A manufacturing flow of apparel such as jeans uses a laser to finish the products. The products are designed using a digital design tool, where photorealistic previews are generated in three dimensions and two dimensions. Imagery of the products are sent to retailers where customers can order the products, such as online orders. Imagery of the products are sent to factories where the products are finished. Based on the imagery, the factories make adjustments to the processes as needed so that the actual products will have an appearance as in the received imagery. As orders are received by the retailers, the factories can manufacture the desired products on demand, and the products can be delivered to customers.

A manufacturing flow of apparel such as jeans uses a laser to finish the products. The products are designed using a digital design tool, where photorealistic previews are generated in three dimensions and two dimensions. Imagery of the products are sent to retailers where customers can order the products, such as online orders. Imagery of the products are sent to factories where the products are finished. Based on the imagery, the factories make adjustments to the processes as needed so that the actual products will have an appearance as in the received imagery. As orders are received by the retailers, the factories can manufacture the desired products on demand, and the products can be delivered to customers.

A method and decoding unit for decoding a compressed data structure that encodes a set of Haar coefficients for a 2×2 quad of pixels of a block of pixels. The set of Haar coefficients comprises a plurality of differential coefficients and an average coefficient. A first portion of the compressed data structure encodes the differential coefficients for the 2×2 quad of pixels. A second portion of the compressed data structure encodes the average coefficient for the 2×2 quad of pixels. The first portion of the compressed data structure is used to determine signs and exponents differential coefficients which are non-zero. The second portion of the compressed data structure is used to determine a representation of the average coefficient. The result of a weighted sum of the differential coefficients and the average coefficient for the 2×2 quad of pixels is determined using: (i) the determined signs and exponents for the differential coefficients which are non-zero, (ii) the determined representation of the average coefficient, and (iii) respective weights for the differential coefficients. The determined result is used to determine the decoded value. The determined decoded value is outputted.

A method and decoding unit for decoding a compressed data structure that encodes a set of Haar coefficients for a 2×2 quad of pixels of a block of pixels. The set of Haar coefficients comprises a plurality of differential coefficients and an average coefficient. A first portion of the compressed data structure encodes the differential coefficients for the 2×2 quad of pixels. A second portion of the compressed data structure encodes the average coefficient for the 2×2 quad of pixels. The first portion of the compressed data structure is used to determine signs and exponents differential coefficients which are non-zero. The second portion of the compressed data structure is used to determine a representation of the average coefficient. The result of a weighted sum of the differential coefficients and the average coefficient for the 2×2 quad of pixels is determined using: (i) the determined signs and exponents for the differential coefficients which are non-zero, (ii) the determined representation of the average coefficient, and (iii) respective weights for the differential coefficients. The determined result is used to determine the decoded value. The determined decoded value is outputted.

Synthesis of an image of a view from data of a multi-view video. The synthesis includes an image processing phase as follows: generating image synthesis data from texture data of at least one image of a view of the multi-view video; calculating an image of a synthesised view from the generated synthesis data and at least one image of a view of the multi-view video; analysing the image of the synthesised view relative to a synthesis performance criterion; if the criterion is met, delivering the image of the synthesised view; and if not, iterating the processing phase. The calculation of an image of a synthesised view at a current iteration includes modifying, based on synthesis data generated in the current iteration, an image of the synthesised view calculated during a processing phase preceding the current iteration.

Synthesis of an image of a view from data of a multi-view video. The synthesis includes an image processing phase as follows: generating image synthesis data from texture data of at least one image of a view of the multi-view video; calculating an image of a synthesised view from the generated synthesis data and at least one image of a view of the multi-view video; analysing the image of the synthesised view relative to a synthesis performance criterion; if the criterion is met, delivering the image of the synthesised view; and if not, iterating the processing phase. The calculation of an image of a synthesised view at a current iteration includes modifying, based on synthesis data generated in the current iteration, an image of the synthesised view calculated during a processing phase preceding the current iteration.

This application provides a method for reconstructing a three-dimensional model, a method for training a three-dimensional reconstruction model, an apparatus, a computer device, and a storage medium. The method for reconstructing a three-dimensional model includes: obtaining an image feature coefficient of an input image; respectively obtaining, according to the image feature coefficient, a global feature map and an initial local feature map based on a texture and a shape of the input image; performing edge smoothing on the initial local feature map, to obtain a target local feature map; respectively splicing the global feature map and the target local feature map based on the texture and the shape, to obtain a target texture image and a target shape image; and performing three-dimensional model reconstruction according to the target texture image and the target shape image, to obtain a target three-dimensional model.

This application provides a method for reconstructing a three-dimensional model, a method for training a three-dimensional reconstruction model, an apparatus, a computer device, and a storage medium. The method for reconstructing a three-dimensional model includes: obtaining an image feature coefficient of an input image; respectively obtaining, according to the image feature coefficient, a global feature map and an initial local feature map based on a texture and a shape of the input image; performing edge smoothing on the initial local feature map, to obtain a target local feature map; respectively splicing the global feature map and the target local feature map based on the texture and the shape, to obtain a target texture image and a target shape image; and performing three-dimensional model reconstruction according to the target texture image and the target shape image, to obtain a target three-dimensional model.

A method comprising: providing a 3D representation of at least one object as an input for an encoder (500); projecting the 3D representation onto at least one 2D patch (502); generating at least a geometry image and a texture image from the 2D patch (504); generating, based on the geometry image, a mesh comprising a number of vertices (506); mapping the number of vertices to two- dimensional (2D) coordinates of the texture image (508); and signalling said 2D coordinates of the texture image to be applied to the number of vertices of the mesh in or along a bitstream (510).

A method comprising: providing a 3D representation of at least one object as an input for an encoder (500); projecting the 3D representation onto at least one 2D patch (502); generating at least a geometry image and a texture image from the 2D patch (504); generating, based on the geometry image, a mesh comprising a number of vertices (506); mapping the number of vertices to two- dimensional (2D) coordinates of the texture image (508); and signalling said 2D coordinates of the texture image to be applied to the number of vertices of the mesh in or along a bitstream (510).