2,274 research outputs found
Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach
In this paper, we propose an Instant Photorealistic Style Transfer (IPST)
approach, designed to achieve instant photorealistic style transfer on
super-resolution inputs without the need for pre-training on pair-wise datasets
or imposing extra constraints. Our method utilizes a lightweight StyleNet to
enable style transfer from a style image to a content image while preserving
non-color information. To further enhance the style transfer process, we
introduce an instance-adaptive optimization to prioritize the photorealism of
outputs and accelerate the convergence of the style network, leading to a rapid
training completion within seconds. Moreover, IPST is well-suited for
multi-frame style transfer tasks, as it retains temporal and multi-view
consistency of the multi-frame inputs such as video and Neural Radiance Field
(NeRF). Experimental results demonstrate that IPST requires less GPU memory
usage, offers faster multi-frame transfer speed, and generates photorealistic
outputs, making it a promising solution for various photorealistic transfer
applications.Comment: 8 pages (reference excluded), 6 figures, 4 table
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Cross-domain synthesizing realistic faces to learn deep models has attracted
increasing attention for facial expression analysis as it helps to improve the
performance of expression recognition accuracy despite having small number of
real training images. However, learning from synthetic face images can be
problematic due to the distribution discrepancy between low-quality synthetic
images and real face images and may not achieve the desired performance when
the learned model applies to real world scenarios. To this end, we propose a
new attribute guided face image synthesis to perform a translation between
multiple image domains using a single model. In addition, we adopt the proposed
model to learn from synthetic faces by matching the feature distributions
between different domains while preserving each domain's characteristics. We
evaluate the effectiveness of the proposed approach on several face datasets on
generating realistic face images. We demonstrate that the expression
recognition performance can be enhanced by benefiting from our face synthesis
model. Moreover, we also conduct experiments on a near-infrared dataset
containing facial expression videos of drivers to assess the performance using
in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note:
substantial text overlap with arXiv:1905.0028
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