A dynamic haptic rendering method based on 2D images and finger movement trajectories

Abstract

This invention provides a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories, comprising: acquiring an original two-dimensional image; converting the target object in the original two-dimensional image into a three-dimensional structural point cloud; mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data set and trajectory data set; performing interpolation smoothing on the matching point set to construct a continuous geometric contour curve; calculating the first derivative of each segment in the geometric contour curve and using the first derivative as the stimulus value of virtual haptics; converting the stimulus value into a driving voltage signal, and controlling the operation of an electrostatic adhesive haptic feedback device through the driving voltage signal; this invention's dynamic haptic rendering generation method based on two-dimensional images and finger movement trajectories breaks through the limitations of traditional haptic feedback relying on static templates and dedicated sampling data, and realizes automatic modeling and real-time response from two-dimensional visual input to physical haptic output.

Description

Technical Field

[0001] This invention relates to the field of circuit technology, and in particular to a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories, a dynamic haptic rendering device based on two-dimensional images and finger movement trajectories, a computer device, and a storage medium. Background Technology

[0002] Haptic rendering is a technology that uses computer-generated force feedback information to enable users to perceive touch when interacting with a virtual environment. It effectively enhances the user's immersive experience and the realism of interaction, and is widely used in virtual reality, remote operation, and human-computer interaction scenarios. In recent years, the rapid development of haptic rendering technologies such as thermal haptic feedback, ultrasonic haptic feedback, and electrostatic adsorption technology has allowed users to perceive the texture and shape of objects on touch surfaces with high fidelity. However, most current haptic rendering technologies and commercial haptic feedback products are based on pre-designed static haptic patterns, allowing users to interact only with predefined texture patterns. These methods lack the ability to dynamically respond to the user's finger movement trajectory. When users interact with objects with complex shapes, changes in the direction of finger sliding often produce different haptic experiences due to variations in the object's surface contours and shape features. Existing predefined feedback mechanisms cannot adapt to these directional changes, resulting in a significant disconnect between the feedback effect and the realistic touch interaction experience. Furthermore, rendering methods based on visual haptic sensors can capture high-resolution images of the contact surface, providing more detailed information about the object's shape and the physical properties of touch. However, these types of sensors rely heavily on tactile datasets, and data acquisition requires active detection, which consumes a lot of time and cost. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories, a dynamic haptic rendering device based on two-dimensional images and finger movement trajectories, a computer device, and a storage medium to overcome or at least partially solve the above problems.

[0004] To address the aforementioned problems, this invention discloses a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories, comprising:

[0005] Obtain the original two-dimensional image;

[0006] The target object in the original two-dimensional image is converted into a three-dimensional structural point cloud;

[0007] The initial trajectory of the user's interaction with the target object is obtained, and a complete predicted trajectory is generated based on the initial trajectory;

[0008] The three-dimensional structural point cloud and the complete predicted trajectory are mapped to a unified coordinate system to obtain the corresponding point cloud data set and trajectory data set. Spatial matching of point cloud data and trajectory data is performed to obtain the matching point set.

[0009] The matching point set is transformed into virtual tactile stimulation values;

[0010] The stimulus value is converted into a driving voltage signal, and the operation of the device is controlled by the driving voltage signal.

[0011] Preferably, converting the target object in the original two-dimensional image into a three-dimensional structured point cloud includes:

[0012] A cross-modal diffusion generation method based on a single image is used to convert the target object into a three-dimensional structural point cloud.

[0013] Preferably, obtaining the initial trajectory of the user's interaction with the target object and generating a complete predicted trajectory based on the initial trajectory includes:

[0014] Record the positions of pixels on the touchscreen during the initial swipe of a finger as the historical swipe trajectory of the finger;

[0015] The historical sliding trajectory is input into the finger sliding prediction algorithm, which consists of a multi-layer Transformer encoder-decoder structure. The historical sliding trajectory is input into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory.

[0016] The input encoding block of the trajectory is input into the Transformer encoder to obtain the output finger trajectory features. The multilayer perceptron is used to map the finger trajectory features into a decision prediction trajectory.

[0017] Random Gaussian noise is used as an adaptive enhancement strategy without reference points. The finger trajectory features and random Gaussian noise are input into the Transformer decoder and then transformed into a dynamic predicted trajectory through a multilayer perceptron mapping.

[0018] The decision prediction trajectory and the dynamic prediction trajectory are connected and merged to obtain the final complete prediction trajectory.

[0019] Preferably, the step of mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets, and performing spatial matching of the point cloud data and trajectory data to obtain a matching point set includes:

[0020] Based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set, the spatial intersection region of the point cloud data point set and the trajectory data point set is obtained.

[0021] By using KD-Tree to construct a spatial index structure for spatial intersection regions, the nearest neighbor points of the trajectory data point set in the spatial intersection regions are calculated to obtain the index set with the minimum Euclidean distance in space;

[0022] Extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

[0023] Preferably, the step of transforming the matching point set into virtual tactile stimulus values ​​includes:

[0024] Interpolation is performed on the set of matching points to smooth and construct a continuous geometric contour curve;

[0025] Calculate the first derivative of each section point in the geometric contour curve, and use the first derivative as the stimulus value for virtual touch.

[0026] Preferably, the step of interpolating and smoothing the matching point set to construct a continuous geometric contour curve includes:

[0027] By matching the coordinate values ​​of the sliding direction and height direction corresponding to the point set, equidistant interpolation points are generated along the sliding direction;

[0028] Using a cubic spline interpolation function to interpolate at equidistant interpolation points, a continuous geometric profile curve is formed.

[0029] Preferably, the step of converting the stimulus value into a driving voltage signal and controlling the operation of the device through the driving voltage signal includes:

[0030] The virtual tactile stimulus values ​​are converted into voltage signals using a linear mapping function;

[0031] The voltage signal is modulated using a high-frequency carrier wave to obtain the final driving voltage signal, which is then used to control the operation of the device.

[0032] This invention discloses a dynamic haptic rendering device based on two-dimensional images and finger movement trajectories, comprising:

[0033] The first acquisition module is used to acquire the original two-dimensional image;

[0034] The conversion module is used to convert the target object in the original two-dimensional image into a three-dimensional structure point cloud;

[0035] The generation module is used to obtain the initial trajectory of the user's interaction with the target object and generate a complete predicted trajectory based on the initial trajectory.

[0036] The spatial matching module is used to map the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain the corresponding point cloud data point set and trajectory data point set, and to perform spatial matching of the point cloud data and trajectory data to obtain the matching point set.

[0037] The stimulus value transformation module is used to transform the matching point set into virtual tactile stimulus values;

[0038] The control module is used to convert the stimulus value into a driving voltage signal, and control the operation of the device through the driving voltage signal.

[0039] Preferably, the conversion module includes:

[0040] The transformation submodule is used to convert the target object into a 3D structured point cloud using a cross-modal diffusion generation method based on a single image.

[0041] This invention discloses a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described steps of dynamic haptic rendering based on two-dimensional images and finger movement trajectories.

[0042] This invention discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described steps of dynamic haptic rendering based on two-dimensional images and finger movement trajectories.

[0043] The embodiments of the present invention have the following advantages:

[0044] In this embodiment of the invention, the dynamic haptic rendering method based on two-dimensional images and finger movement trajectories includes: acquiring an original two-dimensional image; converting the target object in the original two-dimensional image into a three-dimensional structural point cloud; acquiring the initial trajectory of the user interacting with the target object, and generating a complete predicted trajectory based on the initial trajectory; mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets, performing spatial matching of the point cloud data and trajectory data to obtain a matching point set; performing interpolation smoothing on the matching point set to construct a continuous geometric contour curve; calculating the first derivative of each segment in the geometric contour curve, and using the first derivative as the stimulus value for virtual haptics; converting the stimulus value into a driving voltage signal, and controlling the operation of the device through the driving voltage signal. This invention provides a dynamic haptic rendering generation method based on two-dimensional images and finger movement trajectories, breaking through the limitations of traditional haptic feedback relying on static templates and dedicated sampling data, and realizing automatic modeling and real-time response from two-dimensional visual input to physical haptic output. This invention is the first to collaboratively apply visual reconstruction and sliding trajectory perception for tactile signal generation. Dynamic spatial matching is achieved through geometric modeling and trajectory prediction, replacing the traditional "predefined plus fixed output" tactile rendering process. Furthermore, this invention employs a prediction mechanism to dynamically predict the user's sliding trajectory. This model can dynamically generate personalized tactile feedback based on changes in the sliding direction and path of different users. The system can complete trajectory updates and virtual tactile feature reconstruction within 100 ms. The implementation method of this invention is simple and flexible, requiring only a single ordinary 2D image and sliding trajectory to generate high-fidelity tactile signals for electrostatic device driving. This end-to-end data generation mechanism has broad adaptability and platform independence, applicable to any planar image and user sliding behavior. This invention possesses the ability to quickly generate realistic physical feedback without complex hardware support, significantly accelerating the widespread application of tactile interaction in consumer electronics, accessibility, and human-computer collaboration, and is of great significance for promoting the industrialization of universal tactile technology. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to an embodiment of the present invention;

[0047] Figure 2This is a flowchart illustrating the steps of an embodiment of a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram illustrating the interaction between an original two-dimensional image and an embodiment of the present invention.

[0049] Figure 4 This is a flowchart illustrating a trajectory prediction algorithm according to an embodiment of the present invention;

[0050] Figure 5 This is a structural block diagram of an embodiment of a dynamic haptic rendering device based on two-dimensional images and finger movement trajectories according to an embodiment of the present invention;

[0051] Figure 6 This is an internal structural diagram of a computer device according to one embodiment. Detailed Implementation

[0052] To make the technical problems, technical solutions, and beneficial effects solved by the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0053] This invention aims to provide a dynamic haptic rendering generation method based on two-dimensional images and finger movement trajectories, overcoming problems in existing technologies such as delayed haptic feedback response, lack of motion trajectory adaptation, and mismatch between feedback information and actual interactive experience. The proposed method can automatically generate haptic signals with direction awareness by combining the user's finger sliding trajectory and the structural features of the target object in the image, such as... Figure 1 As shown, this significantly enhances the realism and immersion during interaction. It solves the problem that most existing haptic rendering methods rely on static predefined textures and cannot respond in real-time to the dynamic sliding path of the user's finger and the geometric changes of the target surface.

[0054] Reference Figure 2 The diagram illustrates a flowchart of an embodiment of a dynamic haptic rendering method based on a two-dimensional image and finger movement trajectory according to the present invention, which may specifically include the following steps:

[0055] Step 101: Obtain the original two-dimensional image;

[0056] In this embodiment of the invention, the method can be applied to various terminals such as smart computers, personal computers, laptops, smartphones, tablets, and portable wearable devices. This embodiment of the invention does not limit the specific type of terminal. The operating system of the terminal may include Android, Harmony OS, iOS, Windows Phone, Windows, etc. This invention does not impose excessive restrictions on this.

[0057] In a specific embodiment of the present invention, the terminal may also be connected to an electrostatic tactile feedback device, which is used to provide tactile feedback to the user.

[0058] In this embodiment of the invention, a single original two-dimensional image can first be obtained, such as... Figure 3 The image shown is the original two-dimensional image displayed on the terminal's screen.

[0059] Step 102: Convert the target object in the original two-dimensional image into a three-dimensional structured point cloud;

[0060] Further applied to embodiments of the present invention, after obtaining the original two-dimensional image, the target object in the original two-dimensional image can be further converted into a three-dimensional structural point cloud, and the three-dimensional structural information of the target object can be obtained from a single two-dimensional image. A cross-modal diffusion generation method based on a single image is employed to transform the image... Convert to a dense 3D point cloud ;

[0061] Specifically, in this embodiment of the invention, converting the target object in the original two-dimensional image into a three-dimensional structured point cloud includes: converting the target object into a three-dimensional structured point cloud using a cross-modal diffusion generation method based on a single image.

[0062] For example, Figure 3 A specific animal image is identified as the target object, and this target object is converted into a three-dimensional point cloud structure.

[0063] Step 103: Obtain the initial trajectory of the user's interaction with the target object, and generate a complete predicted trajectory based on the initial trajectory;

[0064] On the other hand, the initial trajectory of the user's interaction with the target object can also be obtained, such as Figure 3 The trajectory shown is used to generate a complete predicted trajectory based on the initial trajectory.

[0065] In this embodiment of the invention, obtaining the initial trajectory of the user's interaction with the target object and generating a complete predicted trajectory based on the initial trajectory includes:

[0066] The system records the pixel positions on the touchscreen during the initial swipe of a finger as the finger's historical swipe trajectory; the user swipes their finger on the touchscreen to interact with the target image, such as... Figure 3 As shown, the system records finger trajectories in real time through an integrated finger swipe prediction algorithm (FTP) and dynamically predicts the complete trajectory path in the future. The process of the trajectory prediction algorithm is as follows: Figure 4 As shown;

[0067] Record the position of the touchscreen pixels during the initial swipe of the finger as the historical swipe trajectory of the finger. L represents the length of the time series;

[0068] The historical sliding trajectory is input into the finger sliding prediction algorithm, which consists of a multi-layer Transformer encoder-decoder structure. The historical sliding trajectory is input into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory.

[0069] Historical sliding trajectory The input is fed into the proposed finger swipe prediction algorithm FTP, which consists of a multi-layer Transformer encoder-decoder structure. Firstly, the input is... The input is fed into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory;

[0070] The input encoding block of the trajectory is input into the Transformer encoder to obtain the output finger trajectory features. The multilayer perceptron is used to map the finger trajectory features into a decision prediction trajectory.

[0071] The trajectory input encoding block obtained in the previous step is input into the Transformer encoder of the algorithm, which outputs the finger trajectory feature. (D represents the encoding length), using a multilayer perceptron to... Mapped to decision prediction trajectory ( (The predicted length of the decision prediction trajectory);

[0072] Random Gaussian noise is used as an adaptive enhancement strategy without reference points. The finger trajectory features and random Gaussian noise are input into the Transformer decoder and then transformed into a dynamic predicted trajectory through a multilayer perceptron mapping.

[0073] Use random Gaussian noise As an adaptive augmentation strategy without reference points, and The input is fed into the Transformer decoder and then mapped by a multilayer perceptron to be transformed into a dynamically predicted trajectory. ;in, Indicates the predicted length of the dynamically predicted trajectory;

[0074] The decision prediction trajectory and the dynamic prediction trajectory are connected and merged to obtain the final complete prediction trajectory.

[0075] Will and The connection and merging process yields the final predicted complete finger trajectory. .

[0076] Step 104: Map the three-dimensional structure point cloud and the complete predicted trajectory to a unified coordinate system to obtain the corresponding point cloud data set and trajectory data set. Perform spatial matching of the point cloud data and trajectory data to obtain the matching point set.

[0077] In practical application to this embodiment of the invention, mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets, and performing spatial matching of the point cloud data and trajectory data to obtain a matching point set, includes:

[0078] Based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set, the spatial intersection region of the point cloud data point set and the trajectory data point set is obtained.

[0079] A spatial index structure for spatial intersection regions is constructed using KD-Tree. The nearest neighbor points of the trajectory data point set within these regions are calculated to obtain the index set with the minimum Euclidean distance. Based on the horizontal and vertical coordinate boundaries of the screen trajectory, the corresponding valid trajectory segments and intersecting point cloud regions are extracted. By constructing a nearest neighbor matching structure based on KD-Tree, the shortest distance between each valid trajectory point and the point cloud region is calculated, yielding the interaction cross-section between the sliding trajectory and the point cloud region.

[0080] Extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

[0081] The point cloud from step 102 and the complete predicted trajectory obtained in step 103 Mapping each point to a unified coordinate system yields the corresponding point cloud data set. and trajectory data point set Spatial matching of point cloud data and trajectory data is performed; among them, This represents the x-coordinate of the Lth point. This represents the y-coordinate of the Lth point. This represents the z-coordinate of the Lth point. This indicates the position of the Nth point in the u direction. Let L represent the position of the Nth point in the v direction, L represent the number of point cloud data points, and N represent the number of trajectory data points. The process includes the following sub-steps:

[0082] S31, Based on the finger trajectory point Horizontal range and point cloud point set The vertical range, to obtain and Spatial intersection area ; where, in the formula Let v represent the position of the nth point in the v direction, n = 1, 2, 3, ..., N; n is the sequence of finger trajectory points; T represents the number of valid trajectory points, and K represents the number of point clouds in the spatial intersection region.

[0083] S32. Constructing a KD-Tree based on... Spatial index structure for trajectory point set Calculate its in Find the nearest neighbor points in the array and obtain the set of indices with the smallest Euclidean distance in space. Where T in the formula represents the number of elements in the index set, which corresponds to the number of valid trajectory points, and one valid trajectory point corresponds to one distance index.

[0084] S33, from Extract from index The point set in the data is used to obtain the matching point set between the point cloud data and the trajectory data. Visual reconstruction and sliding trajectory perception are used in synergy for tactile signal generation, and dynamic spatial matching is achieved through geometric modeling and trajectory prediction.

[0085] Step 105: Transform the set of matching points into virtual tactile stimulation values;

[0086] In this embodiment of the invention, transforming the matching point set into virtual tactile stimulus values ​​includes:

[0087] Interpolation is performed on the set of matching points to smooth and construct a continuous geometric contour curve;

[0088] Calculate the first derivative of each section point in the geometric contour curve, and use the first derivative as the stimulus value for virtual touch.

[0089] Furthermore, in a preferred embodiment of the present invention, the step of interpolating and smoothing the matching point set to construct a continuous geometric contour curve includes:

[0090] By matching the coordinate values ​​of the sliding direction and height direction corresponding to the point set, equidistant interpolation points are generated along the sliding direction;

[0091] Using a cubic spline interpolation function to interpolate at equidistant interpolation points, a continuous geometric profile curve is formed.

[0092] Specifically, using The coordinates of the corresponding sliding direction and geometric height direction (i.e., the coordinates of this point set) The projected values ​​on the X and Z axes, where the X-axis represents the sliding direction and the Z-axis represents the corresponding geometric height direction), between each pair of adjacent sampling points (e.g. and Equidistant interpolation points are generated along the sliding direction. ; where, in the formula Represents the x-coordinates of adjacent sampling points. Represents the z-coordinate of adjacent sampling points. This represents the j-th interpolation point in the x-direction. This represents the sequence corresponding to the interpolation point. This indicates the number of interpolation points.

[0093] Using cubic spline interpolation function Interpolating the interpolation points generated in S41 creates a continuous geometric profile curve. ; where, in the formula This represents the j-th interpolation point in the z-direction. This represents the sequence corresponding to the interpolation point. The number of interpolation points is indicated by the interactive point cloud cross-section obtained in step 104. First, an interpolation smoothing operation is performed on this cross-section to construct a continuous geometric contour distribution. Then, the first-order gradient of this cross-section surface is calculated in the sliding direction, resulting in a gradient sequence reflecting the surface undulations of the object. This gradient value can be considered the basis for representing the intensity of virtual tactile stimulation, used to characterize the frictional changes and surface height differences perceived by the user's fingertips when sliding in different areas, constituting a digital feature that can be mapped to a real tactile signal.

[0094] In this embodiment of the invention, the smooth geometric profile obtained in step 105 is used. The first derivative of each point on the profile is calculated. This derivative value is used as the stimulus value of virtual tactile sensation to characterize the frictional changes and surface height differences perceived by the user's fingertips when sliding in different areas, thus forming a digital feature that can be mapped to a real tactile signal.

[0095] Step 106: Convert the stimulus value into a driving voltage signal, and control the operation of the device through the driving voltage signal.

[0096] Specifically, the virtual tactile stimulation values ​​obtained in step 106 The signal is converted into a driving voltage signal that can be recognized by the electrostatic haptic feedback device. This voltage signal controls the screen surface to generate a corresponding physical friction force, thereby forming a dynamic and realistic tactile stimulus that adaptively matches the image target and the sliding trajectory. The electrostatic haptic feedback device can also be replaced by tactile devices such as ultrasonic arrays and motor vibrations, and the embodiments of the present invention do not impose too many limitations on this.

[0097] In this embodiment of the invention, converting the stimulus value into a driving voltage signal and controlling the operation of the device through the driving voltage signal includes:

[0098] The virtual tactile stimulus values ​​are converted into voltage signals using a linear mapping function;

[0099] The voltage signal is modulated using a high-frequency carrier wave to obtain the final generated driving voltage signal, which controls the operation of the device. The device may include an electrostatic haptic feedback device; however, this embodiment of the invention does not impose excessive limitations on this.

[0100] Using linear mapping functions Virtual tactile stimulation Converted to voltage signal This is the magnification factor;

[0101] Using high-frequency carrier The voltage signal generated in modulation S61 The final generated driving voltage signal is obtained. .

[0102] In this embodiment of the invention, the dynamic haptic rendering method based on two-dimensional images and finger movement trajectories includes: acquiring an original two-dimensional image; converting the target object in the original two-dimensional image into a three-dimensional structural point cloud; acquiring the initial trajectory of the user interacting with the target object, and generating a complete predicted trajectory based on the initial trajectory; mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets, performing spatial matching of the point cloud data and trajectory data to obtain a matching point set; performing interpolation smoothing on the matching point set to construct a continuous geometric contour curve; calculating the first derivative of each segment in the geometric contour curve, and using the first derivative as the stimulus value for virtual haptics; converting the stimulus value into a driving voltage signal, and controlling the operation of the device through the driving voltage signal. This invention provides a dynamic haptic rendering generation method based on two-dimensional images and finger movement trajectories, breaking through the limitations of traditional haptic feedback relying on static templates and dedicated sampling data, and realizing automatic modeling and real-time response from two-dimensional visual input to physical haptic output. This invention is the first to collaboratively apply visual reconstruction and sliding trajectory perception to tactile signal generation. Dynamic spatial matching is achieved through geometric modeling and trajectory prediction, replacing the traditional "predefined plus fixed output" tactile rendering process. Furthermore, this invention employs a prediction mechanism to dynamically predict the user's sliding trajectory. This model can dynamically generate personalized tactile feedback based on changes in the sliding direction and path of different users. The system can complete trajectory updates and virtual tactile feature reconstruction within 100 ms. This invention is simple to implement and flexible, requiring only a single ordinary 2D image and sliding trajectory to generate high-fidelity tactile signals for electrostatic device driving. This end-to-end data generation mechanism has broad adaptability and platform independence, applicable to any planar image and user sliding behavior. Currently, there is an urgent need for low-cost, highly flexible tactile interaction technology in scenarios such as digital media, virtual reality, distance education, and rehabilitation training. This invention possesses the ability to quickly generate realistic physical feedback without complex hardware support, significantly accelerating the widespread application of tactile interaction in consumer electronics, accessibility, and human-computer collaboration, and is of great significance for promoting the industrialization of universal tactile technology.

[0103] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0104] Reference Figure 5This diagram illustrates a structural block diagram of a dynamic haptic rendering device based on a two-dimensional image and finger movement trajectory according to an embodiment of the present invention. Specifically, it may include the following modules:

[0105] The first acquisition module 301 is used to acquire the original two-dimensional image;

[0106] The conversion module 302 is used to convert the target object in the original two-dimensional image into a three-dimensional structure point cloud;

[0107] The generation module 303 is used to obtain the initial trajectory of the user's interaction with the target object and generate a complete predicted trajectory based on the initial trajectory;

[0108] The spatial matching module 304 is used to map the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain the corresponding point cloud data point set and trajectory data point set, and to perform spatial matching of the point cloud data and trajectory data to obtain the matching point set.

[0109] Stimulus value transformation module 305 is used to transform the matching point set into virtual tactile stimulation values;

[0110] The control module 306 is used to convert the stimulus value into a driving voltage signal and control the operation of the device through the driving voltage signal.

[0111] Preferably, the conversion module includes:

[0112] The transformation submodule is used to convert the target object into a 3D structured point cloud using a cross-modal diffusion generation method based on a single image.

[0113] Preferably, the generation module includes:

[0114] The recording submodule is used to record the positions of touchscreen pixels during the initial swipe of a finger on the touchscreen as the historical swipe trajectory of the finger.

[0115] The input submodule is used to input the historical sliding trajectory into the finger sliding prediction algorithm. The finger sliding prediction algorithm consists of a multi-layer Transformer encoder-decoder structure, which inputs the historical sliding trajectory into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory.

[0116] The mapping submodule is used to input the input encoded block of the trajectory into the Transformer encoder to obtain the output finger trajectory features, and then use a multilayer perceptron to map the finger trajectory features into a decision prediction trajectory.

[0117] The transformation submodule is used to use random Gaussian noise as an adaptive enhancement strategy without reference points. It inputs finger trajectory features and random Gaussian noise into the Transformer decoder and transforms them into a dynamic predicted trajectory through a multilayer perceptron mapping.

[0118] The connection and merging submodule is used to connect and merge the decision prediction trajectory and the dynamic prediction trajectory to obtain the final complete prediction trajectory.

[0119] Preferably, the spatial matching module includes:

[0120] The spatial intersection region submodule is used to obtain the spatial intersection region of the point cloud data point set and the trajectory data point set based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set.

[0121] The index set submodule is used to construct a spatial index structure for spatial intersection regions using KD-Tree, calculate the nearest neighbor of the trajectory data point set in the spatial intersection region, and obtain the index set with the minimum Euclidean distance in space.

[0122] The extraction submodule is used to extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

[0123] The stimulus value transformation module includes:

[0124] A submodule is constructed to perform interpolation smoothing on the matching point set to construct a continuous geometric contour curve;

[0125] The calculation submodule is used to calculate the first derivative of each section point in the geometric contour curve and use the first derivative as the stimulus value of virtual touch.

[0126] Preferably, the construction submodule includes:

[0127] Interpolation point units are used to generate equidistant interpolation points along the sliding direction by matching the coordinate values ​​of the corresponding sliding and height directions in the point set;

[0128] The constituent units are used to interpolate equidistant interpolation points using a cubic spline interpolation function to form a continuous geometric profile curve.

[0129] Preferably, the control module includes:

[0130] The conversion submodule is used to convert virtual tactile stimulus values ​​into voltage signals using a linear mapping function;

[0131] The modulation submodule is used to modulate the voltage signal using a high-frequency carrier to obtain the final generated drive voltage signal, and to control the operation of the device through the drive voltage signal.

[0132] The modules in the aforementioned dynamic haptic rendering device based on two-dimensional images and finger movement trajectories can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0133] The dynamic haptic rendering device based on two-dimensional images and finger movement trajectories provided above can be used to execute the dynamic haptic rendering method based on two-dimensional images and finger movement trajectories provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0134] In one embodiment, a computer device is provided, the internal structure of which can be shown in the figure below. Figure 6 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a dynamic haptic rendering method based on two-dimensional images and finger movement trajectories. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0135] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0136] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0137] Obtain the original two-dimensional image;

[0138] The target object in the original two-dimensional image is converted into a three-dimensional structural point cloud;

[0139] The initial trajectory of the user's interaction with the target object is obtained, and a complete predicted trajectory is generated based on the initial trajectory;

[0140] The three-dimensional structural point cloud and the complete predicted trajectory are mapped to a unified coordinate system to obtain the corresponding point cloud data set and trajectory data set. Spatial matching of point cloud data and trajectory data is performed to obtain the matching point set.

[0141] The matching point set is transformed into virtual tactile stimulation values;

[0142] The stimulus value is converted into a driving voltage signal, and the operation of the device is controlled by the driving voltage signal.

[0143] Preferably, converting the target object in the original two-dimensional image into a three-dimensional structured point cloud includes:

[0144] A cross-modal diffusion generation method based on a single image is used to convert the target object into a three-dimensional structural point cloud.

[0145] Preferably, obtaining the initial trajectory of the user's interaction with the target object and generating a complete predicted trajectory based on the initial trajectory includes:

[0146] Record the positions of pixels on the touchscreen during the initial swipe of a finger as the historical swipe trajectory of the finger;

[0147] The historical sliding trajectory is input into the finger sliding prediction algorithm, which consists of a multi-layer Transformer encoder-decoder structure. The historical sliding trajectory is input into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory.

[0148] The input encoding block of the trajectory is input into the Transformer encoder to obtain the output finger trajectory features. The multilayer perceptron is used to map the finger trajectory features into a decision prediction trajectory.

[0149] Random Gaussian noise is used as an adaptive enhancement strategy without reference points. The finger trajectory features and random Gaussian noise are input into the Transformer decoder and then transformed into a dynamic predicted trajectory through a multilayer perceptron mapping.

[0150] The decision prediction trajectory and the dynamic prediction trajectory are connected and merged to obtain the final complete prediction trajectory.

[0151] Preferably, the step of mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets, and performing spatial matching of the point cloud data and trajectory data to obtain a matching point set includes:

[0152] Based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set, the spatial intersection region of the point cloud data point set and the trajectory data point set is obtained.

[0153] By using KD-Tree to construct a spatial index structure for spatial intersection regions, the nearest neighbor points of the trajectory data point set in the spatial intersection regions are calculated to obtain the index set with the minimum Euclidean distance in space;

[0154] Extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

[0155] Preferably, the step of transforming the matching point set into virtual tactile stimulus values ​​includes:

[0156] Interpolation is performed on the set of matching points to smooth and construct a continuous geometric contour curve;

[0157] Calculate the first derivative of each section point in the geometric contour curve, and use the first derivative as the stimulus value for virtual touch.

[0158] Preferably, the step of interpolating and smoothing the matching point set to construct a continuous geometric contour curve includes:

[0159] By matching the coordinate values ​​of the sliding direction and height direction corresponding to the point set, equidistant interpolation points are generated along the sliding direction;

[0160] Using a cubic spline interpolation function to interpolate at equidistant interpolation points, a continuous geometric profile curve is formed.

[0161] Preferably, the step of converting the stimulus value into a driving voltage signal and controlling the operation of the device through the driving voltage signal includes:

[0162] The virtual tactile stimulus values ​​are converted into voltage signals using a linear mapping function;

[0163] The voltage signal is modulated using a high-frequency carrier wave to obtain the final driving voltage signal, which is then used to control the operation of the device.

[0164] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0165] Obtain the original two-dimensional image;

[0166] The target object in the original two-dimensional image is converted into a three-dimensional structural point cloud;

[0167] The initial trajectory of the user's interaction with the target object is obtained, and a complete predicted trajectory is generated based on the initial trajectory;

[0168] The three-dimensional structural point cloud and the complete predicted trajectory are mapped to a unified coordinate system to obtain the corresponding point cloud data set and trajectory data set. Spatial matching of point cloud data and trajectory data is performed to obtain the matching point set.

[0169] The matching point set is transformed into virtual tactile stimulation values;

[0170] The stimulus value is converted into a driving voltage signal, and the operation of the device is controlled by the driving voltage signal.

[0171] Preferably, converting the target object in the original two-dimensional image into a three-dimensional structured point cloud includes:

[0172] A cross-modal diffusion generation method based on a single image is used to convert the target object into a three-dimensional structural point cloud.

[0173] Preferably, obtaining the initial trajectory of the user's interaction with the target object and generating a complete predicted trajectory based on the initial trajectory includes:

[0174] Record the positions of pixels on the touchscreen during the initial swipe of a finger as the historical swipe trajectory of the finger;

[0175] The historical sliding trajectory is input into the finger sliding prediction algorithm, which consists of a multi-layer Transformer encoder-decoder structure. The historical sliding trajectory is input into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory.

[0176] The input encoding block of the trajectory is input into the Transformer encoder to obtain the output finger trajectory features. The multilayer perceptron is used to map the finger trajectory features into a decision prediction trajectory.

[0177] Random Gaussian noise is used as an adaptive enhancement strategy without reference points. The finger trajectory features and random Gaussian noise are input into the Transformer decoder and then transformed into a dynamic predicted trajectory through a multilayer perceptron mapping.

[0178] The decision prediction trajectory and the dynamic prediction trajectory are connected and merged to obtain the final complete prediction trajectory.

[0179] Preferably, the step of mapping the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets, and performing spatial matching of the point cloud data and trajectory data to obtain a matching point set includes:

[0180] Based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set, the spatial intersection region of the point cloud data point set and the trajectory data point set is obtained.

[0181] By using KD-Tree to construct a spatial index structure for spatial intersection regions, the nearest neighbor points of the trajectory data point set in the spatial intersection regions are calculated to obtain the index set with the minimum Euclidean distance in space;

[0182] Extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

[0183] Preferably, the step of transforming the matching point set into virtual tactile stimulus values ​​includes:

[0184] Interpolation is performed on the set of matching points to smooth and construct a continuous geometric contour curve;

[0185] Calculate the first derivative of each section point in the geometric contour curve, and use the first derivative as the stimulus value for virtual touch.

[0186] Preferably, the step of interpolating and smoothing the matching point set to construct a continuous geometric contour curve includes:

[0187] By matching the coordinate values ​​of the sliding direction and height direction corresponding to the point set, equidistant interpolation points are generated along the sliding direction;

[0188] Using a cubic spline interpolation function to interpolate at equidistant interpolation points, a continuous geometric profile curve is formed.

[0189] Preferably, the step of converting the stimulus value into a driving voltage signal and controlling the operation of the device through the driving voltage signal includes:

[0190] The virtual tactile stimulus values ​​are converted into voltage signals using a linear mapping function;

[0191] The voltage signal is modulated using a high-frequency carrier wave to obtain the final driving voltage signal, which is then used to control the operation of the device.

[0192] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0193] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0195] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0197] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0198] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0199] The foregoing has provided a detailed description of a dynamic haptic rendering method, a dynamic haptic rendering device, a computer device, and a storage medium based on two-dimensional images and finger movement trajectories provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A dynamic haptic rendering method based on two-dimensional images and finger movement trajectories, characterized in that, include: Obtain the original two-dimensional image; The target object in the original two-dimensional image is converted into a three-dimensional structural point cloud; Obtain the initial trajectory of the user's interaction with the target object, and generate a complete predicted trajectory based on the initial trajectory; The three-dimensional structural point cloud and the complete predicted trajectory are mapped to a unified coordinate system to obtain the corresponding point cloud data set and trajectory data set. Spatial matching of point cloud data and trajectory data is performed to obtain the matching point set. The matching point set is transformed into virtual tactile stimulation values; The stimulus value is converted into a driving voltage signal, and the operation of the device is controlled by the driving voltage signal. The step of obtaining the initial trajectory of the user's interaction with the target object and generating a complete predicted trajectory based on the initial trajectory includes: Record the positions of pixels on the touchscreen during the initial swipe of a finger as the historical swipe trajectory of the finger; The historical sliding trajectory is input into the finger sliding prediction algorithm, which consists of a multi-layer Transformer encoder-decoder structure. The historical sliding trajectory is input into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory. The input encoding block of the trajectory is input into the Transformer encoder to obtain the output finger trajectory features. The multilayer perceptron is used to map the finger trajectory features into a decision prediction trajectory. Random Gaussian noise is used as an adaptive enhancement strategy without reference points. The finger trajectory features and random Gaussian noise are input into the Transformer decoder and then transformed into a dynamic predicted trajectory through a multilayer perceptron mapping. The decision prediction trajectory and the dynamic prediction trajectory are connected and merged to obtain the final complete prediction trajectory.

2. The dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to claim 1, characterized in that, The step of converting the target object in the original two-dimensional image into a three-dimensional structured point cloud includes: A cross-modal diffusion generation method based on a single image is used to convert the target object into a three-dimensional structural point cloud.

3. The dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to claim 1, characterized in that, The process involves mapping the three-dimensional structure point cloud and the complete predicted trajectory to a unified coordinate system to obtain corresponding point cloud data sets and trajectory data sets. Spatial matching of the point cloud data and trajectory data is then performed to obtain a matching point set, including: Based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set, the spatial intersection region of the point cloud data point set and the trajectory data point set is obtained. By using KD-Tree to construct a spatial index structure for spatial intersection regions, the nearest neighbor points of the trajectory data point set in the spatial intersection regions are calculated to obtain the index set with the minimum Euclidean distance in space; Extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

4. The dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to claim 1, characterized in that, The step of transforming the matching point set into virtual tactile stimulation values ​​includes: Interpolation is performed on the set of matching points to smooth and construct a continuous geometric contour curve; Calculate the first derivative of each section point in the geometric contour curve, and use the first derivative as the stimulus value for virtual touch.

5. The dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to claim 4, characterized in that, The step of interpolating and smoothing the matching point set to construct a continuous geometric contour curve includes: By matching the coordinate values ​​of the sliding direction and height direction corresponding to the point set, equidistant interpolation points are generated along the sliding direction; Using a cubic spline interpolation function to interpolate at equidistant interpolation points, a continuous geometric profile curve is formed.

6. The dynamic haptic rendering method based on two-dimensional images and finger movement trajectories according to claim 1, characterized in that, The step of converting the stimulus value into a driving voltage signal and controlling the operation of the device through the driving voltage signal includes: The virtual tactile stimulus values ​​are converted into voltage signals using a linear mapping function; The voltage signal is modulated using a high-frequency carrier wave to obtain the final driving voltage signal, which is then used to control the operation of the device.

7. A dynamic haptic rendering device based on two-dimensional images and finger movement trajectories, characterized in that, include: The first acquisition module is used to acquire the original two-dimensional image; The conversion module is used to convert the target object in the original two-dimensional image into a three-dimensional structure point cloud; The generation module is used to obtain the initial trajectory of the user's interaction with the target object, and generate a complete predicted trajectory based on the initial trajectory; The spatial matching module is used to map the three-dimensional structural point cloud and the complete predicted trajectory to a unified coordinate system to obtain the corresponding point cloud data point set and trajectory data point set, and to perform spatial matching of the point cloud data and trajectory data to obtain the matching point set. The stimulus value transformation module is used to transform the matching point set into virtual tactile stimulus values; The control module is used to convert the stimulus value into a driving voltage signal, and control the operation of the device through the driving voltage signal; The generation module includes: The recording submodule is used to record the positions of touchscreen pixels during the initial swipe of a finger on the touchscreen as the historical swipe trajectory of the finger. The input submodule is used to input the historical sliding trajectory into the finger sliding prediction algorithm. The finger sliding prediction algorithm consists of a multi-layer Transformer encoder-decoder structure, which inputs the historical sliding trajectory into a fully connected network layer and a PReLu activation layer to obtain the input encoding block of the trajectory. The mapping submodule is used to input the input encoded block of the trajectory into the Transformer encoder to obtain the output finger trajectory features, and then use a multilayer perceptron to map the finger trajectory features into a decision prediction trajectory. The transformation submodule is used to use random Gaussian noise as an adaptive enhancement strategy without reference points. It inputs finger trajectory features and random Gaussian noise into the Transformer decoder and transforms them into a dynamic predicted trajectory through a multilayer perceptron mapping. The connection and merging submodule is used to connect and merge the decision prediction trajectory and the dynamic prediction trajectory to obtain the final complete prediction trajectory.

8. The dynamic haptic rendering device based on two-dimensional images and finger movement trajectories according to claim 7, characterized in that, The conversion module includes: The transformation submodule is used to convert the target object into a 3D structured point cloud using a cross-modal diffusion generation method based on a single image.

9. The dynamic haptic rendering device based on two-dimensional images and finger movement trajectories according to claim 7, characterized in that, The spatial matching module includes: The spatial intersection region submodule is used to obtain the spatial intersection region of the point cloud data point set and the trajectory data point set based on the vertical range of the point cloud data point set and the horizontal range of the trajectory data point set. The index set submodule is used to construct a spatial index structure for spatial intersection regions using KD-Tree, calculate the nearest neighbor of the trajectory data point set in the spatial intersection region, and obtain the index set with the minimum Euclidean distance in space. The extraction submodule is used to extract the point set of the spatial intersection region from the index values ​​of the index set to obtain the matching point set.

10. The dynamic haptic rendering device based on two-dimensional images and finger movement trajectories according to claim 7, characterized in that, The stimulus value transformation module includes: A submodule is constructed to perform interpolation smoothing on the matching point set to construct a continuous geometric contour curve; The calculation submodule is used to calculate the first derivative of each section point in the geometric contour curve and use the first derivative as the stimulus value of virtual touch.

11. The dynamic haptic rendering device based on two-dimensional images and finger movement trajectories according to claim 10, characterized in that, The construction submodule includes: Interpolation point units are used to generate equidistant interpolation points along the sliding direction by matching the coordinate values ​​of the corresponding sliding and height directions in the point set; The constituent units are used to interpolate equidistant interpolation points using a cubic spline interpolation function to form a continuous geometric profile curve.

12. The dynamic haptic rendering device based on two-dimensional images and finger movement trajectories according to claim 7, characterized in that, The control module includes: The conversion submodule is used to convert virtual tactile stimulus values ​​into voltage signals using a linear mapping function; The modulation submodule is used to modulate the voltage signal using a high-frequency carrier to obtain the final generated drive voltage signal, and to control the operation of the device through the drive voltage signal.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic haptic rendering method based on two-dimensional images and finger movement trajectories as described in any one of claims 1 to 6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic haptic rendering method based on two-dimensional images and finger movement trajectories as described in any one of claims 1 to 6.

Images