Video object removal has achieved advanced performance due to the recent success of video generative models. However, when addressing the side effects of objects, \textit{e.g.,} their shadows and reflections, existing works struggle to eliminate these effects for the scarcity of paired video data as supervision. This paper presents \method, termed \textbf{R}emove \textbf{O}bjects with \textbf{S}ide \textbf{E}ffects, a framework that systematically studies the object's effects on environment, which can be categorized into five common cases: shadows, reflections, light, translucency and mirror. Given the challenges of curating paired videos exhibiting the aforementioned effects, we leverage a 3D rendering engine for synthetic data generation. We carefully construct a fully-automatic pipeline for data preparation, which simulates a large-scale paired dataset with diverse scenes, objects, shooting angles, and camera trajectories. ROSE is implemented as an video inpainting model built on diffusion transformer. To localize all object-correlated areas, the entire video is fed into the model for reference-based erasing. Moreover, additional supervision is introduced to explicitly predict the areas affected by side effects, which can be revealed through the differential mask between the paired videos. To fully investigate the model performance on various side effect removal, we presents a new benchmark, dubbed ROSE-Bench, incorporating both common scenarios and the five special side effects for comprehensive evaluation. Experimental results demonstrate that \method achieves superior performance compared to existing video object erasing models and generalizes well to real-world video scenarios.

Parsing building facades into procedural grammars plays an important role for 3D building model generation tasks, which have been long desired in computer vision. Deep learning is a promising approach to facade parsing, however, a straightforward solution by directly applying standard deep learning approaches cannot always yield the optimal results. This is primarily due to two reasons: 1) it is nontrivial to train existing semantic segmentation networks for facade parsing, e.g., Fully-Convolutional Neural Networks (FCN) which are usually weak at predicting fine-grained shapes (J. Long et al., 2015); and 2) building facades are man-made architectures with highly regularized shape priors, and the prior knowledge plays an important role in facade parsing, for which how to integrate the prior knowledge into deep neural networks remains an open problem. In this paper, we present a novel symmetric loss function that can be used in deep neural networks for end-to-end training. This novel loss is based on the assumption that most of windows and doors have a highly symmetric rectangle shape, and it penalizes all window predictions that are non-rectangles. This prior knowledge is smoothly integrated into the end-to-end training process. Quantitative evaluation demonstrates that our method has outperformed previous state-of-art methods significantly on five popular facade parsing datasets. Qualitative results have shown that our method effectively aids deep convolutional neural networks to predict more accurate, visually pleasing, and symmetric shapes. To the best of our knowledge, we are the first to incorporate symmetry constraint into end-to-end training in deep neural networks for facade parsing.

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale and rotation changes simultaneously in log-polar coordinates. Moreover, a variant of correlation filter is used to predict the translational motion individually. Our experimental results demonstrate that the proposed tracker achieves very promising prediction performance compared with the state-of-the-art visual object tracking methods while still retaining the advantages of high efficiency and simplicity in conventional correlation filter-based tracking methods.

The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region Proposal Network. We test our method by training a FCN-8s network with the novel loss function. Experimental results show that our method has outperformed previous state-of-the-art methods significantly on both the ECP dataset and the eTRIMS dataset. As far as we know, we are the first to employ end-to-end deep convolutional neural network on full image scale in the task of building facades parsing.