Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this work, we take one step further to attack the problem of video harmonization. Specifically, we train a convolutional neural network in an adversarial way, exploiting a pixel-wise disharmony discriminator to achieve more realistic harmonized results and introducing a temporal loss to increase temporal consistency between consecutive harmonized frames. Thanks to the pixel-wise disharmony discriminator, we are also able to relieve the need of input foreground masks. Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network. Experiments show that training on our synthetic dataset generalizes well to the real-world composite dataset. Also, our method successfully incorporates temporal consistency during training and achieves more harmonious results than previous methods.
Directly applying image harmonization frame by frame for videos inevitably results in flicker artifacts. This is because the corresponding regions in different frames are harmonized in different ways. Plenty of approaches have been proposed to enforce temporal consistency in video editing, such as spatio-temporal smoothing @cite_19 @cite_7 @cite_2 , optimization with a temporal loss @cite_31 @cite_38 , frame propagation @cite_3 , etc . The above methods either require extra post-processing operations or rely on a time-consuming optimization process. Recently, some video style transfer methods @cite_23 @cite_29 show that temporal consistency and style transfer can be simultaneously learned by a CNN, which acquires considerable temporal consistency at a very little time cost. Inspired by this idea, our proposed method also chooses to incorporate the temporal consistency during the training phase. However, instead of calculating a global temporal loss, we introduce a regional temporal loss that forces our model to pay more attention to the disharmonious regions, therefore leading to more coherent harmonized results.
@cite_38 @cite_7 @cite_29 @cite_3 @cite_19 @cite_23 @cite_2 @cite_31
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In this paper, we present a new approach for recovering spacetime-consistent depth maps from multiple video sequences captured by stationary, synchronized and calibrated cameras for depth based free viewpoint video rendering. Our two-pass approach is generalized from the recently proposed region-tree based binocular stereo matching method. In each pass, to enforce temporal consistency between successive depth maps, the traditional region-tree is extended into a temporal one by including connections to “temporal neighbor regions” in previous video frames, which are identified using estimated optical flow information. For enforcing spatial consistency, multi-view geometric constraints are used to identify inconsistencies between depth maps among different views which are captured in an inconsistency map for each view. Iterative optimizations are performed to progressively correct inconsistencies through inconsistency maps based depth hypotheses pruning and visibility reasoning. Furthermore, the background depth and color information is generated from the results of the first pass and is used in the second pass to enforce sequence-wise temporal consistency and to aid in identifying and correcting spatial inconsistencies. The extensive experimental evaluations have shown that our proposed approach is very effective in producing spatially and temporally consistent depth maps. Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of a clip with different intervals. This learning process makes the learned model more capable of dealing with motion speed variance. Given a clip sampled from a video, we use its past and future neighboring clips as the temporal context, and reconstruct the two temporal transitions, i.e., present-past transition and present-future transition, reflecting the temporal information in different views. The proposed method exploits the two transitions simultaneously by incorporating a bidirectional reconstruction which consists of a backward reconstruction and a forward reconstruction. We apply the proposed method to two challenging video tasks, i.e., complex event detection and video captioning, in which it achieves state-of-the-art performance. Notably, our method generates the best single feature for event detection with a relative improvement of 10.4 on the MEDTest-13 dataset and achieves the best performance in video captioning across all evaluation metrics on the YouTube2Text dataset. Recent years have seen tremendous progress in still-image segmentation; however the naive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: (1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and (2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of “clockwork” convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets. Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of a clip with different intervals. This learning process makes the learned model more capable of dealing with motion speed variance. Given a clip sampled from a video, we use its past and future neighboring clips as the temporal context, and reconstruct the two temporal transitions, i.e., present @math past transition and present @math future transition, reflecting the temporal information in different views. The proposed method exploits the two transitions simultaneously by incorporating a bidirectional reconstruction which consists of a backward reconstruction and a forward reconstruction. We apply the proposed method to two challenging video tasks, i.e., complex event detection and video captioning, in which it achieves state-of-the-art performance. Notably, our method generates the best single feature for event detection with a relative improvement of 10.4 on the MEDTest-13 dataset and achieves the best performance in video captioning across all evaluation metrics on the YouTube2Text dataset.