CN105184096A - Virtual human movement pose calculating method - Google Patents
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- CN105184096A CN105184096A CN201510623136.2A CN201510623136A CN105184096A CN 105184096 A CN105184096 A CN 105184096A CN 201510623136 A CN201510623136 A CN 201510623136A CN 105184096 A CN105184096 A CN 105184096A
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Abstract
The invention discloses a virtual human movement pose calculating method. The method comprises the steps that a two-layer cluster model is built for human body terminal position information and joint rotating angle information corresponding to the human body terminal position information, a k-means clustering rule is adopted in the first-layer space for carrying out position constraint relation clustering, and a maximum and minimum distance clustering rule is adopted in the second-layer space for carrying out clustering on relevant joint angle information; a mapping relation between test sample tail end position information and the joint angle in the training sample clustering space is built, joint angle information meeting constraint conditions is searched for through an improved constraint equation, reverse solving is achieved, and therefore the movement pose of the whole virtual human is restored. The virtual human movement pose calculating method has the advantages of being high in solving precision and solving speed, and can achieve more lifelike human movement.
Description
Technical field
The present invention relates to a kind of computing method of motion of virtual human pose, for the motion control of visual human, belong to motion of virtual human control technology field.
Background technology
Based on the editor of motion of virtual human capture-data with to reuse be carry out analysis and treament to existing motion of virtual human capture-data, thus generate new motion of virtual human data.Editor and synthesis are that the conventional exercise data that utilizes carries out the method for editing Yu processing.But the edit methods of general motion data can only produce independent, specific motion segment, the exercise data that can not synthesize continuous print in real time, meet the demands.
The machine learning method such as subspace analysis, statistical learning, manifold learning is widely used in analysis and the synthesis of exercise data in recent years.Eng-JonOng etc. propose the reverse movement method based on double-deck Clustering Model, the method solves based on known sample information, double-deck Clustering Model is used for learn existing motion capture data, thus the kinematical constraint model setting up whole role's skeleton is used to guide reverse movement generation.But what the method adopted is BIP formatted data, and have ignored human body head information, the partial parameters in double-deck Clustering Model is not solved by sample information, but specifies based on experience value, and versatility is restricted.
Summary of the invention
Technical matters to be solved by this invention is: the computing method providing a kind of motion of virtual human pose, adopt BVH format motion capture-data, comprise detailed header information, minimax distance clustering method is adopted to the training of sample, greatly reduces computing time.
The present invention is for solving the problems of the technologies described above by the following technical solutions:
Computing method for motion of virtual human pose, comprise the steps:
Step 1, obtains the exercise data of visual human in training sample, comprises the position vector of hip joint and the angle vector in angle vector and other joints, and carries out pre-service to it, obtain the position vector in visual human's end joint;
Step 2, position vector and the known angle vector in the end joint obtained according to step 1 set up double-deck Clustering Model, the position vector in end joint is carried out k-means cluster as ground floor to its position constraint relation and obtains position Cluster space, the angle in end joint vector is carried out minimax distance cluster as the second layer to its angle restriction relation and obtains angle Cluster space;
Step 3, the position vector in end joint in known visual human's test sample book, calculate the Euclidean distance of each cluster centre in the position Cluster space that above-mentioned position vector and step 2 obtain, the position Cluster space at cluster centre place minimum for Euclidean distance is extracted;
Step 4, finds in the angle Cluster space that the angle vector that in position Cluster space step 3 extracted, each position vector is corresponding obtains in step 2, and is all extracted by the angle Cluster space at above-mentioned angle vector place;
Step 5, the Euclidean distance of each cluster centre of angle Cluster space that the position vector of calculating test sample book and step 4 are extracted, extracts angle Cluster space minimum for Euclidean distance, and calculates the position vector of the angle vector correspondence in this angle Cluster space; The position vector of the position vector calculated and test sample book is asked Euclidean distance, and position vector minimum for Euclidean distance is proposed from the position vector calculated, obtain the articulate angle vector of test sample book institute;
Step 6, utilizes dynamic time warping technology to do temporal constraint to the articulate angle vector that step 5 obtains, obtains the motion pose of visual human's test sample book.
Preferably, end joint described in step 1 comprises hip, head, left wrist, right wrist, left ankle and right ankle.
Preferably, the form of exercise data described in step 1 is BVH form.
Preferably, described in step 1, pretreated process is: from hip joint, the rotation matrix that the position vector equaling current joint father node according to the position vector of current joint is first multiplied by current joint is multiplied by the computation rule of the current joint excursion matrix of its father node relatively again, then calculates the position vector in other each joints successively.
Preferably, described in step 2, k-means cluster middle distance measurement criterion is Euclid distance criterion.
The present invention adopts above technical scheme compared with prior art, has following technique effect:
The computing method of motion of virtual human pose of the present invention, with BVH format motion capture-data for research object.Compared to existing technology, the present invention for research object, had both comprised the exercise data of human body basic structure with six human body end joints, also can ensure the requirement of algorithm to sample, adopts minimax distance clustering method, greatly reduce computing time to the training of sample.With the action sequence outside sample for research object, utilization dynamic time warping (DTW) technology obtains the similarity in motion sequence to be measured and database between alternative motion segments, and the joint angles information aggregate obtaining qualified human body obtains more simple, reliable reverse movement and solves.
Accompanying drawing explanation
Fig. 1 is the flowage structure figure of the computing method of motion of virtual human pose of the present invention.
Fig. 2 is the trie tree structural drawing of BVH format motion data of the present invention.
Fig. 3 is the structured flowchart of the computing system of motion of virtual human pose of the present invention.
Embodiment
Be described below in detail embodiments of the present invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.
As shown in Figure 1 and Figure 2, the concrete steps of the computing method of motion of virtual human pose are as follows:
Step 1, BVH file employing hierarchical structure has carried out the description to actor model, and as shown in Figure 2, the root node of tree is hip joint, and leaf node is head, left wrist, right wrist, left ankle, right ankle.Because human skeleton model has level, the movement of whole tool skeleton represents with the translation of its root node, and other node only rotates.So the direction of human motion is determined by the translation and rotating of hip joint, other each child node be rotated in carry out under its father node local coordinate system that is true origin, for shoulder joint, its rotation can drive its lower one deck bone to rotate.Large arm pose is determined by shoulder joint pose, and its pose under global coordinate system to be multiplied with the excursion matrix of large arm under its local coordinate system by the pose of shoulder joint and the rotation matrix of shoulder joint to obtain.Shoulder joint pose is again determined by neck pose, and by that analogy, according to tree hierachy recurrence layer by layer, the calculating of the pose of each articulation point to be multiplied with the excursion matrix in this joint according to the pose of its father node current and rotation matrix to obtain.That is:
wherein
refer to that the i-th joint is at global coordinate system upper/lower positions vector; T
ireferring to excursion matrix, is be made up of the side-play amount of the i-th articulation point and its father node; R
irefer to the rotation matrix of the i-th articulation point, by around Z, X, 3 of Y-axis rotate Eulerian angle and calculate; I is from 0.
Step 2, motion editing module are core places of the present invention, and in order to accurately divide human body movement data, the present invention is by vectorial six human body terminal positions
as ground floor, cluster is carried out to its position constraint relation; On this basis, depending on the joint angles vector corresponding to end
for the second layer carries out cluster to its angle restriction relation.Specifically, this step specifically comprises:
Step 2.1, in ground floor Cluster space, k-means cluster is carried out to human body terminal position vector, human body final word can be divided into a series of positions Cluster space; Suppose the raw c of ground floor common property
mindividual cluster, i-th position constraint cluster c
p,i(i=1 ..., m) represent.Each cluster comprises human body six terminal position information.Definition
for cluster c
p,icluster centre, available
represent.Concrete steps are:
(1) Cluster space is divided into desired clusters number k and maximum iteration time n;
(2) get the somatic data of k frame at random as cluster centre, calculate the distance of remainder data frame to this cluster centre, in this step, distance criterion adopts Euclidean distance to calculate;
(3) find out minor increment, and this Frame is included into nearest cluster centre;
(4) to each Frame in each class, calculate its distance to other class centers, if it is less than to the distance at a certain class center the distance that it arrives self class center, needs again to plan such, it is belonged to the class that distance center is near;
(5) operation is cycled to repeat.
Step 2.2, in second layer Cluster space, adopt minimax distance cluster rule to realize, the method not only avoids the setting of parameter, and cluster centre is known, thus reduces computer running time, improves solution efficiency.Such as i-th (i=1 ..., m) under individual position constraint cluster, find out c
p,iin joint angles information vector corresponding to all human body terminal positions vector, cluster is carried out to it.Make i-th position constraint relation cluster c in ground floor Cluster space
p,ia corresponding jth joint angles relation cluster c
q, i, j(i=1 ..., m, j=1 ..., c
i) represent, q represents that this cluster belongs to second layer Cluster space.The angle information in what each cluster comprised is each joint of human body, definition
for cluster c
q, i, jcluster centre, then have
specific algorithm step is as follows:
(1) S={B is supposed
1', B
2', B
3' ..., B
n' be i-th position cluster C
p,i(i=1 ..., the joint angles arrangement set that human body terminal position vector m) comprised is corresponding, wherein subscript p represents and belongs to ground floor Cluster space, and initial joint angles sequence number is idx=1;
(2) for list entries B
idx' as first cluster centre C
1, according to Euclidean distance function dis=||B
k'-C
1||, search from C
1motion frame farthest
its ultimate range is max_dis, now
be second class, determine second cluster centre C
2, idx++;
(3) all the other each motion frames are calculated one by one to two cluster centre C
1and C
2between distance,
there are two minimum distance values and extract, is d respectively
1, d
2.Now with C
1distance be d
1joint angles information frame be B
i' with C
2distance be d
2joint angles information frame be B
j', compare d
1, d
2, get maximal value max{d
1, d
2.With d
2> d
1for example, if d
2≤ θ max_dis, θ are threshold value, and the present invention is according to the size of sample specifying information definite threshold, and 0 < θ < 1, then by this frame B
j' be included into from its nearest class; If d
2> θ max_dis, then with this frame B
jnew cluster centre C is set up centered by ' joint angles information
3, idx++;
(4) on the basis of previous step, continue to find next cluster centre according to the algorithm of minimax distance, if max{d
1, d
2, d
3> θ max_dis, then determine next cluster centre C
4, otherwise be included into that nearest class.Cycling, till no longer including next cluster centre.
Step 3, motion-control module reverse movement solve the positional information referred at known each end of human body, solve the angle information in other joint associated therewith.The present invention is by outer for sample terminal position information
combine with double-deck Clustering Model, search out by the equation of constraint improved the joint angles information meeting constraint condition, realize reverse movement and solve.
The Converse solved concrete steps of single frame are as follows:
Steps A: ground floor position constraint Cluster space C
p,i(i=1 ..., m), total m class, as long as determine
belong to which classification, represent with Euclidean distance discriminant function, search out the constrained clustering of satisfied following condition:
?
substitute in m discriminant function, make discriminant score
that minimum class is exactly
generic.I.e. each cluster C
p,iin cluster centre
with
relatively, get the position relationship cluster that Euclidean distance is minimum, just can obtain immediate position constraint relation cluster.If
belong to a certain position relationship cluster, the joint angles information cluster that so this Cluster space is corresponding is likely similar to the data on human skeleton to be measured;
Step B: the position constraint relation cluster C solving out in the first step to each
p,i, all can there is the joint angles relation Cluster space C associated with it
q, i, j(i=1 ..., m, j=1 ..., c
i).Suppose C
q, i, jin have C
iindividual joint angles cluster, therefore, will solve the joint angles relation cluster meeting certain condition in this joint angles relation cluster set.Specific operation process is as follows:
Namely step B1: because joint angles relation cluster centre is known in double-deck Clustering Model space, can try to achieve terminal position information corresponding to cluster centre according to formula (1)
Step B2: calculate
with
distance
Relatively
to the distance between all kinds of, if meet
Then
with C
q, i, kmore close.
Step C: for the kth joint angles relation cluster having solved out, the joint angles information vector number that it comprises is V.V joint angles information vector is substituted into formula (1), obtains
principle is similar to step B2, obtain with
corresponding joint angles information.
The timing of the Converse solved consideration motion frame of motion sequence, concrete steps are as follows:
Steps A: carry out Converse solved to frame terminal position information first in motion sequence, search for qualified motion frame;
Step B: carry out Converse solved to frame terminal position information last in motion sequence simultaneously, search for qualified motion frame;
Step C: judge the joint angles information that intermediate frame terminal position information is corresponding:
Step C1: determine simultaneously there is first frame and the set of last frame motion sequence;
Step C2: make Y=(F
s..., F
e), F
s, F
eframe and last frame respectively, meet two condition (1) s < e; (2) at F
s, F
ebetween do not repeat to there is first frame and last frame information, then Y=(F
s..., F
e) be required motion sequence set;
Step D: to Y=(F
s..., F
e) to solve according to formula (1) and draw terminal position information aggregate Y
*=(p
s *..., p
e*);
Step e: application dynamic time warping (DTW) technology, calculates motion Q and Y to be measured
*between similarity: assuming that Q={p
1, p
2... p
l, Y
*=(p
1 *... ..p
m*), p is worked as
sand p
t *be respectively Q and Y
*s frame and the terminal position information of y frame, l and m is Q and Y
*motion frame number, calculate the similarity between Q and Y, similarity Sim (Q, Y finally between this Q and Y
*) be all diff (Q, Y
*) and inverse:
after calculating the similarity between final word motion sequence to be measured and candidate's final word motion sequence, get and make Sim (Q, Y
*) maximum Y
*, the joint angles information aggregate Y=(F of its correspondence
s..., F
e) be final required motion sequence.Namely the Converse solved of one section of terminal position information aggregate is realized.
Motion sequence to be measured is made integrally based on DTW searching method, not only make us utilize in double-deck Cluster space to allow high dimensional data be mapped to lower dimensional space and directly reduce time complexity to neighbor search, and avoid searching in the sequence of longer length motion set, these advantages shorten computing time effectively, improve counting yield.
As shown in Figure 3, be the computing system of motion of virtual human pose of the present invention, comprise data preprocessing module, motion editing module, motion-control module.Data preprocessing module, carries out pre-service to BVH form capture-data, obtains the posture information of each joint data of human body; Motion editing module, carries out clustering processing to pretreated human synovial data according to double-deck Clustering Model structure, extracts key frame data; Motion-control module, to the outer human body end joint position information of input amendment, for combine with double-deck Clustering Model Converse solved go out other joint angles information of human body, realize the motion control of human body.
Above embodiment is only and technological thought of the present invention is described, can not limit protection scope of the present invention with this, and every technological thought proposed according to the present invention, any change that technical scheme basis is done, all falls within scope.
Claims (5)
1. computing method for motion of virtual human pose, is characterized in that: comprise the steps:
Step 1, obtains the exercise data of visual human in training sample, comprises the position vector of hip joint and the angle vector in angle vector and other joints, and carries out pre-service to it, obtain the position vector in visual human's end joint;
Step 2, position vector and the known angle vector in the end joint obtained according to step 1 set up double-deck Clustering Model, the position vector in end joint is carried out k-means cluster as ground floor to its position constraint relation and obtains position Cluster space, the angle in end joint vector is carried out minimax distance cluster as the second layer to its angle restriction relation and obtains angle Cluster space;
Step 3, the position vector in end joint in known visual human's test sample book, calculate the Euclidean distance of each cluster centre in the position Cluster space that above-mentioned position vector and step 2 obtain, the position Cluster space at cluster centre place minimum for Euclidean distance is extracted;
Step 4, finds in the angle Cluster space that the angle vector that in position Cluster space step 3 extracted, each position vector is corresponding obtains in step 2, and is all extracted by the angle Cluster space at above-mentioned angle vector place;
Step 5, the Euclidean distance of each cluster centre of angle Cluster space that the position vector of calculating test sample book and step 4 are extracted, extracts angle Cluster space minimum for Euclidean distance, and calculates the position vector of the angle vector correspondence in this angle Cluster space; The position vector of the position vector calculated and test sample book is asked Euclidean distance, and position vector minimum for Euclidean distance is proposed from the position vector calculated, obtain the articulate angle vector of test sample book institute;
Step 6, utilizes dynamic time warping technology to do temporal constraint to the articulate angle vector that step 5 obtains, obtains the motion pose of visual human's test sample book.
2. the computing method of motion of virtual human pose as claimed in claim 1, is characterized in that: end joint described in step 1 comprises hip, head, left wrist, right wrist, left ankle and right ankle.
3. the computing method of motion of virtual human pose as claimed in claim 1, is characterized in that: the form of exercise data described in step 1 is BVH form.
4. the computing method of motion of virtual human pose as claimed in claim 1, it is characterized in that: described in step 1, pretreated process is: from hip joint, the rotation matrix that the position vector equaling current joint father node according to the position vector of current joint is first multiplied by current joint is multiplied by the computation rule of the current joint excursion matrix of its father node relatively again, then calculates the position vector in other each joints successively.
5. the computing method of motion of virtual human pose as claimed in claim 1, is characterized in that: described in step 2, k-means cluster middle distance measurement criterion is Euclid distance criterion.
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