A method and system for optimizing motion capture prediction in 3D production

Abstract

The present invention is applicable to the field of 3D production technology, and provides a method and system for optimizing motion capture prediction in 3D production. The present invention optimizes the basic shooting route in real time by tracking and monitoring the positioning identifiers of the action targets; performs binocular shooting on the action targets; identifies the positions of multiple active capture identifiers and corresponding auxiliary active capture identifiers of the action targets; identifies the offset of the identifier spatial data; calculates the relative optimization deviation and the opposite optimization deviation according to the relative offset data and the opposite offset data, and performs motion capture prediction optimization processing on multiple active capture identifiers. It can optimize the shooting route for binocular shooting, identify the positions of multiple active capture identifiers and corresponding auxiliary active capture identifiers, then perform offset identification and deviation optimization in terms of relative and opposite aspects, and then perform motion capture prediction optimization processing, so as to realize the optimization of identifier offset, effectively improve the accuracy of motion capture, and produce a realistic action animation effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional production, and particularly relates to a method and system for optimizing motion capture prediction in three-dimensional production. Background Art

[0002] Three-dimensional production is a technology for creating, editing, and optimizing three-dimensional data models in a virtual three-dimensional space by using professional software and technologies. These models can be static objects or dynamic scenes or characters. For the three-dimensional production of dynamic characters, motion capture is sometimes required to produce the desired motion animation effects.

[0003] In the prior art, motion capture in three-dimensional production mainly uses optical motion capture technology, which is completed by tracking and monitoring the markers pasted on the motion target. However, due to the influence of factors such as possible clothing wrinkles and air disturbances on the outer surface of the motion target, it is easy to cause the markers to shift, affecting the accuracy of motion capture and making the motion animation effects in three-dimensional production inconsistent with the actual situation. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for optimizing motion capture prediction in three-dimensional production, aiming to solve the technical problems existing in the prior art mentioned in the background art.

[0005] The embodiments of the present invention are implemented as follows:

[0006] A method for optimizing motion capture prediction in three-dimensional production, the method specifically includes the following steps:

[0007] Obtain the basic movement route of the motion target, plan the basic shooting route, and track and monitor the positioning markers of the motion target, and optimize the basic shooting route in real time to generate an optimized shooting route;

[0008] According to the optimized shooting route, perform binocular shooting on the motion target to obtain binocular shooting data in real time;

[0009] According to the binocular shooting data, identify the positions of multiple active motion capture markers and corresponding auxiliary motion capture markers of the motion target to obtain marker space data;

[0010] Perform offset identification on the marker space data, select multiple relatively offset markers and multiple oppositely offset markers, and extract relative offset data and oppositely offset data;

[0011] According to the relative offset data and the oppositely offset data, calculate the relative optimization deviation and the oppositely optimization deviation, and perform motion capture prediction optimization processing on multiple active motion capture markers.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining the basic movement route of the action target, planning the basic shooting route, tracking and monitoring the positioning identifier of the action target, and optimizing the basic shooting route in real time to generate the optimized shooting route specifically include the following steps:

[0013] Obtain the basic movement route of the action target and the motion capture shooting parameters;

[0014] According to the basic movement route and the motion capture shooting parameters, perform motion capture shooting planning to generate a basic shooting route;

[0015] Determine the positioning identifier of the action target;

[0016] During the movement of the action target, track and monitor the positioning identifier of the action target to obtain positioning monitoring data;

[0017] According to the positioning monitoring data, optimize the basic shooting route in real time to generate an optimized shooting route.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing tracking binocular shooting on the action target according to the optimized shooting route and obtaining binocular shooting data in real time specifically include the following steps:

[0019] Perform shooting tracking control according to the optimized shooting route;

[0020] Determine the motion capture shooting frame rate;

[0021] Perform binocular shooting on the action target according to the motion capture shooting frame rate to obtain binocular shooting data in real time.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the steps of identifying the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers of the action target according to the binocular shooting data and obtaining identifier space data specifically include the following steps:

[0023] Obtain the identifier feature data of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers;

[0024] Based on the identifier feature data, perform feature matching on the binocular shooting data and record the feature matching information;

[0025] According to the feature matching information, identify the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers to obtain identifier space data.

[0026] As a further limitation of the technical solution of the embodiment of the present invention, the step of performing offset identification on the identification space data, selecting a plurality of relative offset identifiers and a plurality of opposite offset identifiers, and extracting relative offset data and opposite offset data specifically includes the following steps:

[0027] According to the identification space data, record the relative identification distances between a plurality of active capture identifiers and corresponding auxiliary active capture identifiers;

[0028] Based on a preset standard distance and a tolerance interval, perform offset comparison on the plurality of relative identification distances, and record the offset comparison results;

[0029] According to the offset comparison results, select a plurality of relative offset identifiers and a plurality of opposite offset identifiers from the plurality of active capture identifiers;

[0030] Extract relative offset data and opposite offset data from the plurality of relative identification distances.

[0031] As a further limitation of the technical solution of the embodiment of the present invention, the step of calculating relative optimization deviations and opposite optimization deviations according to the relative offset data and the opposite offset data, and performing dynamic capture prediction optimization processing on the plurality of active capture identifiers specifically includes the following steps:

[0032] Calculate relative optimization deviations and opposite optimization deviations according to the relative offset data and the opposite offset data;

[0033] Perform spatial deviation analysis on the identification space data to determine the relative deviation directions of the plurality of relative offset identifiers and the opposite deviation directions of the plurality of opposite offset identifiers;

[0034] According to the relative optimization deviations and the plurality of relative deviation directions, perform dynamic capture prediction optimization processing on the plurality of corresponding relative offset identifiers;

[0035] According to the opposite optimization deviations and the plurality of opposite deviation directions, perform dynamic capture prediction optimization processing on the plurality of corresponding opposite offset identifiers.

[0036] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the relative optimization deviation is:

[0037] ;

[0038] Wherein, is the relative optimization deviation, is the relative identification distance between the th relative offset identifier and the corresponding auxiliary active capture identifier, and there are relative offset identifiers in total, is the standard distance;

[0039] The calculation formula for the opposite optimization deviation is as follows:

[0040] ;

[0041] wherein, is the opposite optimization deviation, is the th relative identification distance between the opposite offset identifier and the corresponding auxiliary motion capture identifier, and there are opposite offset identifiers in total.

[0042] A motion capture prediction optimization system in 3D production, the system includes a shooting route optimization module, a tracking binocular shooting module, an identification position recognition module, an identification offset recognition module, and a motion capture prediction optimization module, wherein:

[0043] The shooting route optimization module is used to obtain the basic movement route of the action target, plan the basic shooting route, and track and monitor the positioning identifier of the action target to optimize the basic shooting route in real time and generate an optimized shooting route;

[0044] The tracking binocular shooting module is used to perform tracking binocular shooting on the action target according to the optimized shooting route and obtain binocular shooting data in real time;

[0045] The identification position recognition module is used to recognize the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers of the action target according to the binocular shooting data and obtain identification space data;

[0046] The identification offset recognition module is used to perform offset recognition on the identification space data, select multiple relative offset identifiers and multiple opposite offset identifiers, and extract relative offset data and opposite offset data;

[0047] The motion capture prediction optimization module is used to calculate the relative optimization deviation and the opposite optimization deviation according to the relative offset data and the opposite offset data, and perform motion capture prediction optimization processing on multiple active motion capture identifiers.

[0048] As a further limitation of the technical solution of the embodiment of the present invention, the shooting route optimization module specifically includes:

[0049] The basic acquisition unit is used to obtain the basic movement route of the action target and the motion capture shooting parameters;

[0050] The shooting planning unit is used to perform motion capture shooting planning according to the basic movement route and the motion capture shooting parameters and generate a basic shooting route;

[0051] The identification determination unit is used to determine the positioning identifier of the action target;

[0052] A tracking and monitoring unit, configured to track and monitor the positioning identifier of the action target during the movement of the action target, and obtain positioning monitoring data;

[0053] A route optimization unit, configured to optimize the basic shooting route in real time according to the positioning monitoring data, and generate an optimized shooting route.

[0054] As a further limitation of the technical solution of the embodiment of the present invention, the identification offset recognition module specifically includes:

[0055] A distance recording unit, configured to record the relative identification distances between multiple active capture identifiers and corresponding auxiliary capture identifiers according to the identification space data;

[0056] An offset comparison unit, configured to perform offset comparison on multiple relative identification distances based on a preset standard distance and a tolerance interval, and record the offset comparison result;

[0057] An identification selection unit, configured to select multiple relatively offset identifiers and multiple oppositely offset identifiers from multiple active capture identifiers according to the offset comparison result;

[0058] A data extraction unit, configured to extract relatively offset data and oppositely offset data from multiple relative identification distances.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] In the embodiment of the present invention, by tracking and monitoring the positioning identifier of the action target, the basic shooting route is optimized in real time; binocular shooting is performed on the action target; position recognition is performed on multiple active capture identifiers and corresponding auxiliary capture identifiers of the action target; offset recognition is performed on the identification space data; relative optimization deviation and opposite optimization deviation are calculated according to the relatively offset data and the oppositely offset data, and dynamic capture prediction optimization processing is performed on multiple active capture identifiers. It can optimize the shooting route for binocular shooting, perform position recognition on multiple active capture identifiers and corresponding auxiliary capture identifiers, then perform offset recognition and deviation optimization in terms of relative and opposite aspects, and then perform dynamic capture prediction optimization processing, so as to realize the optimization of identification offset, effectively improve the accuracy of motion capture, and produce a realistic motion animation effect. Description of the Drawings

[0061] Figure 1 Shows a flowchart of the dynamic capture prediction optimization method in 3D production provided by the embodiment of the present invention;

[0062] Figure 2 Shows a flowchart of real-time optimization of the basic shooting route in the method provided by the embodiment of the present invention;

[0063] Figure 3The flowchart of binocular shooting for tracking in the method provided by the embodiment of the present invention is shown;

[0064] Figure 4 The flowchart of obtaining identification space data in the method provided by the embodiment of the present invention is shown;

[0065] Figure 5 The flowchart of offset identification and data extraction in the method provided by the embodiment of the present invention is shown;

[0066] Figure 6 The flowchart of dynamic capture prediction optimization processing in the method provided by the embodiment of the present invention is shown;

[0067] Figure 7 The application architecture diagram of the dynamic capture prediction optimization system in 3D production provided by the embodiment of the present invention is shown;

[0068] Figure 8 The structural block diagram of the shooting route optimization module in the system provided by the embodiment of the present invention is shown;

[0069] Figure 9 The structural block diagram of the identification offset identification module in the system provided by the embodiment of the present invention is shown. Detailed implementation manners

[0070] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0071] It can be understood that in the prior art, for motion capture in 3D production, mainly optical motion capture technology is adopted, which is completed by tracking and monitoring the markers pasted on the motion target. However, due to the influence of factors such as possible clothing wrinkles and air disturbances on the outer surface of the motion target, it is easy to cause the markers to shift, affecting the accuracy of motion capture and making the motion animation effect in 3D production inconsistent with the actual situation.

[0072] To solve the above problems, a motion capture prediction optimization method and system in 3D production disclosed in an embodiment of the present invention obtain the basic movement route of a motion target, plan a basic shooting route, track and monitor the positioning identifier of the motion target, and optimize the basic shooting route in real time to generate an optimized shooting route; according to the optimized shooting route, perform tracking binocular shooting on the motion target to obtain binocular shooting data in real time; based on the binocular shooting data, identify the positions of multiple active capture identifiers and corresponding auxiliary active capture identifiers of the motion target to obtain identifier space data; perform offset identification on the identifier space data, select multiple relative offset identifiers and multiple opposite offset identifiers, and extract relative offset data and opposite offset data; according to the relative offset data and the opposite offset data, calculate the relative optimization deviation and the opposite optimization deviation, and perform motion capture prediction optimization processing on multiple active capture identifiers. It can optimize the shooting route for tracking binocular shooting, identify the positions of multiple active capture identifiers and corresponding auxiliary active capture identifiers, then perform offset identification and deviation optimization in terms of relative and opposite aspects, and then perform motion capture prediction optimization processing, so as to realize the optimization of identifier offset, effectively improve the accuracy of motion capture, and produce a realistic motion animation effect.

[0073] Specifically, Figure 1 FIG. shows a flowchart of the motion capture prediction optimization method in 3D production provided by an embodiment of the present invention.

[0074] In a preferred embodiment provided by the present invention, a motion capture prediction optimization method in 3D production specifically includes the following steps:

[0075] Step S101, obtain the basic movement route of the motion target, plan the basic shooting route, track and monitor the positioning identifier of the motion target, and optimize the basic shooting route in real time to generate an optimized shooting route.

[0076] In an embodiment of the present invention, by obtaining the basic movement route of the motion target and motion capture shooting parameters, and then according to the basic movement route and motion capture shooting parameters, perform motion capture shooting planning to ensure that during the shooting process, it can be synchronized with the basic movement route of the motion target and always be separated from the motion target by the separation distance set in the motion capture shooting parameters, and generate a basic shooting route according to the shooting height and shooting angle. By determining the positioning identifier of the motion target, continuously perform infrared monitoring on the motion target during the movement process of the motion target, and perform positioning analysis to obtain the positioning monitoring data of the motion target. By comparing the positioning monitoring data with the basic movement route for coincidence, judge whether the movement process of the motion target deviates from the basic movement route, and when the motion target deviates from the basic movement route, on the basis of the current position deviation, keep the subsequent movement route of the motion target unchanged, and make corresponding adjustments to the basic shooting route to generate an optimized shooting route.

[0077] It can be understood that the motion capture shooting parameters include parameters such as shooting distance, shooting height, shooting angle, and shooting frame rate.

[0078] It can be understood that the positioning identifier can be an infrared identifier that maintains a fixed temperature and has a significant difference from the temperature of the action target. During the movement of the action target, infrared monitoring can be performed on the positioning identifier to achieve the positioning and recognition of the action target.

[0079] Specifically, Figure 2 The flowchart of the real-time optimization of the basic shooting route in the method provided by the embodiment of the present invention is shown.

[0080] Among them, in another preferred embodiment provided by the present invention, the steps of obtaining the basic movement route of the action target, planning the basic shooting route, tracking and monitoring the positioning identifier of the action target, and real-time optimizing the basic shooting route to generate an optimized shooting route specifically include the following steps:

[0081] Step S1011: Obtain the basic movement route of the action target and the motion capture shooting parameters.

[0082] Step S1012: Perform motion capture shooting planning according to the basic movement route and the motion capture shooting parameters to generate a basic shooting route.

[0083] Step S1013: Determine the positioning identifier of the action target.

[0084] Step S1014: During the movement of the action target, track and monitor the positioning identifier of the action target to obtain positioning monitoring data.

[0085] Step S1015: According to the positioning monitoring data, real-time optimize the basic shooting route to generate an optimized shooting route.

[0086] Furthermore, the motion capture prediction optimization method in the 3D production further includes the following steps:

[0087] Step S102: Perform tracking binocular shooting on the action target according to the optimized shooting route to obtain binocular shooting data in real time.

[0088] In the embodiment of the present invention, during the movement of the action target, shooting tracking control is performed on the action target according to the optimized shooting route, so that during the shooting process, it can move according to the optimized shooting route and perform real-time adjustment during the real-time optimization of the route, and determine the motion capture shooting frame rate. According to the motion capture shooting frame rate, binocular shooting and transmission are performed on the action target to obtain binocular shooting data in real time.

[0089] Specifically, Figure 3The flowchart of binocular shooting for tracking in the method provided by the embodiments of the present invention is shown.

[0090] Wherein, in another preferred embodiment provided by the present invention, the step of performing binocular shooting on the action target according to the optimized shooting route and obtaining binocular shooting data in real time specifically includes the following steps:

[0091] Step S1021: Perform shooting tracking control according to the optimized shooting route.

[0092] Step S1022: Determine the motion capture shooting frame rate.

[0093] Step S1023: Perform binocular shooting on the action target according to the motion capture shooting frame rate, and obtain binocular shooting data in real time.

[0094] Furthermore, the motion capture prediction optimization method in the 3D production further includes the following steps:

[0095] Step S103: Identify the positions of multiple active motion capture markers and corresponding auxiliary motion capture markers of the action target according to the binocular shooting data, and obtain marker space data.

[0096] In the embodiments of the present invention, by obtaining the marker feature data of multiple active motion capture markers and corresponding auxiliary motion capture markers, then based on the marker feature data, perform feature matching of the markers on the binocular shooting data, record the matching results to generate feature matching information, and then according to the feature matching information, identify and record the spatial positions of multiple active motion capture markers and corresponding auxiliary motion capture markers to obtain marker space data.

[0097] It can be understood that multiple active motion capture markers are pasted on the outer surface of the action target, and corresponding auxiliary motion capture markers are pasted at a standard distance from each active motion capture marker, and the active motion capture marker and the corresponding auxiliary motion capture marker need to be on the same motion joint.

[0098] It can be understood that in the embodiments of the present invention, a point can be selected as the coordinate origin in the positioning markers to construct a space coordinate system, and then in the space coordinate system, identify the spatial positions of multiple active motion capture markers and corresponding auxiliary motion capture markers.

[0099] Specifically, Figure 4 The flowchart of obtaining marker space data in the method provided by the embodiments of the present invention is shown.

[0100] Wherein, in another preferred embodiment provided by the present invention, the step of identifying the positions of multiple active motion capture markers and corresponding auxiliary motion capture markers of the action target according to the binocular shooting data and obtaining marker space data specifically includes the following steps:

[0101] Step S1031: Obtain the identification feature data of multiple active capture identifiers and the corresponding auxiliary active capture identifiers.

[0102] Step S1032: Based on the identification feature data, perform feature matching on the binocular shooting data and record the feature matching information.

[0103] Step S1033: According to the feature matching information, perform position recognition on multiple active capture identifiers and the corresponding auxiliary active capture identifiers to obtain identification space data.

[0104] Furthermore, the motion capture prediction optimization method in the 3D production further includes the following steps:

[0105] Step S104: Perform offset recognition on the identification space data, select multiple relatively offset identifiers and multiple oppositely offset identifiers, and extract the relative offset data and the opposite offset data.

[0106] In the embodiment of the present invention, according to the spatial coordinate data of multiple active capture identifiers and the corresponding auxiliary active capture identifiers in the identification space data, calculate the relative identification distance between multiple active capture identifiers and the corresponding auxiliary active capture identifiers, and then use a preset standard distance and a tolerance interval to perform a comparative analysis of the offset of multiple relative identification distances. When the relative identification distance is less than the standard distance and the difference between the relative identification distance and the standard distance is greater than the tolerance interval, the corresponding active capture identifier is a relatively offset identifier; when the relative identification distance is greater than the standard distance and the difference between the relative identification distance and the standard distance is greater than the tolerance interval, the corresponding active capture identifier is an oppositely offset identifier, so as to be able to select multiple relatively offset identifiers and multiple oppositely offset identifiers from multiple active capture identifiers, and then extract the relative offset data of multiple relatively offset identifiers and the opposite offset data of multiple oppositely offset identifiers from multiple relative identification distances.

[0107] Specifically, Figure 5 shows the flowchart of offset recognition and data extraction in the method provided by the embodiment of the present invention.

[0108] Among them, in another preferred embodiment provided by the present invention, the performing offset recognition on the identification space data, selecting multiple relatively offset identifiers and multiple oppositely offset identifiers, and extracting the relative offset data and the opposite offset data specifically includes the following steps:

[0109] Step S1041: According to the identification space data, record the relative identification distance between multiple active capture identifiers and the corresponding auxiliary active capture identifiers.

[0110] Step S1042: Based on a preset standard distance and a tolerance interval, perform an offset comparison on multiple relative identification distances and record the offset comparison result.

[0111] Step S1043: Select a plurality of relative offset identifiers and a plurality of opposite offset identifiers from the plurality of active capture identifiers according to the offset comparison result.

[0112] Step S1044: Extract relative offset data and opposite offset data from the plurality of relative identifier distances.

[0113] Further, the motion capture prediction optimization method in the three-dimensional production further includes the following steps:

[0114] Step S105: Calculate a relative optimization deviation and an opposite optimization deviation according to the relative offset data and the opposite offset data, and perform motion capture prediction optimization processing on the plurality of active capture identifiers.

[0115] In an embodiment of the present invention, according to the relative offset data and the opposite offset data, calculate a relative optimization deviation and an opposite optimization deviation, and then perform a spatial deviation analysis on the identifier space data to determine the relative deviation direction in which a plurality of relative offset identifiers approach the corresponding auxiliary motion capture identifiers and the opposite deviation direction in which a plurality of opposite offset identifiers move away from the corresponding auxiliary motion capture identifiers in the spatial coordinate system. Furthermore, perform motion capture prediction optimization processing on the plurality of corresponding relative offset identifiers in a direction opposite to the relative optimization deviation and the plurality of relative deviation directions, and perform motion capture prediction optimization processing on the plurality of corresponding opposite offset identifiers in a direction opposite to the opposite optimization deviation and the plurality of opposite deviation directions. Specifically, the calculation formula for the relative optimization deviation is:

[0116] ;

[0117] where, is the relative optimization deviation, is the relative identifier distance between the th relative offset identifier and the corresponding auxiliary motion capture identifier, and there are relative offset identifiers in total, is the standard distance;

[0118] The calculation formula for the opposite optimization deviation is:

[0119] ;

[0120] where, is the opposite optimization deviation, is the relative identifier distance between the th opposite offset identifier and the corresponding auxiliary motion capture identifier, and there are opposite offset identifiers in total.

[0121] Specifically, Figure 6 shows a flowchart of performing motion capture prediction optimization processing in the method provided by an embodiment of the present invention.

[0122] Among them, in another preferred embodiment provided by the present invention, calculating the relative optimization deviation and the opposite optimization deviation according to the relative offset data and the opposite offset data, and performing dynamic capture prediction optimization processing on multiple active capture identifiers specifically includes the following steps:

[0123] Step S1051: Calculate the relative optimization deviation and the opposite optimization deviation according to the relative offset data and the opposite offset data.

[0124] Step S1052: Perform spatial deviation analysis on the identifier space data to determine the relative deviation directions of multiple relative offset identifiers and the opposite deviation directions of multiple opposite offset identifiers.

[0125] Step S1053: Perform dynamic capture prediction optimization processing on multiple corresponding relative offset identifiers according to the relative optimization deviation and multiple relative deviation directions.

[0126] Step S1054: Perform dynamic capture prediction optimization processing on multiple corresponding opposite offset identifiers according to the opposite optimization deviation and multiple opposite deviation directions.

[0127] Furthermore, Figure 7 shows an application architecture diagram of a dynamic capture prediction optimization system in 3D production provided by an embodiment of the present invention.

[0128] Specifically, in another preferred embodiment provided by the present invention, a dynamic capture prediction optimization system in 3D production includes:

[0129] A shooting route optimization module 101, configured to obtain a basic movement route of an action target, plan a basic shooting route, and perform tracking and monitoring on the positioning identifiers of the action target, and optimize the basic shooting route in real time to generate an optimized shooting route.

[0130] In an embodiment of the present invention, the shooting route optimization module 101 obtains the basic movement route of the action target and the motion capture shooting parameters, and then performs motion capture shooting planning according to the basic movement route and the motion capture shooting parameters to ensure that during the shooting process, it can keep in sync with the basic movement route of the action target and always be separated from the action target by the separation distance set in the motion capture shooting parameters, and generates a basic shooting route according to the shooting height and shooting angle. By determining the positioning identifier of the action target, during the movement of the action target, continuous infrared monitoring is performed on the action target, and positioning analysis is carried out to obtain the positioning monitoring data of the action target. By comparing the positioning monitoring data with the basic movement route for coincidence, it is judged whether the movement process of the action target deviates from the basic movement route. When the action target deviates from the basic movement route, based on the current position deviation, the subsequent movement route of the action target remains unchanged, and the basic shooting route is adjusted accordingly to generate an optimized shooting route.

[0131] Specifically, Figure 8 Fig. shows the structural block diagram of the shooting route optimization module 101 in the system provided by the embodiment of the present invention.

[0132] Among them, in another preferred embodiment provided by the present invention, the shooting route optimization module 101 specifically includes:

[0133] The basic acquisition unit 1011 is used to obtain the basic movement route of the action target and the motion capture shooting parameters.

[0134] The shooting planning unit 1012 is used to perform motion capture shooting planning according to the basic movement route and the motion capture shooting parameters to generate a basic shooting route.

[0135] The identifier determination unit 1013 is used to determine the positioning identifier of the action target.

[0136] The tracking and monitoring unit 1014 is used to track and monitor the positioning identifier of the action target during the movement of the action target to obtain the positioning monitoring data.

[0137] The route optimization unit 1015 is used to optimize the basic shooting route in real time according to the positioning monitoring data to generate an optimized shooting route.

[0138] Furthermore, the motion capture prediction optimization system in the 3D production further includes:

[0139] The tracking binocular shooting module 102 is used to perform tracking binocular shooting on the action target according to the optimized shooting route to obtain the binocular shooting data in real time.

[0140] In an embodiment of the present invention, during the movement of the action target, the tracking binocular shooting module 102 controls the shooting and tracking of the action target according to the optimized shooting route, so that during the shooting process, it can move according to the optimized shooting route, and perform real-time adjustment during the real-time optimization of the route, and determine the motion capture shooting frame rate. According to the motion capture shooting frame rate, binocular shooting and transmission of the action target are performed, and binocular shooting data is obtained in real time.

[0141] The identification position recognition module 103 is used to recognize the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers of the action target according to the binocular shooting data, and obtain identification space data.

[0142] In an embodiment of the present invention, the identification position recognition module 103 obtains the identification feature data of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers, and then based on the identification feature data, performs feature matching of the identifiers on the binocular shooting data, records the matching results, generates feature matching information, and then according to the feature matching information, recognizes and records the spatial positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers, and obtains identification space data.

[0143] The identification offset recognition module 104 is used to perform offset recognition on the identification space data, select multiple relative offset identifiers and multiple opposite offset identifiers, and extract relative offset data and opposite offset data.

[0144] In an embodiment of the present invention, the identification offset recognition module 104 calculates the relative identification distances between multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers according to the spatial coordinate data of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers in the identification space data, and then uses a preset standard distance and a tolerance interval to perform a comparative analysis of the offsets of multiple relative identification distances. When the relative identification distance is less than the standard distance and the difference between the relative identification distance and the standard distance is greater than the tolerance interval, the corresponding active motion capture identifier is a relative offset identifier; when the relative identification distance is greater than the standard distance and the difference between the relative identification distance and the standard distance is greater than the tolerance interval, the corresponding active motion capture identifier is an opposite offset identifier, so that multiple relative offset identifiers and multiple opposite offset identifiers can be selected from multiple active motion capture identifiers, and then multiple relative offset data of multiple relative offset identifiers and multiple opposite offset data of multiple opposite offset identifiers can be extracted from multiple relative identification distances.

[0145] Specifically, Figure 9 The structural block diagram of the identification offset recognition module 104 in the system provided by the embodiment of the present invention is shown.

[0146] Among them, in another preferred embodiment provided by the present invention, the identification offset recognition module 104 specifically includes:

[0147] A distance recording unit 1041 for recording the relative identification distances between multiple active capture identifiers and corresponding passive capture identifiers according to the identification space data.

[0148] An offset comparison unit 1042 for performing offset comparison on multiple said relative identification distances based on a preset standard distance and tolerance range, and recording the offset comparison result.

[0149] An identification selection unit 1043 for selecting multiple relatively offset identifiers and multiple oppositely offset identifiers from multiple said active capture identifiers according to the offset comparison result.

[0150] A data extraction unit 1044 for extracting relatively offset data and oppositely offset data from multiple said relative identification distances.

[0151] Furthermore, the motion capture prediction optimization system in the 3D production further includes:

[0152] A motion capture prediction optimization module 105 for calculating a relative optimization deviation and an opposite optimization deviation according to the relatively offset data and the oppositely offset data, and performing motion capture prediction optimization processing on multiple said active capture identifiers.

[0153] In an embodiment of the present invention, the motion capture prediction optimization module 105 calculates a relative optimization deviation and an opposite optimization deviation according to the relatively offset data and the oppositely offset data, then performs spatial deviation analysis on the identification space data to determine, in the spatial coordinate system, the relative deviation direction in which multiple relatively offset identifiers approach the corresponding passive capture identifiers, and the opposite deviation direction in which multiple oppositely offset identifiers move away from the corresponding passive capture identifiers. Furthermore, the motion capture prediction optimization processing is performed on multiple corresponding relatively offset identifiers in a direction opposite to the relative optimization deviation and multiple relative deviation directions, and the motion capture prediction optimization processing is performed on multiple corresponding oppositely offset identifiers in a direction opposite to the opposite optimization deviation and multiple opposite deviation directions. Specifically, the calculation formula for the relative optimization deviation is:

[0154] ;

[0155] Wherein, is the relative optimization deviation, is the relative identification distance between the th relatively offset identifier and the corresponding passive capture identifier, and there are relatively offset identifiers in total, is the standard distance;

[0156] The calculation formula for the opposite optimization deviation is:

[0157] ;

[0158] Wherein, For the opposite optimization deviation, For the relative identification distance between the th opposite offset identifier and the corresponding auxiliary motion capture identifier, and there are

[0159] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0160] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0161] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. A method for optimizing motion capture prediction in 3D production, characterized in that The method specifically includes the following steps: Obtain the basic movement route of the action target, plan the basic shooting route, and track and monitor the positioning identifier of the action target, and optimize the basic shooting route in real time to generate an optimized shooting route; According to the optimized shooting route, perform tracking binocular shooting on the action target to obtain binocular shooting data in real time; According to the binocular shooting data, perform position recognition on multiple active capture identifiers and corresponding auxiliary capture identifiers of the action target to obtain identifier space data; Perform offset recognition on the identifier space data, select multiple relative offset identifiers and multiple opposite offset identifiers, and extract relative offset data and opposite offset data; According to the relative offset data and the opposite offset data, calculate the relative optimization deviation and the opposite optimization deviation, and perform dynamic capture prediction optimization processing on multiple active capture identifiers; The performing offset recognition on the identifier space data, selecting multiple relative offset identifiers and multiple opposite offset identifiers, and extracting relative offset data and opposite offset data specifically includes the following steps: According to the identifier space data, record the relative identifier distances between multiple active capture identifiers and corresponding auxiliary capture identifiers; Based on a preset standard distance and a tolerance interval, perform offset comparison on multiple relative identifier distances, and record the offset comparison results; According to the offset comparison results, select multiple relative offset identifiers and multiple opposite offset identifiers from multiple active capture identifiers; Extract relative offset data and opposite offset data from multiple relative identifier distances; The calculating the relative optimization deviation and the opposite optimization deviation according to the relative offset data and the opposite offset data, and performing dynamic capture prediction optimization processing on multiple active capture identifiers specifically includes the following steps: Calculate the relative optimization deviation and the opposite optimization deviation according to the relative offset data and the opposite offset data; Perform spatial deviation analysis on the identifier space data to determine the relative deviation directions of multiple relative offset identifiers and the opposite deviation directions of multiple opposite offset identifiers; According to the relative optimization deviation and multiple relative deviation directions, perform dynamic capture prediction optimization processing on multiple corresponding relative offset identifiers; According to the opposite optimization deviation and multiple opposite deviation directions, perform dynamic capture prediction optimization processing on multiple corresponding opposite offset identifiers; The calculation formula for the relative optimization deviation is as follows: ; where is the relative optimization deviation, is the relative identification distance between the th relative offset identification and the corresponding auxiliary motion capture identification. There are relative offset identifications in total, ; where is the opposite optimization deviation, is the th opposite offset identification and the relative identification distance between the corresponding auxiliary motion capture identification. There are opposite offset identifications in total.

2. The motion capture prediction optimization method in 3D production according to claim 1, wherein The obtaining the basic movement route of the action target, planning the basic shooting route, and tracking and monitoring the positioning identifier of the action target, and optimizing the basic shooting route in real time to generate an optimized shooting route specifically includes the following steps: Obtain the basic movement route of the action target and dynamic capture shooting parameters; According to the basic movement route and the dynamic capture shooting parameters, perform dynamic capture shooting planning to generate a basic shooting route; Determine the positioning identifier of the action target; During the movement of the action target, track and monitor the positioning identifier of the action target to obtain positioning monitoring data; According to the positioning monitoring data, optimize the basic shooting route in real time to generate an optimized shooting route.

3. The motion capture prediction optimization method in 3D production according to claim 1, wherein Performing binocular shooting on the motion target according to the optimized shooting route and obtaining binocular shooting data in real time specifically includes the following steps: Performing shooting tracking control according to the optimized shooting route; Determining the motion capture shooting frame rate; Performing binocular shooting on the motion target according to the motion capture shooting frame rate and obtaining binocular shooting data in real time.

4. The motion capture prediction optimization method in 3D production according to claim 1, characterized in that Identifying the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers of the motion target according to the binocular shooting data and obtaining identifier space data specifically includes the following steps: Obtaining identifier feature data of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers; Performing feature matching on the binocular shooting data based on the identifier feature data and recording the feature matching information; Identifying the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers according to the feature matching information and obtaining identifier space data.

5. A dynamic capture prediction optimization system in 3D production, characterized in that, The system includes a shooting route optimization module, a tracking binocular shooting module, an identifier position identification module, an identifier offset identification module, and a motion capture prediction optimization module, where: The shooting route optimization module is used to obtain the basic movement route of the motion target, plan the basic shooting route, track and monitor the positioning identifier of the motion target, and optimize the basic shooting route in real time to generate an optimized shooting route; The tracking binocular shooting module is used to perform tracking binocular shooting on the motion target according to the optimized shooting route and obtain binocular shooting data in real time; The identifier position identification module is used to identify the positions of multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers of the motion target according to the binocular shooting data and obtain identifier space data; The identifier offset identification module is used to perform offset identification on the identifier space data, select multiple relative offset identifiers and multiple opposite offset identifiers, and extract relative offset data and opposite offset data; The motion capture prediction optimization module is used to calculate relative optimization deviation and opposite optimization deviation according to the relative offset data and the opposite offset data, and perform motion capture prediction optimization processing on multiple active motion capture identifiers; The identifier offset identification module specifically includes: A distance recording unit for recording the relative identifier distances between multiple active motion capture identifiers and corresponding auxiliary motion capture identifiers according to the identifier space data; An offset comparison unit for performing offset comparison on multiple relative identifier distances based on a preset standard distance and tolerance interval and recording the offset comparison result; An identifier selection unit for selecting multiple relative offset identifiers and multiple opposite offset identifiers from multiple active motion capture identifiers according to the offset comparison result; A data extraction unit for extracting relative offset data and opposite offset data from multiple relative identifier distances; Calculating relative optimization deviation and opposite optimization deviation according to the relative offset data and the opposite offset data and performing motion capture prediction optimization processing on multiple active motion capture identifiers specifically means: Calculating relative optimization deviation and opposite optimization deviation according to the relative offset data and the opposite offset data; Perform spatial deviation analysis on the identification space data to determine the relative deviation directions of multiple relative offset identifications and the opposite deviation directions of multiple opposite offset identifications; According to the relative optimization deviation and multiple relative deviation directions, perform motion capture prediction optimization processing on multiple corresponding relative offset identifications; According to the opposite optimization deviation and multiple opposite deviation directions, perform motion capture prediction optimization processing on multiple corresponding opposite offset identifications; The calculation formula for the relative optimization deviation is as follows: ; where is the relative optimization deviation, is the relative identification distance between the th relative offset identification and the corresponding auxiliary motion capture identification. There are relative offset identifications in total, and is the standard distance; The calculation formula for the opposite optimization deviation is as follows: ; where is the opposite optimization deviation, is the th opposite offset identification and the corresponding auxiliary motion capture identification. There are opposite offset identifications in total.

6. The motion capture prediction optimization system in 3D production according to claim 5, characterized in that, The shooting route optimization module specifically includes: A basic acquisition unit for acquiring the basic movement route of the action target and the motion capture shooting parameters; A shooting planning unit for performing motion capture shooting planning according to the basic movement route and the motion capture shooting parameters to generate a basic shooting route; An identification determination unit for determining the positioning identification of the action target; A tracking and monitoring unit for tracking and monitoring the positioning identification of the action target during the movement of the action target to obtain positioning monitoring data; A route optimization unit for optimizing the basic shooting route in real time according to the positioning monitoring data to generate an optimized shooting route.

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