Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

Abstract: The present invention concerns an apparatus configured to partition a picture into leaf blocks using recursive multi-tree partitioning, block-based encode the picture into a data stream using the partitioning of the picture into the leaf blocks, wherein the apparatus is configured to, in partitioning the picture into the leaf blocks, for a predetermined block which extends beyond a boundary of the picture, reduce an available set of split modes depending on a position at which the boundary of the picture crosses the predetermined block in order to obtain a reduced set of one or more split modes, wherein the apparatus is configured to signal a selected split mode in the data stream.

Abstract: A video encoder is disclosed that is configured to encode a slice of a current picture into a data stream by partitioning the slice into rows of Coding Tree Units (CTUs), select a CTU dependency offset for the slice out of a plurality of CTU dependency offsets, the CTU dependency offset indicating a minimum CTU offset between consecutive CTU rows to be obeyed by a decoder when decoding CTU rows in parallel, and signal the CTU dependency offset in the data stream. The CTU dependency offset is selected based on a slice type of the slice.

Abstract: A proposed intermediate way of handling the renderable portion of the first view results in more efficient coding. Instead of omitting the coding of the renderable portion completely, even more efficient coding of multi-view signals entails merely suppressing the coding of the residual signal within the renderable portion, whereas the prediction parameter coding still takes place from the non-renderable portion of the multi-view signal across the renderable portion so that prediction parameters for the renderable portion may be exploited for predicting parameters for the non-renderable portion. The additional coding rate for transmitting the prediction parameters for the renderable portion may be kept low as this merely aims at forming a continuation of the parameter history across the renderable portion to serve as a basis for prediction parameters of other portions of the multi-view signal.

Abstract: A video decoder for decoding an encoded video signal including encoded picture data and indication data of a picture of a video to reconstruct the picture of the video is provided. The video decoder includes an interface configured for receiving the encoded video signal, and a data decoder configured for reconstructing the picture of the video by decoding the encoded picture data using the indication data. The picture is partitioned into a plurality of coding areas. One or more coding areas of the plurality of coding areas include two or more coding tree units of the plurality of coding tree units, wherein each coding area of the one or more coding areas which includes two or more coding tree units exhibits a coding order for the two or more coding tree units of the coding area.

Abstract: The present invention concerns an apparatus configured to partition a picture into leaf blocks using recursive multi-tree partitioning, block-based encode the picture into a data stream using the partitioning of the picture into the leaf blocks, wherein the apparatus is configured to, in partitioning the picture into the leaf blocks, for a predetermined block which extends beyond a boundary of the picture, reduce an available set of split modes depending on a position at which the boundary of the picture crosses the predetermined block in order to obtain a reduced set of one or more split modes, wherein the apparatus is configured to signal a selected split mode in the data stream.

Abstract: Semantic fill techniques are described that support generating fill and editing images from semantic inputs. A user input, for example, is received by a semantic fill system that indicates a selection of a first region of a digital image and a corresponding semantic label. The user input is utilized by the semantic fill system to generate a guidance attention map of the digital image. The semantic fill system leverages the guidance attention map to generate a sparse attention map of a second region of the digital image. A semantic fill of pixels is generated for the first region based on the semantic label and the sparse attention map. The edited digital image is displayed in a user interface.

Abstract: Concepts are described which enable improved suitability of a respective codec for parallel processing at encoder and/or decoder.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for combining digital images. In particular, in one or more embodiments, the disclosed systems combine latent codes of a source digital image and a target digital image utilizing a blending network to determine a combined latent encoding and generate a combined digital image from the combined latent encoding utilizing a generative neural network. In some embodiments, the disclosed systems determine an intersection face mask between the source digital image and the combined digital image utilizing a face segmentation network and combine the source digital image and the combined digital image utilizing the intersection face mask to generate a blended digital image.

Abstract: Apparatus (54) for decoding a predetermined block (18) of a picture using intra-prediction, configured to read, from a data stream (12), a mode index (200) using a binarization code (202). The mode index (200) points to one out of a list (204) of matrix-based intra-prediction modes. The apparatus is configured to predict samples (108) of the predetermined block (18) by computing a matrix-vector product (206) between an input vector (102) derived from reference samples (17) in a neighbourhood of the predetermined block (18) and a prediction matrix (19) associated with the matrix-based intra-prediction mode (k) pointed to by the mode index (200) and associating components (210) of an output vector (208) obtained by the matrix-vector product (206) onto sample positions (104) of the predetermined block. The list (204) of matrix-based intra-prediction modes consists of an even number of matrix-based intra-prediction modes.

Abstract: A video decoder for decoding an encoded video signal including encoded picture data and indication data of a picture of a video to reconstruct the picture of the video is provided. The video decoder includes an interface configured for receiving the encoded video signal, and a data decoder configured for reconstructing the picture of the video by decoding the encoded picture data using the indication data. The picture is partitioned into a plurality of coding areas. One or more coding areas of the plurality of coding areas include two or more coding tree units of the plurality of coding tree units, wherein each coding area of the one or more coding areas which includes two or more coding tree units exhibits a coding order for the two or more coding tree units of the coding area.

Abstract: Techniques for block wise encoding and decoding may be applied to encoders, decoders, and methods for encoding or decoding. In one example, there is provided a technique to decode or encode a predetermined block of the picture by assigning, based on a first signalization in the data stream, the predetermined block to a first set or a second set; sorting the assigned set of intra-prediction modes according to intra-prediction modes used for neighboring blocks, neighboring the predetermined block, to obtain a list of intra prediction modes; deriving, for the predetermined block, from the data stream, an index into the list of intra prediction modes; and predicting the predetermined block using an intra prediction mode onto which the index points.

Abstract: Video codec for supporting temporal inter-prediction, configured to perform padding of an area of a referenced portion of a reference picture which extends beyond a border of the reference picture, which referenced portion is referenced by an inter predicted block of a current picture by selecting one of a plurality of intra-prediction modes, and padding the area using the selected intra-prediction mode.

Abstract: A subblock-based coding of transform coefficient blocks of the enhancement layer is rendered more efficient. To this end, the subblock subdivision of the respective transform coefficient block is controlled on the basis of the base layer residual signal or the base layer signal. In particular, by exploiting the respective base layer hint, the subblocks may be made longer along a spatial frequency axis transverse to edge extensions observable from the base layer residual signal or the base layer signal.

Abstract: The coding efficiency of scalable video coding is increased by substituting missing spatial intra prediction parameter candidates in a spatial neighborhood of a current block of the enhancement layer by use of intra prediction parameters of a co-located block of the base layer signal. By this measure, the coding efficiency for coding the spatial intra prediction parameters is increased due to the improved prediction quality of the set of intra prediction parameters of the enhancement layer, or, more precisely stated, the increased likelihood, that appropriate predictors for the intra prediction parameters for an intra predicted block of the enhancement layer are available thereby increasing the likelihood that the signaling of the intra prediction parameter of the respective enhancement layer block may be performed, on average, with less bits.

Abstract: Video codec for supporting temporal inter-prediction, configured to perform padding of an area of a referenced portion of a reference picture which extends beyond a border of the reference picture, which referenced portion is referenced by an inter predicted block of a current picture by selecting one of a plurality of intra-prediction modes, and padding the area using the selected intra-prediction mode.

Abstract: Block based decoder configured to partition a picture (10) of more than one color component (101, 102) and of a color sampling format, according to which each color component (101, 102) is equally sampled, into blocks using a partitioning scheme according to which the picture (10) is equally partitioned with respect to each color component (101, 102). Additionally, the block based decoder is configured to decode a first color component (101) of the picture (10) in units of the blocks with selecting, for each of intra-predicted first color component blocks (18?11-18?1n) of the picture (10), one out of a first set (508) of intra-prediction modes.