Semi-parametric Image Synthesis
원문 초록
We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques. The results are shown in the supplementary video at https://youtu.be/U4Q98lenGLQ
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
Process-aware and high-fidelity microstructure generation using stable diffusion
LSegDiff: A Latent Diffusion Model for Medical Image Segmentation
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
토론
이 논문과 연결된 포럼 글이 아직 없습니다.