The difference is that we extract the high-level semantics only through an encoder, i.e., the low-resolution branch S of MODNet, which has two main advantages. First, unlike natural images of which foreground and background fit seamlessly together, images generated by replacing backgrounds are usually unnatural. Fig. In this way, the matting algorithms only have to estimate the foreground probability inside the unknown area based on the priori from the other two regions. Using two powerful models if you would like to achieve somewhat accurate results. Autonomous-Ai-drone-scripts (2020), https://github.com/ZHKKKe/MODNet[3] Xu, N. et al., Deep Image MattingAdobe Research (2017), https://sites.google.com/view/deepimagematting[4] GrabCut algorithm by OpenCV, https://docs.opencv.org/3.4/d8/d83/tutorial_py_grabcut.html. It calculates the absolute difference between the input image and the composited image obtained, from the ground truth foreground and the ground truth background. We measure the model size by the total number of parameters, and we reflect the execution efficiency by the average inference time over PHM-100 on an NVIDIA GTX 1080Ti GPU (input images are cropped to 512512). - Real-Time High-Resolution Background Matting, keras-onnx Xu \etal[DIM] proposed an auto-encoder architecture to predict alpha matte from a RGB image and a trimap. the performance of trimap-free DIM without pre-training is far worse than the one with pre-training. 2, MODNet consists of three branches, which learn different sub-objectives through specific constraints. Since obtaining a trimap requires user effort, some recent methods (including our MODNet) attempt to avoid it, as described below. Visual Comparisons of Trimap-free Methods on PHM-100. High-Resolution Representations. We follow the original papers to reproduce the methods that have no publicly available codes. Hence, it can reflect the matting performance more comprehensively. These two pieces of training are made on the MODNet architecture. 9, when a moving object suddenly appears in the background, the result of BM will be affected, but MODNet is robust to such disturbances. More Visual Comparisons of Trimap-free Methods on PHM-100. As described in Sec. If you find a rendering bug, file an issue on GitHub. Since the fine boundaries are preserved in ~dp output by M, we append an extra constraint to maintain the details in M as: We generalize MODNet to the target domain by optimizing Lcons and Ldd simultaneously. However, the subsequent branches process all S(I) in the same way, which may cause the feature maps with false semantics to dominate the predicted alpha mattes in some images. The fusion branch F in MODNet is a straightforward CNN module, combining semantics and details. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. Finally, a fusion branch, also supervised by the whole ground truth matte is added to predict the final result of the alpha matte, which will be used to remove the background of the input image. Third, MODNet can be easily optimized end-to-end since it is a single well-designed model instead of a complex pipeline. It removes the fine structures (such as hair) that are not essential to human semantics. This new background removal technique can extract a person from a single input image, without the need for a green screen in real-time! Then, there is the self-supervised training process. It is much faster than contemporaneous matting methods and runs at 63 frames per second. 2.

An arbitrary CNN architecture can be used where you see the convolutions happening, in this case, they used MobileNetV2 because it was made for mobile devices. As exhibited in Fig. Zhang \etal[LFM] applied a fusion network to combine the predicted foreground and background. 1 summarizes our framework. tflite2tensorflow MODNet has several advantages over previous trimap-free methods. Advantages of MODNet over Trimap-based Method. We use MobileNetV2 pre-trained on the Supervisely Person Segmentation (SPS) [SPS] dataset as the backbone of all trimap-free models. Moreover, MODNet suffers less from the domain shift problem in practice due to the proposed SOC and OFD. When comparing MODNet and RobustVideoMatting you can also consider the following projects: I want to translate only the background of image using image-to-image translation. Other works designed their pipelines that contained multiple models. For example, background matting [BM] replaces the trimap by a separate background image. MODNet suffers less from the domain shift problem in practice due to the proposed SOC and OFD. - An artificial intelligence platform for the StarCraft II with large-scale distributed training and grand-master agents. Most existing matting methods take a pre-defined trimap as an auxiliary input, which is a mask containing three regions: absolute foreground (=1), absolute background (=0), and unknown area (=0.5). Similar to existing multiple-model approaches, the first step of MODNet is to locate the human in the input image I. In computer vision, we can divide these mechanisms into spatial-based or channel-based according to their operating dimension.

pytorch-deep-image-matting Real-world data can be divided into multiple domains according to different device types or diverse imaging methods. Now, do you really need a green screen for real-time human matting? dont have to squint at a PDF. This GrabCut algorithm basically estimates the color distribution of the foreground item and the background using a gaussian mixture model. We can apply arbitrary CNN backbone to S. As a result, it is not easy to compare these methods fairly. Suppose that we have three consecutive frames, and their corresponding alpha mattes are t1, t, and t+1, where t is the frame index. Fig. Lutz \etal[AlphaGAN] demonstrated the effectiveness of generative adversarial networks [GAN] in matting. Unfortunately, this technique needs two inputs: an image, and its trimap. ": https://arxiv.org/pdf/2011.11961.pdf, Implement GrabCut yourself: https://github.com/louisfb01/iterative-grabcut, MODNet GitHub code: https://github.com/ZHKKKe/MODNet, Deep Image Matting - Adobe Research: https://sites.google.com/view/deepimagematting, CNNs explanation video: https://youtu.be/YUyec4eCEiY. This paper has presented a simple, fast, and effective MODNet to avoid using a green screen in real-time human matting. Deep Image Matting by Adobe Research, is an example of using the power of deep learning for this task. One possible future work is to address video matting under motion blurs through additional sub-objectives, e.g., optical flow estimation. Liu \etal[BSHM] concatenated three networks to utilize coarse labeled data in matting. The purpose of reusing the low-level features is to reduce the computational overheads of D. In addition, we further simplify D in the following three aspects: (1) D consists of fewer convolutional layers than S; (2) a small channel number is chosen for the convolutional layers in D; (3) we do not maintain the original input resolution throughout D. In practice, D consists of 12 convolutional layers, and its maximum channel number is 64. With a batch size of 16, the initial learning rate is 0.01 and is multiplied by 0.1 after every 10 epochs. Cho \etal[NIMUDCNN] and Shen \etal[DAPM] combined the classic algorithms with CNN for alpha matte refinement. To successfully remove the background using the Deep Image Matting technique, we need a powerful network able to localize the person somewhat accurately. As a consequence, the labeled datasets for human matting are usually small. As you can see, the network is basically mainly composed of downsampling, convolutions, and upsampling. For example, the foreground probability of a certain pixel belonging to the background may be wrong in the predicted alpha matte p but is correct in the predicted coarse semantic mask sp. Currently, a green screen is required to obtain a high quality alpha matte in real time. We briefly discuss some other techniques related to the design and optimization of our method. Modern deep learning and the power of our GPUs made it possible to create much more powerful applications that are yet not perfect. MODNet is a light-weight matting objective decomposition network (MODNet), which can process portrait matting from a single input image in realtime. Want to hear about new tools we're making?

One possible future work is to address video matting under motion blurs through additional sub-objectives, e.g., optical flow estimation. Applying trimap-based methods in practice requires an additional step to obtain the trimap, which is commonly implemented by a depth camera, e.g., ToF [ToF]. To overcome the domain shift problem, we introduce a self-supervised strategy based on sub-objective consistency (SOC) for MODNet. Fortunately for us, this new technique can process human matting from a single input image, without the need for a green screen or a trimap in real-time at up to 63 frames per second! MODNet versus BM under Fixed Camera Position. When a green screen is not available, most existing matting methods [AdaMatting, CAMatting, GCA, IndexMatter, SampleMatting, DIM] use a pre-defined trimap as a priori. If you are not familiar with convolutional neural networks, or CNNs, I invite you to watch the video I made explaining what they are. The main problem of all these methods is that they cannot be used in interactive applications since: (1) the background images may change frame to frame, and (2) using multiple models is computationally expensive. Here we only provide visual results444Refer to our online supplementary video for more results. First, neural networks are better at learning a set of simple objectives rather than a complex one. A version of this model is currently used in most websites you use to automatically remove the background from your pictures. Through three interdependent branches, S, D, and F, which are constrained by specific supervisions generated from the ground truth matte g. In this stage, we freeze the BatchNorm [BatchNorm] layers within MODNet and finetune the convolutional layers by Adam with a learning rate of 0.0001. Existing works constructed their validation benchmarks from a small amount of labeled data through image synthesis. because no ground truth mattes are available. It is not an easy task to find the person and remove the background. For a fair comparison, we train all models on the same dataset, which contains nearly 3000 annotated foregrounds. Now, theres one last step to this networks architecture. This fusion branch is just a CNN module used to combine the semantics and details, where an upsampling has to be made if we want the accurate details around the semantics. - A repository for storing models that have been inter-converted between various frameworks. The impact of this setup on detail prediction is negligible since D contains a skip link. To demonstrate this, we conduct experiments on the open-source Adobe Matting Dataset (AMD) [DIM]. Create an account to follow your favorite communities and start taking part in conversations.

However, it still performs inferior to trimap-based DIM, since PHM-100 contains samples with challenging poses or costumes. On a carefully designed human matting benchmark newly proposed in this work, MODNet greatly outperforms prior trimap-free methods. The design of MODNet benefits from optimizing a series of correlated sub-objectives simultaneously via explicit constraints. Although dp may contain inaccurate values for the pixels with md=0, it has high precision for the pixels with md=1. Besides, the indices of these channels vary in different images. We use Mean Square Error (MSE) and Mean Absolute Difference (MAD) as quantitative metrics. Based on that data, you can find the most popular open-source packages, Attention Mechanisms. It may fail in fast motion videos. Intuitively, this pixel should have close values in p and sp. They called their network: MODNet. Consequently, they are unavailable in real-time applications. They reconstruct sound using cameras and a laser beam on any vibrating surface, allowing them to isolate music instruments, focus on a specific speaker, remove ambient noises, and many more amazing applications. BM relies on a static background image, which implicitly assumes that all pixels whose value changes in the input image sequence belong to the foreground. We prove this standpoint by the matting results on Adobe Matting Dataset222Refer to Appendix B for the results of portrait images (with synthetic backgrounds) from Adobe Matting Dataset.. This trimap is the one sent to the Deep Image Matting model with the original image, and you get your output. It is designed for real-time applications, running at 63 frames per second (fps) on an Nvidia GTX 1080Ti GPU with an input size of 512512. Wang \etal[net_hrnet] proposed to keep high-resolution representations throughout the model and exchange features between different resolutions, which induces huge computational overheads. Although our results are not able to surpass those of the trimap-based methods on the human matting benchmarks with trimaps, our experiments show that MODNet is more stable in practical applications due to the removal of the trimap input. (, (b) To adapt to real-world data, MODNet is finetuned on the unlabeled data by using the consistency between sub-objectives. Since the flickering pixels in a frame are likely to be correct in adjacent frames, we may utilize the preceding and the following frames to fix these pixels.

The decomposed sub-objectives are correlated and help strengthen each other, we can optimize MODNet end-to-end. This strategy utilizes the consistency among the sub-objectives to reduce artifacts in the predicted alpha matte. Looking like this. 8. Second, professional photography is often carried out under controlled conditions, like special lighting that is usually different from those observed in our

MODNet can process trimap-free portrait matting in realtime under changing scenes. Since BM does not support dynamic backgrounds, we conduct validations footnotemark: in the fixed-camera scenes from [BM]. Finally, we demonstrate the effectiveness of SOC and OFD in adapting MODNet to real-world data.

In MODNet, we extend this idea by dividing the trimap-free matting objective into semantic estimation, detail prediction, and semantic-detail fusion. The code and a pre-trained model will also be available soon on their Github [2], as they wrote on their page. By taking only RGB images as input, MODNet enables the prediction of alpha mattes under changing scenes. Unlike the results on PHM-100, the performance gap between trimap-free and trimap-based models is much smaller. There are two insights behind MODNet. 7, we composite the foreground over a green screen to emphasize that SOC is vital for generalizing MODNet to real-world data. We further conduct ablation experiments to evaluate various aspects of MODNet. For example, MSE and MAD between trimap-free MODNet and trimap-based DIM is only about 0.001. More importantly, our method achieves remarkable results in daily photos and videos. as well as similar and alternative projects. Nonetheless, feeding RGB images into a single neural network still yields unsatisfactory alpha mattes. "Make-A-Scene": a fantastic blend between text and sketch-conditioned image generation. daily life. It is a small network and extremely efficient when compared to other state-of-the-art architectures. These drawbacks make all aforementioned matting methods not suitable for real-time applications, such as preview in a camera. arXiv Vanity renders academic papers from Many techniques are using basic computer vision algorithms for this task quickly but not precisely. Is a Green Screen Really Necessary for Real-Time Human Matting? A Trimap-Free Portrait Matting Solution in Real Time [AAAI 2022] (by ZHKKKe), Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML! Its a light-weight matting objective decomposition network. Unlike the binary mask output from image segmentation [IS_Survey] and saliency detection [SOD_Survey], matting predicts an alpha matte with preccise foreground probability for each pixel, which is represented by in the following formula: where i is the pixel index, and B is the background of I. 4.2). Unfortunately, our method is not able to handle strange costumes and strong motion blurs that are not covered by the training set. caer If we come back to the full architecture here, we can see that they apply what they called a one-frame delay. Methods that are based on multiple models [SHM, BSHM, DAPM] have shown that regarding trimap-free matting as a trimap prediction (or segmentation) step plus a trimap-based matting step can achieve better performances. NVIDIA GPU (dGPU) support. For each foreground, we generate 5 samples by random cropping and 10 samples by compositing the backgrounds from the OpenImage dataset [openimage]. Although these images have monochromatic or blurred backgrounds, the labeling process still needs to be completed by experienced annotators with considerable amount of time and the help of professional tools. MODNet is shown to have good performances on the carefully designed PHM-100 benchmark and a variety of real-world data. By assuming that the images captured by the same kind of device (such as smartphones) belong to the same domain, we capture several video clips as the unlabeled data for self-supervised SOC domain adaptation. However, adding the L2 loss on blurred G(~p) will smooth the boundaries in the optimized ~p. You can just imagine the time it would need to process a whole video. DI-star Second, applying explicit supervisions for each sub-objective can make different parts of the model to learn decoupled knowledge, which allows all the sub-objectives to be solved within one model. Another contribution of this work is a carefully designed validation benchmark for human matting. Instead, MODNet only applies an independent high-resolution branch to handle foreground boundaries. 3, M has three outputs for an unlabeled image ~I, as: We force the semantics in ~p to be consistent with ~sp and the details in ~p to be consistent with ~dp by: where ~md indicates the transition region in ~p, and G has the same meaning as the one in Eq. GitHub for Is a Green Screen Really Necessary for Real-Time Human Matting? Motivated by this, our self-supervised SOC strategy imposes the consistency constraints between the predictions of the sub-objectives (Fig.

it outperforms trimap-based DIM, which reveals the superiority of our network architecture.



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