Tencent Patente

The present application relates the field of machine learning. Disclosed are an image processing method and apparatus, and a device, a medium and a program product. The method comprises: acquiring a noise-added image corresponding to a first image; performing semantic analysis on the first image, so as to obtain semantic analysis data; performing image denoising processing on the noise-added image on the basis of the semantic analysis data, so as to obtain a second image; and on the basis of the feature similarity between the first image and the second image, determining a defect identification result corresponding to the first image. That is, the semantic analysis data is guided to perform image denoising, so that an image object in the second image after denoising being consistent with an image object in the first image is ensured, and then the defect identification result is determined by means of the feature similarity that is obtained by means of a comparison, so that the accuracy and identification efficiency of image object identification can be improved.

The present application belongs to the technical field of computers. Disclosed are a control method and apparatus, a device, and a computer-readable storage medium. The method comprises: displaying a virtual environment picture, the virtual environment picture displaying a direction control, the direction control being used for controlling a virtual character to move in a virtual environment, the display mode of the direction control being a first display mode, and the first display mode being used for indicating that the moving mode of the virtual character in the virtual environment is a non-stealth mode (201); and, in response to a target gesture operation in respect of the direction control, displaying the direction control in an adjusted display mode of the direction control, an operation distance corresponding to the target gesture operation not being greater than a stealth range threshold, and the adjusted display mode of the direction control being used for indicating that the moving mode of the virtual character in the virtual environment is a stealth mode (202). This method allows users to determine moving modes of virtual characters in virtual environments with higher accuracy.

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A collision detection method, executed by a computer device. The method comprises: acquiring trajectory information to be tested of a first movable device, said trajectory information comprising: a plurality of timestamps within a first trajectory time period of the first movable device, position information of the first movable device at each timestamp, and physical size information of the first movable device (S201); for each timestamp, on the basis of the position information of the first movable device at the timestamp and the physical size information of the first movable device, constructing a first three-dimensional space structure corresponding to the timestamp (S202); acquiring existing trajectory information of a second movable device, wherein the second movable device has been subjected to collision detection and an obtained collision detection feedback result indicates that no collision will occur, the existing trajectory information comprising: a plurality of timestamps of the second movable device in a second trajectory time period and a second three-dimensional space structure corresponding to each timestamp, and each second three-dimensional space structure being a three-dimensional space structure constructed on the basis of the physical size information of the second movable device, and an interference distance and operation range of the second movable device at a timestamp corresponding to the second three-dimensional space structure (S203); by means of using a collision detection policy, performing collision detection processing on the first three-dimensional space structure and the second three-dimensional space structure corresponding to the same timestamp to obtain a collision detection result corresponding to the same timestamp (S204); and on the basis of the collision detection results corresponding to the timestamps amongst the plurality of timestamps, generating a collision detection feedback result of the first movable device (S205).

The present application belongs to the technical field of speech synthesis. Disclosed are a speech synthesis method, an apparatus, a device, a storage medium and a program product. The method comprises: acquiring a semantic feature and an acoustic feature, the semantic feature being used for representing the feature of text information corresponding to a target audio to be synthesized; by means of a first-layer sub-model in a speech synthesis model, embedding the semantic feature into the acoustic feature, so as to obtain an intermediate acoustic feature; inputting the intermediate acoustic feature into a second-layer sub-model in the speech synthesis model, so as to obtain an audio synthesis feature; and, on the basis of the audio synthesis feature, generating a synthesized audio having the same timbre as a reference speech. By means of the method, the sound output by the speech synthesis model can better conform to the actual sound production characteristics of a subject corresponding to the selected timbre, thereby improving the authenticity of speech synthesis.

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The present application discloses a model training method and apparatus, and a biometric image generation method and apparatus. The model training method comprises: acquiring a first sample image, sample distribution data, a second sample image, and a first preset signal; inputting into a generation model to be trained a first encoded feature of the first sample image and the sample distribution data for biometric image generation processing to obtain a target sample image; by incorporating a first object recognition result corresponding to the first sample image, a second object recognition result corresponding to the second sample image, and a third object recognition result corresponding to the target sample image, determining a comparison recognition loss; determining a biological texture loss on the basis of the first encoded feature and the first preset signal; and training said generation model on the basis of the comparison recognition loss and the biological texture loss to obtain an image generation model. The present application can improve the performance of the image generation model, thereby improving the biometric image generation effect by means of the image generation model.

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The present application relates to an image generation method and apparatus based on video frame interpolation, and a computer device. The method comprises: acquiring two video frame images, and on the basis of the displacement of pixel points in the two video frame images, determining first optical flow information between the two video frame images (202); on the basis of the two video frame images and the first optical flow information, predicting an intermediate frame image between the two video frame images to obtain a predicted intermediate frame image (204); for each of the two video frame images, on the basis of the video frame image and the displacement of pixel points in the predicted intermediate frame image, determining second optical flow information between the video frame image and the predicted intermediate frame image (206); for each first pixel point in the predicted intermediate frame image, acquiring, from the second optical flow information, a position offset corresponding to the first pixel point, and on the basis of the position offset, selecting an image block corresponding to the first pixel point from the video frame image (208); on the basis of an image block corresponding to each first pixel point in the predicted intermediate frame image, updating a pixel value at each first pixel point in the predicted intermediate frame image to obtain an updated intermediate frame image corresponding to the video frame image (210); and fusing the updated intermediate frame images corresponding to the two video frame images respectively to obtain a fused intermediate frame image (212).

Embodiments of the present application provide a palmprint image generation method and apparatus, a device, a storage medium, and a program product. The method comprises: determining a plurality of control points on the basis of a predetermined palmprint curve template, and generating a Bézier curve on the basis of the plurality of control points; on the basis of the Bézier curve, generating a palm crease energy map having crease information, the palm crease energy map comprising a palm crease having a crease distribution same as that of the Bézier curve and a crease type different from that of the Bézier curve, wherein the crease type is used for describing the crease information and corresponds to crease direction energy of each pixel on the palm crease; and on the basis of the palm crease energy map, generating a photorealistic palmprint image having detailed texture information .

Provided in the embodiments of the present application are a three-dimensional scene model generation method and a related apparatus. The embodiments of the present application can be applied to various scenarios such as cloud technology, artificial intelligence, virtual reality, and games. The method provided in the embodiments of the present application comprises: partitioning a two-dimensional scene graph obtaining M pieces of two-dimensional scene information by means of an encoding network and on the basis of two-dimensional scene information corresponding to M two-dimensional scene sub-graphs which are obtained by means of partitioning; by means of a diffusion sub-model, converting the M pieces of two-dimensional scene information into corresponding M three-dimensional scene sub-blocks; and then merging the three-dimensional scene sub-blocks to generate a three-dimensional scene. By means of performing partitioning on a two-dimensional scene graph of a large scene so as to generate corresponding two-dimensional scene sub-graphs, and merging three-dimensional scene sub-blocks to generate a three-dimensional scene of a large-scale scene corresponding to the two-dimensional scene graph of the large scene, the inconsistency of overlapping parts of the sub-blocks is eliminated, thus ensuring the accuracy of a three-dimensional training scene.

The present application relates to the technical field of image processing, and discloses a picture processing method and apparatus, a device, and a storage medium. The method comprises: generating conditional embedding information on the basis of an original portrait picture and description text; on the basis of the original portrait picture, the conditional embedding information, and a facial mask map, generating a cross attention map corresponding to the original portrait picture; and on the basis of the conditional embedding information and the cross attention map, performing editing to obtain a processed portrait picture corresponding to the original portrait picture. The method can be applied to various scenarios such as cloud technology, artificial intelligence, intelligent transportation, and assisted driving. According to the method, a facial feature of a person in the original portrait picture and a person expression feature in the description text are fused, thereby ensuring a high similarity between the person in the generated processed portrait picture and the person in the original portrait picture and significantly improving the person fidelity in an editing process.

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