776 results about "Noise (video)" patented technology

The invention discloses a gesture action video generation method and device based on fine-grained semantic description, and the method comprises the steps: carrying out the preprocessing and data enhancement of an original video, and constructing sample data composed of a video frame and a fine-grained semantic description text used for describing a gesture action in the video frame; constructing a training architecture consisting of a video coding module, a text coding module, a noise reduction generation module and a video decoding module; a loss function is constructed based on the difference between added noise in the video coding module and de-noised noise in the de-noising generation module, only parameters of the de-noising generation module are optimized by using the loss function, and a video generation model is formed by the text coding module, the de-noising generation module and the video decoding module after the parameters are optimized; and generating a gesture action video based on the input random vector and the fine-grained semantic description text for describing the gesture action by utilizing a video generation model. In this way, a high-quality general digital human video containing gesture actions is generated based on the fine-grained semantic description text.

The invention discloses a panoramic video frame insertion method based on a potential diffusion model. The method comprises the following steps: compressing an input image to a potential space by using a panoramic perception vector quantization variational auto-encoder to obtain potential features; constructing initial noise, and fusing the motion features extracted by the panoramic optical flow adapter to obtain condition information; performing iterative denoising operation on the potential features of the intermediate frame to obtain final potential features of the intermediate frame; and restoring the final potential features of the intermediate frame into a frame insertion image of a pixel space through a condition decoder. According to the panoramic video frame interpolation method provided by the invention, the potential diffusion model and the panoramic characteristic enhancement technology are combined, so that the perception quality of a frame interpolation result can be remarkably improved, details and complex textures of a pole region can be better reserved, and a high-quality time interpolation solution is provided for immersive panoramic video application.

The invention relates to the technical field of artificial intelligence and the field of medical health, and discloses a video generation method based on multi-modal information fusion, which comprises the following steps: inputting image information and text information, and generating image feature representation and text feature representation; injecting noise into the image feature representation to generate a noisy potential representation; inputting the noisy potential representation into a denoising model to generate an optimized potential representation; inputting the text feature representation into a denoising model, and carrying out semantic fusion on the text feature representation and the optimized potential representation to generate a semantic potential representation; and inputting the semantic potential representation into a decoder, generating a video frame sequence and outputting the video frame sequence. According to the invention, through the multi-modal fusion of the image feature representation and the text feature representation, the generated video is ensured to be consistent with the input content semantically; through noise injection and denoising optimization, the diversity and detail quality of video generation are enhanced; and a video frame sequence is generated through the decoder, so that the visual coherence and natural transition of the video are ensured.

The invention relates to the technical field of data security, in particular to an unstructured video data automatic classification method, which comprises the following steps: S1, video data preprocessing: standardizing video data to a uniform format and size, and carrying out noise processing; s2, video data feature extraction and multi-mode feature fusion: feature extraction is performed on the preprocessed video data from three modes of audio, video and text, time sequence processing is performed, and multi-mode feature fusion is performed; s3, video data same-category matching: classifying the fused multi-modal features to complete video classification; according to the invention, multi-modal information in a video can be captured more accurately, and automatic processing of video data is realized; high processing efficiency and classification accuracy are kept in large-scale data, meanwhile, the degree of automation is improved, the necessity of manual participation is reduced, and the overall process is optimized.

The present invention provides a video generation method, comprising: encoding an image comprising a reference figure to obtain a feature representation of the reference figure; encoding a video comprising a reference action to obtain a feature representation of the reference action; injecting the feature representation of the reference figure and the feature representation of the reference action into a latent diffusion network in a video latent diffusion model by means of at least one timing hold module in a trained video latent diffusion model, wherein the latent diffusion network comprises a plurality of cross-attention and denoising units, one timing hold module is connected in series between at least two cross-attention and denoising units, and each timing hold module comprises a time converter and a space converter; using the latent diffusion network to denoise an inputted noise sequence to obtain a denoised latent vector sequence; and decoding the latent vector sequence to obtain an action video of a target object. The present invention further provides a video generation apparatus, an electronic device, a storage medium, and a program product.

The invention provides an image quality enhancement method and system based on a cloud game, and the method comprises the following steps: firstly, a cloud end renders a game picture and generates an initial video stream; then, performing preliminary optimization on the video stream, and reducing jumping and frame loss through motion compensation of an inter-frame motion vector; the optimized video stream is transmitted to a user side, after the user side receives the video stream, image quality enhancement processing is carried out, specifically, denoising processing is carried out, and random noise and compression noise are eliminated by combining an inter-frame difference method and spatial domain bilateral filtering; sharpening enhancement: enhancing image edges and texture details by using a Laplacian operator; artifact removal: removing compression artifacts through frequency domain filtering; the dynamic contrast is enhanced, and gray level distribution is adjusted through histogram equalization; finally, the video stream after enhancement processing is presented to a user in real time, the image quality of the cloud game can be remarkably improved by gradually updating the processing result of each pixel point, the video stream is presented in real time in a high-quality mode, and the user experience is optimized.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a video generation method, device, equipment and medium based on action coherence, the method comprises the following steps: obtaining a video action material set, carrying out key frame analysis on the video action material set to obtain a key video frame sequence, extracting an inter-frame residual vector between consecutive frames in the key video frame sequence, adjusting a preset initial diffusion model by using the inter-frame residual vector to obtain an optimized diffusion model, performing cosine scaling on the inter-frame residual vector by using the optimized diffusion model to obtain a scaled residual vector, and obtaining a video adjustment text, and carrying out noise addition and splicing on the key video frame sequence by using the video adjustment text and the zoom residual vector to obtain a noise video, and carrying out noise reduction on the noise video to obtain a target action video. According to the method and the device, the action coherence in the customized generated video can be effectively improved.

The invention relates to the technical field of computers, and relates to a video generation method and device, a computer program product and electronic equipment. The video generation method comprises the following steps: obtaining potential spatial features of a current video frame, wherein the current video frame is obtained by denoising an initial noise image through a diffusion model; decision parameters of a preset decision model are adjusted based on the potential spatial features of the current video frame, the preset decision model is used for generating diffusion parameters, and the diffusion parameters are used for controlling the denoising process of the diffusion model; and based on diffusion parameters generated by the preset decision model after the decision parameters are adjusted, performing denoising processing on the potential spatial features of the current video frame through the diffusion model, and generating a next video frame. The quality of the generated video can be improved.

Embodiments of the present application provide a video generation method and apparatus, a device, and a medium. The method can be applied to the technical field of video content generation, and is used for improving the video generation quality. The method comprises: acquiring a sample video frame sequence from a sample video, determining a first step count and sample original noise, and performing data noise addition processing on the sample video frame sequence to obtain video input data; inputting the video input data, a sample text encoded feature corresponding to sample description text, and first embedding information corresponding to the first step count into an initial generation model; and performing noise prediction on the sample video frame sequence by means of M spatiotemporal residual components and M spatiotemporal attention components in the initial generation model to obtain sample predicted noise, correcting a network parameter in the initial generation model on the basis of the sample original noise and the sample predicted noise, and determining the initial generation model comprising the corrected network parameter as a video generation model.

The invention relates to the technical field of biomedical engineering and computer vision, in particular to a non-contact physiological signal extraction method and system based on frequency self-adaption and illumination noise perception.The method comprises the following steps of multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, and non-contact physiological signal extraction. Frequency adaptive gating and frequency domain feature enhancement, depth time attention feature re-calibration, physiological signal regression and closed loop optimization; the method has the beneficial effects that a lightweight end-to-end deep learning network architecture is constructed by systematically fusing three core modules of illumination-noise perception mask, frequency adaptive gating and depth time attention, and the defects that a traditional physical model depends on artificial prior and is poor in anti-interference performance and high in reliability are overcome. And the one-sidedness caused by high calculation complexity and difficulty in distinguishing the signal and noise of the existing deep learning model is avoided, and the weak physiological signal can be recovered from the face video more accurately and robustly.

A processor-implemented method performed for text-based video editing includes receiving a video input and a text prompt. The video input includes a sequence of video frames. Features of the video input are extracted to generate a latent representation of the video input. Noise is injected to the latent representation of the video input to generate a noise injected latent. The noise is conditioned on the video input. An artificial neural network (ANN) model processes the noise injected latent based on the text prompt to adapt the video input according to the text prompt.

The invention is suitable for the field of digital watermarking, and provides an anti-compression coding robust video watermark generation method based on an adversarial neural network, and the method comprises the steps: constructing an MSCA-GAN model; the model comprises an encoder, a decoder, a distortion layer, a discriminator network and an opponent network, and the specific steps are as follows: step S1, the encoder extracts different scale features of a video frame by using a multi-scale convolution attention mechanism, calculates attention weights, efficiently hides and embeds binary watermark information into the video, generates a watermark-containing video, and transmits the watermark-containing video to the decoder; performing confrontation optimization on an embedding strategy with a discriminator in training; according to the method, for H.264 compression layer special training, the multi-scale convolution attention mechanism and the depth separable convolution are combined, and the anti-compression robustness of the watermark under the H.264 standard is effectively improved; through common attack training such as noise layer simulation cutting and zooming, the watermark can still keep high extraction accuracy and robustness in a complex environment.

The invention discloses a multi-modal fusion green port digital management and control system and method, and relates to the technical field of port management and control, and the method comprises the following steps: collecting the signal-to-noise ratio data of a radio channel of a video link, and generating a port area electromagnetic interference thermodynamic diagram through spatial interpolation; and dynamically adjusting a synchronous clock of each camera according to the electromagnetic interference thermodynamic diagram, mapping the frame triggering offset into a pixel compensation matrix, and performing sub-pixel-level space-time correction on the image frame to obtain a corrected video frame sequence. According to the method, target credibility judgment and path optimization under multi-modal perception are realized through construction of an interference thermodynamic diagram, space-time correction, artifact recognition and point cloud fusion, an interference model and a recognition threshold are dynamically regulated and controlled through closed-loop feedback, and an end-to-end self-adaptive port digital management and control method is constructed. The artifact identification accuracy and scheduling stability are significantly improved, and the anti-interference and green efficient operation capabilities of the port management and control system are enhanced.

The invention discloses a deeply-forged face video frame-level positioning method and system based on weak supervised learning, and the method comprises the steps: firstly constructing a training set with a video as a unit, carrying out the data enhancement of a frame-level sample, and generating an enhanced view pair; secondly, splicing the enhanced view pair and inputting the spliced enhanced view pair into a depth forgery detection model to obtain and generate fusion enhanced frame-level features; then, intra-class contrast learning loss, time sequence consistency constraint loss and frame weight loss are constructed, and a deep forgery detection model is trained based on fusion-enhanced frame-level feature joint optimization. And finally, inputting a video to be detected into the deep counterfeiting detection model to output the frame-level confidence, judging whether the video is a forged video or not, and realizing frame-level counterfeiting positioning. According to the method, video-level detection and frame-level positioning are effectively realized, meanwhile, the influence of label noise in part of forged videos is relieved, and the generalization and robustness of the system are improved.

Provided are systems and methods for artificial intelligence (AI)-driven smooth video-to-video generation. An example method includes receiving a first video including first frames; acquiring a text including instructions for transforming the first video; encoding the text into text embeddings corresponding to the first frames; encoding the first frames into image latents; generating initial noise vectors and adding the initial noise vectors to the image latents to obtain noisy image latents; providing the text to a pretrained motion model to generate animation parameters corresponding to the first frames; providing the noisy image latents, the text embeddings, and the animation parameters to a neural network to generate second noise vectors for the image latents; removing the second noise vectors from the noisy image latents to obtain denoised image latents; and decoding the denoised image latents into second frames of a second video.

The invention discloses a robust video watermarking method and system based on frame domain attack training. According to the invention, a model training framework composed of an encoder module, a noise attack module, a generative adversarial module and a decoder module is constructed; and taking minimization of the total loss function as a target, training the model training framework by using training data, and introducing a plurality of noise attacks and watermark removal attacks in the training process to improve the robustness of the model. In practical application, the trained encoder module is utilized to add watermark information to a target video, and the trained decoder module is utilized to extract the watermark information from the watermark video. According to the method, the noise attack and the watermark removal attack are introduced in the training process, so that the stability and the extraction precision of the watermark in a complex attack environment can be improved.

The invention discloses a diffusion model self-guiding-based image or video generation method and system. The method comprises the steps that the input content is a noise potential variable and a future offset step length of a current time step; the diffusion model predicts the score network twice based on the input content to obtain a conditional score network of the current time step and the future offset time step; carrying out linear combination on the two conditional score networks to generate a combined conditional score network; denoising the noise potential variable of the current time step by using a combined condition score network; calculating and updating a potential variable of the previous time step according to a denoising result; in each time step, the self-guiding strategy is adopted to carry out denoising processing on the noise, and noise prediction is optimized; and after de-noising processing of all time steps, a de-noising potential variable is finally output as a final model generation result, and an image or a video is obtained. According to the invention, the quality of the generated image or video can be obviously improved.

The invention relates to a video restoration method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an original video, extracting first edge feature information of the original video, acquiring a video to be restored, encoding a first reference video frame selected from the video to be restored to obtain a first encoding feature, adding noise to the first encoding feature to obtain a first reference video frame feature, acquiring a random noise signal, and outputting the random noise signal to the video to be restored. Fusing the first reference video frame feature and the random noise signal to obtain a noise-added noise feature, obtaining first mask information indicating a to-be-repaired area in the to-be-repaired video, defining a noise reduction control condition based on the first mask information and the first edge feature information, and performing noise reduction on the noise-added noise feature according to the noise reduction control condition to obtain a to-be-repaired video. And carrying out noise reduction on the video features to obtain the denoised video features, and decoding the denoised video features to obtain a restored video. By adopting the method, the video restoration effect can be improved.

The invention relates to the technical field of industrial vision and intelligent monitoring, and discloses a loom control system operation state monitoring and fault diagnosis method based on machine vision, which comprises the following steps: acquiring a video stream in a loom shed area and constructing a two-dimensional space-time slice tensor; performing global motion compensation processing on the space-time slice tensor by using a homography transformation matrix, mapping a compensated dynamic texture feature sequence to a three-dimensional phase space by using a time delay embedding algorithm, and reconstructing a closed phase space trajectory representing periodic operation logic of the loom; the discrete Frechet distance between the phase space trajectory of the current operation cycle and the preset reference trajectory is calculated, and a control instruction is generated. The health degree of the sequential logic of the system is directly quantified on the premise that specific components are not recognized by using the invariant characteristic of the phase space manifold topology; the technical problems that small phase lag is difficult to perceive and nonlinear faults cannot be early warned in a strong noise environment are solved.

The present disclosure relates to a video noise detection method and apparatus, and a device and a medium. The video noise detection method includes: extracting a first video frame and a second video frame from a target video, wherein the first video frame and the second video frame are adjacent video frames; performing differential processing on the first video frame and the second video frame, so as to obtain an inter-frame differential image between the first video frame and the second video frame; performing flat area detection on the first video frame and the second video frame, to obtain an intersection of flat areas in the first video frame and the second video frame; and calculating a time-domain noise value corresponding to the first video frame by using pixel information of the intersection of the flat areas in the inter-frame differential image.

An adaptive object recognition system utilizes relevance-based weighting and scoring that may assist with accuracy and stability. The system receives multiple recognition results for objects across video frames. Weights are assigned to results based on their stability over frames and the relevance scoring of neighboring areas. This allows more confidence to be placed in consistent detections and clearer image regions. The system decouples high and low accuracy areas to apply appropriate thresholding. Temporal analysis is used to favor frequently detected objects. Weighted results are combined to generate final recognitions that are more robust to non-uniform image quality and noise.

The invention provides a video editing model based on text and image common editing and a construction method thereof. The video editing model introduces an optical flow guide mask fusion module and a multi-modal feature recognition and segmentation module into a denoising diffusion implicit model; the method comprises the following steps: inputting an original video into a denoising diffusion implicit model to carry out forward diffusion noise addition to obtain a multi-frame submerged space generation frame, and inputting the multi-frame submerged space generation frame into an optical flow guide mask fusion module to carry out inter-frame feature alignment to obtain a time consistency submerged space generation frame; a text prompt, an image prompt and an original video are input into a multi-modal feature recognition and segmentation module to be aligned and positioned to obtain a condition vector, the condition vector and a time consistency submerged space generation frame are subjected to iterative denoising to generate a target editing video, and dynamic feature modulation is performed in each denoising process; a video editing model based on text and image common editing efficiently edits a video under combined guidance of text and image prompts.

The invention relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention discloses an encrypted video identification method in a Tor environment. The method comprises the four steps of collecting flow, extracting an AU-burst length sequence, extracting upstream features and classifying downstream tasks. Firstly, real-time Tor traffic is captured at a network information service center of a local area network entrance, then features are extracted from the real-time Tor traffic by using a corresponding TREFS i T segmentation strategy according to Tor video traffic characteristics to obtain an encrypted ADU-burst length sequence, then noise filtering and secondary feature extraction are performed by using a 1DCNN model, and finally classification is performed by using a random forest. According to the method for identifying the encrypted video in the Tor complex environment, the encrypted video does not need to be decrypted, the video played by the client through the Tor network can be identified through the video traffic characteristics so as to supervise harmful videos, and the method has wide application scenes and good supervision effects.

Embodiments of the present disclosure provide techniques for restoring video content. An example method generally includes receiving a set of input video frames that include artifacts, generating one or more conditioning features based on the set of video frames, wherein the conditioning features represent content information included in the set of video frames while reducing representation of the artifacts, denoising, using a latent diffusion model and based on the conditioning features, a representation of the set of input video frames that includes noise, and generating a set of output frames based on the denoised representation, wherein the set of output video frames include fewer artifacts relative to the set of input video frames.

Methods for motion-controllable video diffusion include extracting optical flow fields from an input video and computing warped noise by iteratively warping noise between consecutive frames using the optical flow fields. The iteratively warping includes (i) re-Gaussianizing expanded pixel regions by sampling fresh Gaussian noise, and (ii) aggregating contracted pixel regions by merging noise particles and renormalizing variance to preserve spatial Gaussianity. An output video is generated by initializing a diffusion process with the warped noise and iteratively denoising to produce temporally coherent output frames. Various other methods, systems, and computer-readable media are also disclosed.

The invention provides a cross-modal semantic attention collaborative enhancement video subtitle generation method and system, and belongs to the field of video subtitle generation. The method aims at solving the problems that an existing video description model mostly stays in first-order relation modeling on the attention mechanism level, high-order semantic dependence is difficult to capture, and noise is easy to introduce in multi-modal feature fusion. The invention provides a cross-modal semantic attention collaborative enhancement module, the module comprises two key components of context semantic modulation of attention enhancement and cross-modal structure alignment, and the fine modeling ability of the generative model for visual and text semantics is effectively improved by dynamically modulating attention weight and optimizing a modal alignment structure. And integration is carried out based on a non-autoregression coarse-to-fine video description model. Experimental results show that the method can significantly improve the accuracy and diversity of video description generation on the premise of keeping the model scale and the calculation overhead basically unchanged.

The invention discloses a real-time drowning detection method and system based on multi-module integration, and belongs to the technical field of computer vision and artificial intelligence. Aiming at the problems that a traditional drowning detection method is sensitive in environment interference, insufficient in real-time performance, low in small target detection precision and the like, an innovative scheme is provided through multi-technology collaborative optimization: a lightweight backbone network is constructed by adopting DWConv, so that the calculation complexity is reduced; a dynamic receptive field attention module is designed, and noise interference is suppressed by means of RFAConv; an SEAM module is fused to enhance sheltered target feature response; biFPN is integrated, and the robustness of long-distance and small target detection is improved. Experiments show that the mAP at 0.5 of the improved DRSB-YOLO model reaches 86.253%, the model volume is 4.39 MB, the reasoning speed of a Windows platform is 56.1 + / -5.1 FPS, and the real-time processing of 1080P video streams is supported. The system supports cross-scene adaptive switching and local / cloud dual-mode data transmission, can be widely applied to the fields of intelligent lifesaving and the like, and improves the accuracy and timeliness of drowning early warning.

The invention discloses a video user data security analysis and prediction method based on privacy calculation, and the method comprises the steps: S1, collecting video user data, and constructing a data set; s2, performing privacy budget distribution on different processing stages of the video user data through multi-level privacy budget division; s3, defining an adjustment formula of a noise injection amount by adopting a self-adaptive noise injection mechanism, and obtaining preprocessed video user data; s4, constructing a privacy calculation framework, and combining the balance objective function and the graph reconstruction loss function to set a loss function; s5, training a privacy computing framework by using the preprocessed video user data; and S6, executing video user data analysis and prediction through a privacy computing framework, and outputting a prediction result. According to the method, an efficient and scientific optimization scheme can be provided in video user data security analysis and prediction, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses a video generation method and device, a medium, equipment and a computer program product. The method comprises the following steps: acquiring a control image, a depth map corresponding to the control image and target camera track information used for video generation; and according to the control image, the depth map, the target camera track information and a video generation model, performing de-noising processing on noise image features, and determining a target video corresponding to the control image and target video depth information corresponding to the target video. Therefore, in the process of performing video generation through the image, video generation can be performed in combination with the camera track information and the depth information for controlling the image, so that the target video and the video depth information thereof under the control of the camera track information are obtained, and a 3D scene can be obtained through direct rendering based on the target video and the video depth information. Therefore, the matching degree between the target video and the camera track information can be improved, and the 3D scene can be quickly obtained without complex processes and additional operations.