A method for making large-scale group intelligence movement in movie and television animation production

Abstract

The application relates to the technical field of film and television animation production, in particular to a production method for large-scale group intelligent motion in film and television animation production; the method comprises the following steps: creating an arbitrary production surface and obtaining individual attributes of each individual in an initial frame; predicting an updated frame to obtain the running positions of the individuals; performing collision detection on the individuals according to the obtained running positions and individual attributes of the individuals; adjusting the individual attributes of the individuals according to the collision detection results between the individuals and according to an avoidance strategy; updating the frame to obtain the continuous running positions of the individuals, thereby forming continuous film and television animation of group motion; the application combines uniform motion and accelerated motion of the individuals, predicts the positions of the individuals in advance, performs collision detection, and then adopts different avoidance strategies, so that the individuals in the group are prevented from colliding in subsequent running, and the automatic generation of large-scale group intelligent motion animation is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of film and television animation production, in particular to a production method of large-scale group intelligent motion in film and television animation production. BACKGROUND

[0002] Film and television animation refers to animation film, and film and television three-dimensional animation involves film and television special effect creation, pre-filming, film and television 3D animation, special effect post-composition, film and television drama special effect animation, etc.

[0003] In the process of film and television animation production, hand-drawing is the main way to build group motion animation. Animators draw the posture and position changes of each individual in the group frame by frame by relying on their artistic creativity and understanding of motion laws. However, once faced with a large-scale group scene, the workload is greatly increased. With the application of computer technology in the field of animation production, graphic drawing and transformation tools begin to appear, which enables animation production to improve efficiency to a certain extent with the help of the computing power of computers, but this way lacks in-depth simulation of individual behavior.

[0004] In the patent document with the application number "CN201110447842.8", it is recorded that "the method comprises the following steps: creating a production surface; dividing the production surface into a plurality of small production surfaces; creating a particle emitter on the production surface to generate particles, each particle representing an individual in the group; the small production surface is the emission area of the particles, and the particles appear in the area according to the set parameters; setting the motion behavior of the particles, and the particles move on the production surface according to the set motion behavior; setting the particle intelligent avoidance mode, when there is no obstructing particle within a certain range, the particle moves along the set behavior, and when there is an obstructing particle within a certain range, the particle rotates in a certain direction and moves; and the particles are randomly replaced into a group motion cycle", which provides a production method of large-scale group motion. It forms a group motion cycle animation by emitting particles and making the particles move on the production surface according to the set motion behavior. It lacks consideration of different attributes of individuals and reasonable prediction and adjustment of individual motion, and cannot meet the use requirements.

[0005] Therefore, the present application provides a production method of large-scale group intelligent motion in film and television animation production to solve the above-mentioned technical problems. SUMMARY

[0006] The application aims to provide a production method for large-scale group intelligence motion in movie and television animation production, comprehensively considers various attributes of individuals, combines uniform motion and accelerated motion, predicts the positions of individuals in advance, performs collision detection based on the predicted running positions and attributes of individuals in real time, and adopts different avoidance strategies to avoid collisions of individuals in the group in subsequent running, thereby realizing automatic generation of large-scale group intelligence motion animation, greatly reducing manual workload and improving production efficiency.

[0007] To achieve the above object, the application provides the following technical scheme.

[0008] The application provides a production method for large-scale group intelligence motion in movie and television animation production, comprising the following steps.

[0009] An arbitrary production surface is created, and individual attributes of individuals in an initial frame are obtained;

[0010] The running positions of the individuals are predicted and updated;

[0011] Collision detection is performed on the individuals according to the obtained running positions and individual attributes of the individuals;

[0012] The individual attributes of the individuals are adjusted according to the collision detection results between the individuals according to avoidance strategies;

[0013] The continuous running positions of the individuals are obtained, and continuous movie and television animation of group motion is formed.

[0014] The application is further provided that the individual attributes include positions, masses, individual radii, velocities, accelerations and perception radii.

[0015] The application is further provided that the obtaining of the running positions of the individuals comprises:

[0016] Uniform motion position: r a (t+Δt)==r a (t)+v a Δt, wherein r a (t) is the current position of the individual a, v a is the velocity of the individual a, and Δt is the time interval of the updated frame.

[0017] Accelerated motion position: wherein a a is the acceleration of the individual a.

[0018] The application is further provided that the collision detection on the individuals is performed as follows:

[0019] An individual set within the perception radius of each individual in the predicted updated frame is obtained;

[0020] According to the determination rules, determine whether any two individuals in the individual set collide.

[0021] The present invention is further configured such that: the determination rule is: assuming the position of individual a is (x a y a z a The position of individual b is (x b y b z b The individual radius of individual a is r. a The individual radius of individual b is r b ,like If the collision occurs, then individual a and individual b are determined to have collided; otherwise, individual a and individual b have not collided.

[0022] The present invention is further configured such that the avoidance strategy includes the following:

[0023] When a collision is predicted, if the distance between individuals a and b is less than a preset threshold, the vehicle will turn to avoid the collision.

[0024] Otherwise, adjust your speed to avoid the obstacle;

[0025] If no collision is predicted, the individual attributes of the individual are not adjusted.

[0026] The present invention is further configured such that the steering and avoidance process is as follows:

[0027] Calculate the potential collision individual a(x) a y a z a ) relative to the current individual b(x) b y b z b The position vector of )

[0028] Calculate the normal vector of the turning plane of individual a. In the formula, This is the current direction vector of motion;

[0029] Individual a Rotation αθ max Angle, to obtain the direction vector after turning. Where α is the steering coefficient, θ max The maximum turning angle for individual a;

[0030] Update the motion direction vector of individual a to

[0031] The present invention is further configured such that the speed adjustment and avoidance process is as follows:

[0032] calculating the relative speed v of the individual a and the potential collision individual b ab and the current relative distance d ab ;

[0033] according to the relative speed v ab and the distance d ab , calculating the collision time

[0034] calculating the preliminary adjustment speed of the individual a wherein v a is the current movement speed of the individual a, β is an adjustment coefficient, m a is the mass of the individual a, m b is the mass of the individual b;

[0035] if |v a -v a ′|>a max t ab , wherein a max is the maximum acceleration of the individual a, then changing β until |v a -v a ′|≤a max t ab ;

[0036] adjusting the speed of the individual a to v a ′ that satisfies |v a -v max ′|≤a ab t a .

[0037] The further setting of the present application is that it further comprises rendering each individual according to the importance of each individual in the group.

[0038] Compared with the prior art, the present application has the beneficial effects that:

[0039] The application creates an arbitrary production surface, obtains individual attributes of each individual in an initial frame, predicts an update frame, obtains running positions of each individual, performs collision detection on each individual according to the obtained running positions and individual attributes of each individual, adjusts individual attributes of each individual according to collision detection results between individuals according to an avoidance strategy, finally updates the frame to obtain continuous running positions of each individual, and forms continuous video animation of group motion. The production method of large-scale group intelligent motion in video animation production provided by the application comprehensively considers various attributes of individuals, can reflect unique physical characteristics and behavior tendencies of each individual in a group, combines uniform motion and accelerated motion, predicts individual positions in advance, performs collision detection based on predicted running positions and attributes of individuals in real time, adjusts attributes of individuals according to different avoidance strategies, avoids collision of individuals in a group in subsequent running, forms continuous video animation of group motion by updating frames and integrating continuous running positions of individuals, realizes automatic generation of large-scale group intelligent motion animation, greatly reduces manual workload, improves production efficiency, and is especially suitable for animation production of long-time and large-scale group motion. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0041] Embodiment:

[0042] The embodiment provides a production method of large-scale group intelligent motion in video animation production, including the following steps.

[0043] S1, creating an arbitrary production surface, and obtaining individual attributes of each individual in an initial frame.

[0044] Further, the individual attributes include position, mass, individual radius, speed, acceleration and perception radius.

[0045] In the embodiment, it is to be noted that by creating the production surface and obtaining the initial attributes of the individuals, a basic environment can be built for subsequent group motion simulation, and the key parameters of the starting state of each individual are determined. By creating any suitable production surface, a spatial scene of different scenarios can be constructed, and the individual attributes of each individual in the initial frame are obtained, including position (determining the initial coordinates of the individual in space), mass (affecting the collision effect), individual radius (used for collision detection judgment), speed (initial motion speed), acceleration (motion speed change), and perception radius (determining the range of the individual that can perceive other individuals around).

[0046] S2, predicting the updated frame to obtain the running position of each individual.

[0047] Further, obtaining the running position of each individual includes:

[0048] Uniform motion position: r a (t+Δt)==r a (t)+v a Δt, where r a (t) is the current position of the individual a, v a is the speed of the individual a, and Δt is the time interval of the updated frame.

[0049] Accelerated motion position: where a a is the acceleration of the individual a.

[0050] In the embodiment, it is to be noted that by calculating the position of the individual in the subsequent updated frame, the motion trend of the individual can be known in advance, so as to prepare for collision detection and the like. As a real-time method, when the individual moves at a constant speed, it is assumed that an individual is currently located at coordinates (1, 2, 3) and the speed is (0.5, 0, 0), then in the next frame, its position will move to 1+0.5x1=1.5 in the x-axis direction, the y-axis and z-axis coordinates remain unchanged, and the updated position is (1.5, 2, 3); for example, in a racing game animation scene, the vehicle individual has different initial speed and acceleration, for a racing car with an initial speed of 10 m / s and an acceleration of 2 m / s 2 , the time interval is 1 s, and it is assumed that the initial position is (0, 0, 0), then the accelerated motion position is 11 in the x-axis direction. The embodiment considers both uniform and accelerated motion, and can simulate a more diverse individual motion mode, which is suitable for various group scenarios with different motion characteristics. The accurate position prediction provides accurate data basis for subsequent collision detection, and improves the reliability and stability of the entire group motion simulation.

[0051] S3, performing collision detection on each individual according to the obtained running position and individual attributes of each individual.

[0052] Further, the process of collision detection for each individual is as follows:

[0053] Obtaining the set of individuals within the perception radius of each individual in the predicted update frame;

[0054] According to the determination rule, determining whether any two individuals in the individual set collide.

[0055] Further, the determination rule is: assuming the position of individual a is (x a , y a , z a ), the position of individual b is (x b , y b , z b ), the individual radius of individual a is r a , and the individual radius of individual b is r b , if , then it is determined that individual a and individual b collide; otherwise, individual a and individual b do not collide.

[0056] In this embodiment, it should be noted that detecting whether the individuals in the group will collide is an important link to ensure the rationality and authenticity of group movement. By predicting potential collisions, subsequent avoidance strategies can be triggered to avoid unrealistic animation effects such as individual overlap or traversal, making group movement more natural and smooth. The present application performs collision detection based on individual running positions and attributes, taking into account the dynamic changes of individuals during movement, and can adapt to the changing positions and states of individuals in group movement. In addition, by obtaining the set of individuals within the perception radius and then performing collision detection, unnecessary collision calculation is effectively reduced, improving the efficiency of collision detection. Especially in large-scale group scenarios, the overall simulation speed and performance can be significantly improved. By discovering potential collisions in a timely manner, subsequent avoidance strategies can be triggered to avoid unrealistic animation effects such as individual overlap or traversal, making group movement more natural and smooth.

[0057] S4, according to the collision detection result between individuals, adjusting the individual attributes of the individuals according to the avoidance strategy.

[0058] Further, the avoidance strategy includes the following contents:

[0059] When a collision is predicted, if the distance between individual a and individual b is less than a predetermined threshold, then turn to avoid;

[0060] Otherwise, adjust the speed to avoid;

[0061] When no collision is predicted, the individual attributes of the individuals are not adjusted.

[0062] The process of turning to avoid is as follows:

[0063] Calculate the potential collision individual a(x) a y a z a ) relative to the current individual b(x) b y b z b The position vector of )

[0064] Calculate the normal vector of the turning plane of individual a. In the formula, This is the current direction vector of motion;

[0065] Individual a Rotation αθ max Angle, to obtain the direction vector after turning. Where α is the steering coefficient, θ max The maximum turning angle for individual a;

[0066] Update the motion direction vector of individual a to

[0067] The process of adjusting speed to avoid an obstacle is as follows:

[0068] Calculate the relative velocity v between individual a and potential colliding individual b. ab and the current relative distance d ab ;

[0069] Based on relative velocity v ab and distance d ab Calculate collision time

[0070] Calculate the pre-adjustment speed of individual a In the formula, v a Let β be the current velocity of individual a, and m be the adjustment coefficient. a For the mass of individual a, m b For the quality of individual b;

[0071] If |v a -v a ′|>a max t ab In the formula, a max For the maximum acceleration of individual a, then change β until |v a -v a ′|≤a max t ab ;

[0072] Adjust the velocity of individual a to satisfy |v a -v a ′|≤amax t ab v a ′.

[0073] In this embodiment, it should be noted that when the collision detection discovers potential collision risks, the actual occurrence of collision can be effectively avoided by adjusting the attributes of the individuals, ensuring that the group movement can continue, and making the group movement present intelligent and orderly characteristics. The present application provides two different avoidance strategies, which can be flexibly selected according to the specific situation of the collision, so that the group can make more reasonable and diversified response when facing different collision risks, increasing the intelligence and authenticity of the group movement simulation. In addition, the present application considers various attributes of individuals and relative motion relationship in the avoidance strategy, and determines the adjustment amount through accurate calculation, which can better simulate the avoidance behavior conforming to the physical law compared with the simple fixed rule avoidance method, and is more natural and realistic in visual effect. As an embodiment, assuming that the mass of individual a is 100 and the speed is (2, 0, 0), the mass of individual b is 80 and the speed is (1.5, 0, 0), the relative speed is (0.5, 0, 0), and the relative distance is assumed to be 1, then the collision time is 2, if the adjustment coefficient is 0.8, then the prepared adjustment speed is (1.6, 0, 0), if the maximum acceleration of individual a is 0.5 and the time interval is 1, then 0.4<0.5, which meets the requirements, and the speed can be updated to (1.6, 0, 0).

[0074] S5, updating the frame to obtain the continuous running positions of each individual, and forming a continuous video animation of the group movement.

[0075] In addition, it also includes rendering each individual according to the importance of each individual in the group.

[0076] In the embodiment, it is to be noted that the embodiment obtains the position changes of individuals in continuous time sequences (i.e., multiple update frames) by continuously repeating the previous steps of predicting positions, collision detection, and avoidance strategy adjustment, integrates the position information to generate continuous video animations of group motion, and gradually evolves the group motion from the initial static state to a dynamic and coherent animation sequence, thereby completing the production of large-scale group intelligent motion in the entire video animation production. The application realizes the intelligent continuous simulation of group motion by using the method of cyclic update frames, thereby greatly improving the production efficiency, especially for the animation production of a large number of individuals in a large-scale group scene for a long time. At the same time, due to the accurate simulation of individual motion and interaction in the previous steps, the continuous animation formed has high quality in the overall visual effect of group motion. In addition, while generating high-quality video animations, the rendering resources are reasonably allocated to achieve the best balance between visual presentation and production efficiency. As an implementation, in the animation production of an elephant migration, the initial attributes of numerous elephant individuals are first set in the initial frame, and their positions in the starting habitat are determined. Then, in each update frame, the position of each elephant is predicted, the collision between elephants is detected, and the avoidance adjustment is performed according to the above steps. With the update of each frame, the position of each elephant changes, and they form various formations for migration, etc. Finally, a complete continuous animation of elephant migration is formed, showing the entire migration process from the starting place to the destination.

[0077] The method for producing large-scale group intelligent motion in video animation production provided by the application creates an arbitrary production surface, obtains the individual attributes of each individual in the initial frame, predicts the update frame, obtains the running positions of each individual, performs collision detection on each individual according to the obtained running positions and attributes of each individual, adjusts the individual attributes of each individual according to the collision detection results between individuals, and finally updates the frame to obtain the continuous running positions of each individual, thereby forming continuous video animations of group motion. The application comprehensively considers various attributes of individuals, can reflect the unique physical characteristics and behavioral tendencies of each individual in the group, combines uniform and accelerated motion, predicts the positions of individuals in advance, performs real-time collision detection based on the predicted running positions and attributes of individuals, adjusts the attributes of individuals according to different avoidance strategies, avoids collisions between individuals in the group in subsequent running, forms continuous video animations of group motion by cyclically updating frames and integrating the continuous running positions of individuals, realizes the automatic generation of large-scale group intelligent motion animation, thereby greatly reducing the manual workload and improving the production efficiency, and is especially suitable for the animation production of long-time and large-scale group motion.

[0078] In the description of the specification, reference to "one embodiment", "an example", "a specific example" or the like means that a particular feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. The appearances of the phrases "in one embodiment", "an example", "a specific example" or the like in various places in the specification are not necessarily referring to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0079] The preferred embodiments of the application disclosed above are only to help explain the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments described. Obviously, many modifications and variations can be made in light of the contents of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical application of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for creating large-scale group intelligence motion in a motion picture animation, characterized in that, The method comprises the following steps: Creating any production surface and obtaining individual attributes of each individual in an initial frame; Predicting an updated frame and obtaining running positions of each individual; The obtaining of the running positions of each individual comprises: Constant velocity position: wherein is the current position of the individual a, is the velocity of the individual a, is the time interval of the update frame; accelerated motion position: , wherein is the acceleration of the individual a; Performing collision detection on each individual according to the obtained running positions and individual attributes of each individual; The collision detection on each individual is performed according to the following process: Obtaining an individual set of each individual within a sensing radius in the predicted updated frame; According to a judgment rule, determining whether collision occurs between any two individuals in the individual set; The determination rule is: assuming that the position of individual a is (x a, y a), the position of individual b is (x b, y b), the individual radius of individual a is r a, the individual radius of individual b is r b, if , , , , , , , , , then it is determined that individual a and individual b collide; otherwise, individual a and individual b do not collide. According to the collision detection result between individuals, adjusting the individual attributes of each individual according to an avoidance strategy; The avoidance strategy comprises the following contents: When collision is predicted to occur, if the distance between individual a and individual b is less than a preset threshold, then turning to avoid; Otherwise, adjusting the speed to avoid; When no collision is predicted to occur, then not adjusting the individual attributes of each individual; The process of the turning to avoid is as follows: Calculate the position vector of the potential collision individual a ( , , ) relative to the current individual b ( , , ) ; calculating a turning plane normal vector for the individual a , wherein is the current movement direction vector; The individual a is turned by an angle rotated an angle, resulting in a turned direction vector wherein is a turning coefficient, is the maximum turning angle of the individual a; updating the motion direction vector of the individual a to ; The process of the speed adjustment to avoid is as follows: calculating the relative velocity of the individual a and the potential collision individual b and the current relative distance ; According to the relative speed and distance , the collision time is calculated; calculating a preliminary adjustment speed for the individual a wherein is the current movement speed of the individual a, is an adjustment coefficient, is the mass of the individual a, is the mass of the individual b; If , where is the maximum acceleration of the individual a, then the change is made until is satisfied. adjusting the speed of the individual a to satisfy of ; Updating the frame to obtain continuous running positions of each individual, and forming a continuous video animation of group motion.

2. The method according to claim 1, wherein The individual attributes comprise position, mass, individual radius, speed, acceleration and sensing radius.

3. The method of claim 1, wherein, Further comprising rendering each individual according to the importance of each individual in the group.

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