Existing realistic blur datasets provide insufficient variety in scenes and blur patterns to be trained, while expanding data diversity demands considerable time and effort due to complex dual-camera systems. To address the challenge, data augmentation can be an effective way to artificially increase data diversity. However, existing methods on this line are typically designed to estimate motions from a 2D perspective, e.g., estimating 2D non-uniform kernels disregarding 3D aspects of blur modeling, which leads to unrealistic motion patterns due to the fact that camera and object motions inherently arise in 3D space. In this paper, we propose a 3D-aware blur synthesizer capable of generating diverse and realistic blur images for blur data augmentation. Specifically, we estimate 3D camera positions within the motion blur interval, generate the corresponding scene images, and aggregate them to synthesize a realistic blur image. Since the 3D camera positions projected onto the 2D image plane inherently lie in 2D space, we can represent the 3D transformation as a combination of 2D transformation and projected 3D residual component. This allows for 3D transformation without requiring explicit depth measurements, as the 3D residual component is directly estimated via a neural network. Furthermore, our blur synthesizer allows for controllable blur data augmentation by modifying blur magnitude, direction, and scenes, resulting in diverse blur images. As a result, our method significantly improves deblurring performance, making it more practical for real-world scenarios.

In digital photography, autofocus is a key feature that aids high-quality image capture, and modern approaches use the phase patterns arising from dual-pixel sensors as important focus cues. However, dual-pixel data is prone to multiple error sources in its image capturing process, including lens shading or distortions due to the inherent optical characteristics of the lens. We observe that, while these degradations are hard to model using prior knowledge, they are correlated with the spatial position of the pixels within the image sensor area, and we propose a learning-based autofocus model with positional encodings (PE) to capture these patterns. Specifically, we introduce RoI-PE, which encodes the spatial position of our focusing region-of-interest (RoI) on the imaging plane. Learning with RoI-PE allows the model to be more robust to spatially-correlated degradations. In addition, we also propose to encode the current focal position of lens as lens-PE, which allows us to significantly reduce the computational complexity of the autofocus model. Experimental results clearly demonstrate the effectiveness of using the proposed position encodings for automatic focusing based on dual-pixel data.

Video Panoptic Segmentation (VPS) aims at assigning a class label to each pixel, uniquely segmenting and identifying all object instances consistently across all frames. Classic solutions usually decompose the VPS task into several subtasks and utilize multiple surrogates (e.g. boxes and masks, centers and offsets) to represent objects. However, this divide-and-conquer strategy requires complex post-processing in both spatial and temporal domains and is vulnerable to failures from surrogate tasks. In this paper, inspired by object-centric learning which learns compact and robust object representations, we present Slot- VPS, the first end-to-end framework for this task. We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots. The coherent spatio-temporal object's information is retrieved and encoded into the panoptic slots by the proposed Video Panoptic Retriever, enabling to localize, segment, differentiate, and associate objects in a unified manner. Finally, the output panoptic slots can be directly converted into the class, mask, and object ID of panoptic objects in the video. We conduct extensive ablation studies and demonstrate the effectiveness of our approach on two benchmark datasets, Cityscapes- VP S (val and test sets) and VIPER (val set), achieving new state-of-the-art performance of 63.7, 63.3 and 56.2 VPQ, respectively.

This study investigates the influence of digital content on mobile devices including flash animations, app books, and augmented reality (AR) books on children's reading interest and reading concentration for books. The factors responsible for the aforementioned influence were also investigated. The results from this research show that flash animation can significantly increase the interest in reading and concentration. AR books and app books led children to become more interested in storybooks but significantly decreased their reading concentration. Following a comparison of the digital content, it was concluded that all the tested prototypes could positively increase the reading interest of children in the same-storyline books. However, prototypes with a high level of interactivity could have a negative influence on children’s reading concentration. The research findings are important in determining how to design digital content for children.