Lemon Inc.

Bytedance Inc.

Beijing Bytedance Network Technology Co., Ltd.

Beijing Zitiao Network Technology Co., Ltd.

Jing Liu

Linjie Luo

Chunpong Lai

Shen Sang

Xu Wang

Guoxian Song

Jingna Sun

Weihong Zeng

Jianchao Yang

Guohui Wang

Xiao Yang

Tiancheng Zhi

Zhili Chen

Jingcong Zhang

Peibin Chen

Yunzhu Li

Xuchen Song

Wanchun Ma

Haoze Li

Ye Yuan

Xiaochen Lian

Jiashi Feng

Yuanlong Chen

Hongyi Xu

Beixin Hu

Jianfeng Zhang

Rushikesh Dudhat

Zihang Jiang

Yichun Shi

A positioning model optimization method, a positioning method, and a positioning device are provided. The positioning model optimization method includes: inputting a positioning model for a scene, the positioning model including a three-dimensional (3D) point cloud and a plurality of descriptors corresponding to each 3D point in the 3D point cloud; calculating a significance of each 3D point in the 3D point cloud, and if the significance is greater than a predetermined threshold, outputting the 3D point and the plurality of descriptors corresponding to the 3D point to an optimized positioning model for the scene; and outputting the optimized positioning model for the scene.

Positioning model optimization method, positioning method, and positioning device

An animation processing method and apparatus, an electronic device and a storage medium, applied to a shader, the method including: acquiring an animation sample of a model unit; acquiring an external interaction parameter of an augmented reality model, where the augmented reality model includes a plurality of model units; and outputting the augmented reality model, and driving the animation sample of the model unit in the output augmented reality model according to the external interaction parameter. The animation sample of the model unit can be driven according to the external interaction parameter, so that animation of the model can be adjusted according to user operation, thereby improving usability.

Animation processing method and apparatus, electronic device and storage medium

Technologies are described and recited herein for producing controllable synthesized images include a geometry guided 3D GAN framework for high-quality 3D head synthesis with full control on camera poses, facial expressions, head shape, articulated neck and jaw poses; and a semantic SDF (signed distance function) formulation that defines volumetric correspondence from observation space to canonical space, allowing full disentanglement of control parameters in 3D GAN training.

Geometry-guided controllable 3D head synthesis

An image effect implementing method is described herein that includes: acquiring a first image, recognizing a set object in the first image, and acquiring an augmented reality model corresponding to the set object; superimposing, according to coordinate information of pixels of the set object, the augmented reality model onto the first image to obtain a second image; and upon detection of a preset deformation event, controlling, according to a set deformation policy, at least one sub-model of the augmented reality model in the second image to deform, and displaying the deformed second image.

Image effect implementing method and apparatus, electronic device and storage medium

Three-dimensional (3D) avatars may be produced by stylizing a dataset of images based on a user-input text prompt input to a stable diffusion model, and using the output stylized dataset of images to train an efficient geometry-aware 3D generative adversarial network (EG3D) model.

Text-to-3D avatars

A method of generating a stylized 3D avatar is provided. The method includes receiving an input image of a user, generating, using a generative adversarial network (GAN) generator, a stylized image, based on the input image, and providing the stylized image to a first model to generate a first plurality of parameters. The first plurality of parameters include a discrete parameter and a continuous parameter. The method further includes providing the stylized image and the first plurality of parameters to a second model that is trained to generate an avatar image, receiving, from the second model, the avatar image, comparing the stylized image to the avatar image, based on a loss function, to determine an error, updating the first model to generate a second plurality of parameters that correspond to the first plurality of parameters, based on the error, and providing the second plurality of parameters as an output.

Cascaded domain bridging for image generation

A method of generating a style image is described. The method includes receiving an input image of a subject. The method further includes encoding the input image using a first encoder of a generative adversarial network (GAN) to obtain a first latent code. The method further includes decoding the first latent code using a first decoder of the GAN to obtain a normalized style image of the subject, wherein the GAN is trained using a loss function according to semantic regions of the input image and the normalized style image.

Systems and methods directed to controlling the similarity between stylized portraits and an original photo are described. In examples, an input image is received and encoded using a variational autoencoder to generate a latent vector. The latent vector may be blended with latent vectors that best represent a face in the original user portrait image. The resulting blended latent vector may be provided to a generative adversarial network (GAN) generator to generate a controlled stylized image. In examples, one or more layers of the stylized GAN generator may be swapped with one or more layers of the original GAN generator. Accordingly, a user can interactively determine how much stylization vs. personalization should be included in a resulting stylized portrait.

Portrait stylization framework to control the similarity between stylized portraits and original photo

Methods and systems for enlarging a stylized region of an image are disclosed that include receiving an input image, generating, using a first generative adversarial network (GAN) generator, a first stylized image, based on the input image, normalizing the input image, generating, using a second generative adversarial network (GAN) generator, a second stylized image, based on the normalized input image, blending the first stylized image and the second stylized image to obtain a third stylized image, and providing the third stylized image as an output.

Agilegan-based stylization method to enlarge a style region

Methods and systems for generating a texturized image are disclosed. Some examples may include: receiving an input image, receiving an exemplar texture image, generating, using an encoder, a first latent code vector representation based on the input image, generating, using a generative adversarial network generator, a second latent code vector representation based on the exemplar texture image, blending the first latent code vector representation and the second latent code vector representation to obtain a blended latent code vector representation, generating, by the GAN generator, a texturized image based on the blended latent code vector representation and providing the texturized image as an output image.

Agilegan-based refinement method and framework for consistent texture generation

Systems and methods for rendering a translucent object are provided. In one aspect, the system includes a processor coupled to a storage medium that stores instructions, which, upon execution by the processor, cause the processor to receive at least one mesh representing at least one translucent object. For each pixel to be rendered, the processor performs a rasterization-based differentiable rendering of the pixel to be rendered using the at least one mesh and determines a plurality of values for the pixel to be rendered based on the rasterization-based differentiable rendering. The rasterization-based differentiable rendering can include performing a probabilistic rasterization process along with aggregation techniques to compute the plurality of values for the pixel to be rendered. The plurality of values includes a set of color channel values and an opacity channel value. Once values are determined for all pixels, an image can be rendered.

Methods for a rasterization-based differentiable renderer for translucent objects

The present disclosure provides a training method and device for an image identifying model, and an image identifying method. The training method comprises: obtaining image samples of a plurality of categories; inputting image samples of each category into a feature extraction layer of the image identifying model to extract a feature vector of each image sample; calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category; establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information; obtaining augmented sample features of the each category based on the augmented distribution function; and inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning.

Training method and device for image identifying model, and image identifying method

A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.

Method and device for evaluating effect of classifying fuzzy attribute

Systems and method directed to generating a stylized image are disclosed. In particular, the method includes, in a first data path, (a) applying first stylization to an input image and (b) applying enlargement to the stylized image from (a). The method also includes, in a second data path, (c) applying segmentation to the input image to identify a face region of the input image and generate a mask image, and (d) applying second stylization to an entirety of the input image and inpainting to the identified face region of the stylized image. Machine-assisted blending is performed based on (1) the stylized image after the enlargement from the first data path, (2) the inpainted image from the second data path, and (3) the mask image, in order to obtain a final stylized image.

Portrait stylization framework using a two-path image stylization and blending

An image processing method includes acquiring a set of image samples for training an attribute recognition model, wherein the set of image samples includes a first subset of image samples with category labels and a second subset of image samples without category labels; training a sample prediction model using the first subset of image samples, and predicting categories of the image samples in the second subset of image samples using the trained sample prediction model; determining a category distribution of the set of image samples based on the category labels of the first subset of image samples and the predicted categories of the second subset of image samples; and acquiring a new image sample if the determined category distribution does not conform to the expected category distribution, and adding the acquired new image sample to the set of image samples.

Image processing method, image processing device and computer readable medium, for acquiring image sample data for training an attribute recognition model

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Los Angeles, CA, United States of America

Jing Liu