# US 12,762,830 B2 · Method for effect generation and apparatus, computer device, and storage medium

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- Number: US 12,762,830 B2
- Document ID: US12762830B2
- Status: Granted
- Kind: B2
- Application number: 18/744,329
- Filed: 2024-06-14
- Published: 2024-12-19
- Granted: 2026-10-13
- Earliest claimed: 2023-06-14
- Assignees: Beijing Zitiao Network Technology Co., Ltd.
- Applicants: Beijing Zitiao Network Technology Co., Ltd.
- Inventors: Qi Dong
- Web page: https://patentgrep.com/patent/US12762830B2

## CPC

- G06T 11/60: Creating or editing images; Combining images with text
- G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51)
- G06T 13/00: Animation
- G06V 10/54: relating to texture
- G06V 10/75: Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06T 2200/24: involving graphical user interfaces [GUIs]
- G06T 2207/20092: Interactive image processing based on input by user
- G06T 2207/30196: Human being; Person
- G06T 2210/16: Cloth
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A method and apparatus for effect generation, a computer device, and a storage medium are provided. The effect generation method can include: obtaining, in response to a trigger operation of a current target object for an effect, at least one feature data corresponding to the current target object based on the trigger operation; determining a target feature data of the current target object based on the at least one feature data; determining a target effect corresponding to the target feature data based on a matching relationship between the target feature data and effects; and attaching the target effect on a preset part of the target object according to a preset manner.

## Claims

### Claim 1

A method for effect generation, comprising:
obtaining, in response to a trigger operation of a current target object for an effect, at least one feature data corresponding to the current target object based on the trigger operation;
determining a target feature data of the current target object based on the at least one feature data;
determining a target effect corresponding to the target feature data based on a matching relationship between the target feature data and effects; and
attaching the target effect on a preset part of the target object according to a preset manner,
wherein each of the effects is stored in an effect set, and a generation method of the effect set comprises:
displaying a creation page for a model patch, wherein the creation page comprises a plurality of model parameters;
obtaining a model parameter combination set in response to an adjustment operation on one or more of the model parameters;
obtaining an effect model set by performing programmable generation based on the model parameter combination set; and
generating the effect set of an augmented virtual reality based on the effect model set.

### Claim 2 (depends on 1)

The method according to claim 1, wherein determining the target feature data of the current target object based on the at least one feature data comprises:
obtaining a weight corresponding to each of the feature data; and
determining the target feature data by performing a weighting process on the feature data based on the weight.

### Claim 3 (depends on 2)

The method according to claim 2, wherein in response to the feature data including a plurality of feature data, determining the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects comprises:
obtaining each of preset weight parameters corresponding to the effects;
determining a target weight parameter that matches a target weight corresponding to the target feature data by comparing the target weight with each of the preset weight parameters; and
determining the target effect corresponding to the target weight parameter.

### Claim 4 (depends on 3)

The method according to claim 3, wherein determining the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects comprises:
determining a difference value between the target weight and each of the preset weight parameters in response to there being no target weight parameter matching the target weight; and
determining the preset weight parameter having the smallest difference value with the target weight as the target weight parameter; and determining the target effect corresponding to the target weight parameter.

### Claim 5 (depends on 2)

The method according to claim 2, wherein in response to the feature data including one feature data, determining the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects comprises:
obtaining trigger times for the effect in response to the target feature data being the same; and
generating different target effects based on different trigger times; or
determining the target effect corresponding to each of target feature data based on the different target feature data in response to the target feature data being different.

### Claim 6 (depends on 5)

The method according to claim 5, wherein the feature data is an illumination data, and determining the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects comprises:
collecting illumination data of a current spatial environment where the target object is located, wherein different illumination data correspond to different effects; and
obtaining the target effect corresponding to the current illumination data according to a matching relationship between the illumination data and the effects.

### Claim 7 (depends on 5)

The method according to claim 5, wherein the feature data is a texture feature, and determining the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects comprises:
collecting a clothing image of a clothing currently worn by the target object and analyzing texture features of the clothing image; and
obtaining the target effect corresponding to the current texture feature according to a matching relationship between the texture features and the effects.

### Claim 8 (depends on 5)

The method according to claim 5, wherein the feature data is an object attribute data, and determining the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects comprises:
collecting an image of the target object, and analyzing the object attribute data of the target object;
obtaining effects of a plurality of styles corresponding to the object attribute data; and
determining the target effect to be an effect of one style from the effects of the plurality of styles.

### Claim 9 (depends on 1)

The method according to claim 1, wherein after attaching the target effect on the preset part of the target object according to the preset manner, the method further comprises:
switching, in response to a switching operation instruction for the target effect, the target effect based on the switching operation instruction.

### Claim 10 (depends on 1)

The method according to claim 1, wherein after attaching the target effect on the preset part of the target object according to the preset manner, the method further comprises:
obtaining a movement posture of the target object; and
adjusting a display form of the target effect based on the movement posture.

### Claim 11 (depends on 1)

The method according to claim 1, further comprising:
adding corresponding animation data to each effect in the effect set, and controlling a dynamic presenting manner of the effect based on the animation data.

### Claim 12 (depends on 1)

The method according to claim 1, further comprising:
updating a material texture of the effect in response to a material adjustment instruction for the effect; and
storing the effect with the updated material texture in the effect set.

### Claim 13

A computer device, comprising:
a memory and a processor, wherein the memory and the processor are in mutual communication connection, the memory stores computer instructions, and the processor executes the computer instructions to:
obtain, in response to a trigger operation of a current target object for an effect, at least one feature data corresponding to the current target object based on the trigger operation;
determine a target feature data of the current target object based on the at least one feature data;
determine a target effect corresponding to the target feature data based on a matching relationship between the target feature data and effects; and
attach the target effect on a preset part of the target object according to a preset manner,
wherein each of the effects is stored in an effect set, and the processor executes the computer instructions to generate the effect set by:
displaying a creation page for a model patch, wherein the creation page comprises a plurality of model parameters;
obtaining a model parameter combination set in response to an adjustment operation on one or more of the model parameters;
obtaining an effect model set by performing programmable generation based on the model parameter combination set; and
generating the effect set of an augmented virtual reality based on the effect model set.

### Claim 14 (depends on 13)

The computer device according to claim 13, wherein the processor to determine the target feature data of the current target object based on the at least one feature data further executes the computer instructions to:
obtain a weight corresponding to each of the feature data; and
determine the target feature data by performing a weighting process on the feature data based on the weight.

### Claim 15 (depends on 14)

The computer device according to claim 14, wherein in response to the feature data including a plurality of feature data, the processor to determine the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects further executes the computer instructions to:
obtain each of preset weight parameters corresponding to the effects;
determine a target weight parameter that matches a target weight corresponding to the target feature data by comparing the target weight with each of the preset weight parameters; and
determine the target effect corresponding to the target weight parameter.

### Claim 16 (depends on 15)

The computer device according to claim 15, wherein the processor to determine the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects further executes the computer instructions to:
determine a difference value between the target weight and each of the preset weight parameters in response to there being no target weight parameter matching the target weight; and
determine the preset weight parameter having the smallest difference value with the target weight as the target weight parameter; and determining the target effect corresponding to the target weight parameter.

### Claim 17 (depends on 14)

The computer device according to claim 14, wherein in response to the feature data including one feature data, the processor to determine the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects further executes the computer instructions to:
obtain trigger times for the effect in response to the target feature data being the same; and
generate different target effects based on different trigger times; or
determine the target effect corresponding to each of target feature data based on the different target feature data in response to the target feature data being different.

### Claim 18 (depends on 17)

The computer device according to claim 17, wherein the feature data is an illumination data, and the processor to determine the target effect corresponding to the target feature data based on the matching relationship between the target feature data and effects further executes the computer instructions to:
collect illumination data of a current spatial environment where the target object is located, wherein different illumination data correspond to different effects; and
obtain the target effect corresponding to the current illumination data according to a matching relationship between the illumination data and the effects.

### Claim 19

A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are configured to enable a computer to:
obtain, in response to a trigger operation of a current target object for an effect, at least one feature data corresponding to the current target object based on the trigger operation;
determine a target feature data of the current target object based on the at least one feature data;
determine a target effect corresponding to the target feature data based on a matching relationship between the target feature data and effects; and
attach the target effect on a preset part of the target object according to a preset manner,
wherein each of the effects is stored in an effect set, and the computer instructions are configured to enable a computer to generate the effect set by:
displaying a creation page for a model patch, wherein the creation page comprises a plurality of model parameters;
obtaining a model parameter combination set in response to an adjustment operation on one or more of the model parameters;
obtaining an effect model set by performing programmable generation based on the model parameter combination set; and
generating the effect set of an augmented virtual reality based on the effect model set.