CNN based texture synthesize with Semantic segment
This work addresses a specific bottleneck in image generation for applications like photo editing and animation, but it appears incremental as it builds on existing CNN methods.
The paper tackles the problem of texture synthesis distortion in CNNs by introducing a semantic segmentation pre-processing step, resulting in improved spatial location capture and reduced distortion.
Deep learning algorithm display powerful ability in Computer Vision area, in recent year, the CNN has been applied to solve problems in the subarea of Image-generating, which has been widely applied in areas such as photo editing, image design, computer animation, real-time rendering for large scale of scenes and for visual effects in movies. However in the texture synthesize procedure. The state-of-art CNN can not capture the spatial location of texture in image, lead to significant distortion after texture synthesize, we propose a new way to generating-image by adding the semantic segment step with deep learning algorithm as Pre-Processing and analyze the outcome.