公开(公告)号：US20230110114A1

公开(公告)日：2023-04-13

申请号：US17499611

申请日：2021-10-12

Applicant: Adobe Inc.

Inventor： Chinthala Pradyumna Reddy ,  Zhifei Zhang ,  Matthew Fisher ,  Hailin Jin ,  Zhaowen Wang ,  Niloy J Mitra

IPC: G06T11/20 ,  G06T3/40

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.

公开(公告)号：US11875435B2

公开(公告)日：2024-01-16

申请号：US17499611

申请日：2021-10-12

Applicant: Adobe Inc.

Inventor： Chinthala Pradyumna Reddy ,  Zhifei Zhang ,  Matthew Fisher ,  Hailin Jin ,  Zhaowen Wang ,  Niloy J Mitra

IPC: G06T11/20 ,  G06T3/40

CPC classification number: G06T11/203 ,  G06T3/40

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.