  Non-photorealistic videos are in demand with the wave of the metaverse, but
lack of sufficient research studies. This work aims to take a step forward to
understand how humans perceive non-photorealistic videos with eye fixation
(\ie, saliency detection), which is critical for enhancing media production,
artistic design, and game user experience. To fill in the gap of missing a
suitable dataset for this research line, we present NPF-200, the first
large-scale multi-modal dataset of purely non-photorealistic videos with eye
fixations. Our dataset has three characteristics: 1) it contains soundtracks
that are essential according to vision and psychological studies; 2) it
includes diverse semantic content and videos are of high-quality; 3) it has
rich motions across and within videos. We conduct a series of analyses to gain
deeper insights into this task and compare several state-of-the-art methods to
explore the gap between natural images and non-photorealistic data.
Additionally, as the human attention system tends to extract visual and audio
features with different frequencies, we propose a universal frequency-aware
multi-modal non-photorealistic saliency detection model called NPSNet,
demonstrating the state-of-the-art performance of our task. The results uncover
strengths and weaknesses of multi-modal network design and multi-domain
training, opening up promising directions for future works. {Our dataset and
code can be found at \url{https://github.com/Yangziyu/NPF200}}.
