (t) > 0} 2π 0 0 2π

11 Distance function on S Applications often require a distance function to measure similarities between shapes. Let d P be a distance function on P (on parametrized curves) then Distance on S: d S ([c 0 ], [c 1 ]) = inf d P (c 0, c 1 ϕ). ϕ Diff + (I) But we need also to check that d S is well defined, i.e. is independent on the choice of representatives c 0 for [c 0 ] and c 1 for [c 1 ].

12 Distance function on S Applications often require a distance function to measure similarities between shapes. Let d P be a distance function on P (on parametrized curves) then Distance on S: d S ([c 0 ], [c 1 ]) = inf d P (c 0, c 1 ϕ). ϕ Diff + (I) But we need also to check that d S is well defined, i.e. is independent on the choice of representatives c 0 for [c 0 ] and c 1 for [c 1 ]. Computational methods for d S : gradient flows dynamic programming

13 Distance on P d P (c 0, c 1 ) it is usually obtained by defining a Riemannian metric on the infinite dimensional manifold P of parametrized curves: G c T c P T c P R and taking the length of the geodesic α between c 0 P and c 1 P wrt G c, i.e. d P (c 0, c 1 ) = length(α(c 0, c 1 )) α(c 0, c 1 ) is the shortest path between c 0 and c 1 wrt G c We will proceed differently and use the SRV transform R P C, with C a vector space where we can (and will) use the L 2 metric.

14 Virtual characters and skeletal animation Virtual character: a closed surface in R 3 (triangle mesh). Motions of characters via skeletal animation approach. Skeleton consisting of bones connected by joints.

15 Virtual characters and skeletal animation Virtual character: a closed surface in R 3 (triangle mesh). Motions of characters via skeletal animation approach. Skeleton consisting of bones connected by joints.

16 Virtual characters and skeletal animation Virtual character: a closed surface in R 3 (triangle mesh). Motions of characters via skeletal animation approach. Skeleton consisting of bones connected by joints. The coordinates of the vertices of the triangle mesh are specified in a coordinate system aligned with the bone. In the animation the movement of the vertices is determined by the bones.

17 Character animation and skeletal animation Skeleton: rooted tree made of bones and joints. Configuration space J = SE(3) n or J = SO(3) n (for human caracters). Character s pose specified by assigning values to the degrees of freedom. Animation: α [a, b] J, a curve on J, [a, b] interval of time. Motion capturing (recording curves on J ). right fingers right wrist right thumb Motion manipulation consider entire animations as shapes belonging to S. Figure: Skeleton: each node corresponds to a Q(t) SO(3) right hand right radius right humerus left toes left foot left tibia rright clavicle left femur left hipjoint head upperneck lowerneck thorax upperback lowerback root right hipjoint left clavicle right femur left humerus right tibia right foot right toes left radius left thumb left wrist left hand left fingers

18 Motion capturing Generating the data: α [a, b] J, a curve on J = SO(3) n motion capturing with and without markers

19 Shape analysis on Lie groups: spaces and metrics P = Imm(I, G) smooth functions with first derivative 0 S = Imm(I, G)/Diff + (I) shape space

20 Shape analysis on Lie groups: spaces and metrics P = Imm(I, G) smooth functions with first derivative 0 S = Imm(I, G)/Diff + (I) shape space Plan: we aim to obtain a distance function on S by d S ([c 0 ], [c 1 ]) = with d P a distance on P. inf d P (c 0, c 1 ϕ) (1) ϕ Diff + (I)

21 Shape analysis on Lie groups: spaces and metrics P = Imm(I, G) smooth functions with first derivative 0 S = Imm(I, G)/Diff + (I) shape space Plan: we aim to obtain a distance function on S by d S ([c 0 ], [c 1 ]) = with d P a distance on P. inf d P (c 0, c 1 ϕ) (1) ϕ Diff + (I) Definition. We say d P is a reparametrization invariant distance function on P iff d P (c 0, c 1 ) = d P (c 0 ϕ, c 1 ϕ) ϕ Diff + (I). (2) Proposition If d P is a reparametrization invariant distance function on P then d S ([c 0 ], [c 1 ]) as defined in (1) is independent of the choice of representatives of [c 0 ] and [c 1 ].

22 Tools to work with curves on G C (I, G) is an infinite dimensional Lie group, C (I, g) infinite dimensional Lie algebra

23 Tools to work with curves on G C (I, G) is an infinite dimensional Lie group, C (I, g) infinite dimensional Lie algebra The evolution operator of a regular (e.g. finite dimensional) Lie group G with Lie algebra g: Evol C (I, g) C (I, G) = {c C (I, G) c(0) = e} Evol(q)(t) = c(t), where c t = R c(t) (q(t)), c(0) = e, is a diffeomorphism. Evol C (I, g) C (I, G)

24 Tools to work with curves on G C (I, G) is an infinite dimensional Lie group, C (I, g) infinite dimensional Lie algebra The evolution operator of a regular (e.g. finite dimensional) Lie group G with Lie algebra g: Evol C (I, g) C (I, G) = {c C (I, G) c(0) = e} Evol(q)(t) = c(t), where c t = R c(t) (q(t)), c(0) = e, Evol C (I, g) C (I, G) is a diffeomorphism. The inverse of the evolution operator is the so called right logarithmic derivative δ r C (I, G) C (I, g), δ r g = R 1 g (ġ). H. Glöckner, arxiv: v3, March A. Kriegl and P. W. Michor, The convenient setting of global analysis.

25 SRVT for curves on G and distance on P SRVT: square root velocity transform. Let, be a right-invariant metric on G and the induced norm on tangent spaces, R {c Imm(I, G) c(0) = e} {v C (I, g) v(t) 0} δ r c q(t) = R(c)(t) = δr c = R 1 c(t) (ċ(t)) ċ(t) with inverse R 1 {v C (I, g) v(t) 0} {c Imm(I, G) c(0) = e}, R 1 (q)(t) = Evol(q q ) = c(t).

26 SRVT for curves on G and distance on P SRVT: square root velocity transform. Let, be a right-invariant metric on G and the induced norm on tangent spaces, R {c Imm(I, G) c(0) = e} {v C (I, g) v(t) 0} δ r c q(t) = R(c)(t) = δr c = R 1 c(t) (ċ(t)) ċ(t) with inverse R 1 {v C (I, g) v(t) 0} {c Imm(I, G) c(0) = e}, R 1 (q)(t) = Evol(q q ) = c(t). Reparametrization invariant distance on P 1 d P (c 0, c 1 ) = d L 2(R(c 0 ), R(c 1 )) = ( q 0 (t) q 1 (t) 2 2 dt) I Proposition d P is reparametrization invariant. Distance on S d S ([c 0 ], [c 1 ]) = inf ( q 0 (t) q 1 (t) 2 dt) ϕ Diff + (I) I 1 2

27 Connections to the elastic metric Elastic metric on P = Imm(I, G). Using the right invariant metric, on G we can define where G T P T P R G c (h, k) = I [a 2 D s h, v D s k, v ] ds (tangential) (normal) + I [b 2 ( D s h D s h, v v 2 D s k D s k, v v 2 )] ds, here the integration is with respect to arc-length, ds = c (t) dt, and v = c (t) c (t), D sh = h(t(s)) 1 h = s c (t) t. A family of Sobolev type metrics of order one.

28 Connections to the elastic metric Assume I = [0, 1], R Imm(I, G) C (I, g) Theorem The pullback of the L 2 inner product on C (I, g) to P = Imm(I, G), by the SRV transform R is the elastic metric G c (h, h) = I D s h D s h, v v D sh, v 2 ds, and D s h = ḣ ċ, v = ċ ċ. D s denotes differentiation with respect to arc length, v is the unit tangent vector of c and ds denotes integration with respect to arc length. For computational purposes, we can transform the curves with R and then use the L 2 metric.

29 Interpolation of curves (motion blending) Motion blending on G Geodesic paths α [0, 1] P between two parametrized curves on the Lie group G: c = α(0, t), d = α(1, t) we get (visually convincing) deformations from c to d α(x, t) = R 1 ((1 x)r(c) + x R(d))(t).

30 Original curves Reparametrized No reparam. Reparametrized

31 Obstacle example (motion blending) Elastic reparam. Feature reparam. SO(3) no reparam. t SO(3) reparam.

32 Closed curves Using SRVT R, we can identify curves c P = Imm(I, G) with R(c) C (I, g) Open curves C o = R(Imm(I, G))

33 Closed curves Using SRVT R, we can identify curves c P = Imm(I, G) with R(c) C (I, g) Open curves C o = R(Imm(I, G)) = {q C (I, g) q 0}

34 Closed curves Using SRVT R, we can identify curves c P = Imm(I, G) with R(c) C (I, g) Open curves Closed curves C o = R(Imm(I, G)) = {q C (I, g) q 0} C c = R({c Imm(I, G) c(0) = c(1) = e}) C c = r 1 (e), r = ev 1 Evol sc, r C o G ev 1 is the evaluation operator evaluating a curve at t = 1, while sc is the map sc(q) = q q, and we notice that r(q) = R 1 (q)(1). Theorem C c is a submanifold of finite codimension of C (I, g)

35 Curve closing Projection from C o onto C c by means of a constrained minimization problem 1 min q C c 2 q q 0, q 0 C o Instead of minimizing the distance from closed curves to q 0 we minimize the closure constraint:

36 Curve closing Projection from C o onto C c by means of a constrained minimization problem 1 min q C c 2 q q 0, q 0 C o Instead of minimizing the distance from closed curves to q 0 we minimize the closure constraint: Measuring closedness. Consider φ C o R φ(q) = 1 2 log(r(q)) 2, r = ev 1 Evol sc and φ(q) = 0 q C c

37 Projection on the space of closed curves Theorem T q φ(f ) = grad(φ)(q), f L2 = I grad(φ)(q), f dx, The gradient vector field wrt the L 2 inner product is where grad(φ)(q) = q α(q) + α(q), α(q) = Ad T c(q) 1 AdT r(q) (log(r(q))) C (I, g) c(q) = R 1 (q) C (I, G) r(q) = R 1 (q)(1) G. q q, (3) q Projection form C o to C c : Gradient flow u τ = grad(φ)(u), u(t, 0) = q(t).

38 Discrete curves: piecewise continuous in G c based on discrete points { c i = c(θ i )} n i=0 c(t) = n 1 k=0 χ [θk,θ k+1 )(t) exp ( t θ k log( c k+1 c k 1 θ k+1 θ )) c k, (4) k where χ is the characteristic function. SRV transform: q = R( c) piecewise constant function in g: q = { q i } n 1 i=0 q i = η i ηi, η i = log( c i+1 c 1 i ), θ i+1 θ i. The inverse SRV transform: c = R 1 ( q): c i+1 = exp ( q i q i (θ i+1 θ i )) c i

39 Numerical algorithms Curve reparametrization Applying a reparametrization ϕ Diff(I) to the discrete curve c gives c with { c i } n i=0 : c i = c j exp(s log( c j+1 c 1 j )), s = ϕ(θ i) θ j θ j+1 θ j, i = 0,..., n, where j is an index such that θ j ϕ(θ i ) < θ j+1. Note that c 0 = c 0 and c n = c n Curve interpolation [0, 1] P P P with interpolation parameter s. Curve closing in SO(3) (s, c 0, c 1 ) R 1 ((1 s)r( c 0 ) + sr( c 1 )), grad(φ)(q) = q c log(c(1))c T + c log(c(1))c T, q q q, (5) c = R 1 (q) ū k+1 = ū k α k grad(φ)(ū k ), where every ū k is a discrete curve as defined above, i.e., ū k = {ūi k}n i=0.

40 Jumping example (closure of curves)

41 Handspring example (closure of curves) Original Discontinuities t Closed t

42 Curve closure: experiments Original Closed Figure: Discontinuities in the handspring animation.

43 Curve closing Original Closed t t Figure: Application of closing algorithm to a cartwheel animation.

44 References E. Celledoni, M. Eslitzbichler ans A. Scmeding, Shape analysis on Lie groups with applications in computer animation, arxiv: M. Eslitzbichler, Modelling character motions on infinite-dimensional manifolds, The Visual Computer, M. Bauer, M. Eslitzbichler, M. Grasmair, Landmark-Guided Elastic Shape Analysis of Human Character Motions, to appear, M. Bauer, M. Bruveris, S. Marsland, and P.W. Michor, Constructing reparametrization invariant metrics on spaces of plane curves, arxiv, Carnegie-Mellon. Carnegie-Mellon Mocap Database, H. Glöckner. Regularity properties of infinite-dimensional Lie groups, and semiregularity. arxiv: [math], August A. Kriegl and P. W. Michor. Regular infinite dimensional Lie groups. Journal of Lie Theory, 7:61 99, A. Srivastava, E. Klassen, S.H. Joshi, and I.H. Jermyn. Shape Analysis of Elastic Curves in Euclidean Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(7): , July Thank you for listening.