Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called Virtual KITTI (see this http URL), automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.
The degree of photorealism allowed by the recent progress in computer graphics and modern high-level generic graphics platforms enables a more widespread use of synthetic data generated under less constrained settings. First attempts to use synthetic data for training are mainly limited to using rough synthetic models or synthesized real examples ( of pedestrians @cite_43 @cite_30 ). In contrast, Mar 'in al @cite_37 @cite_35 @cite_14 went further and positively answer the intriguing question whether one can learn appearance models of pedestrians in a virtual world and use the learned models for detection in the real world. A related approach is described in @cite_40 , but for scene- and scene-location specific detectors with fixed calibrated surveillance cameras and a priori known scene geometry. In the context of video surveillance too, @cite_41 proposes a virtual simulation test bed for system design and evaluation. Several other works use 3D CAD models for more general object pose estimation @cite_32 @cite_17 and detection @cite_4 @cite_6 .
@cite_30 @cite_35 @cite_37 @cite_14 @cite_4 @cite_41 @cite_32 @cite_6 @cite_43 @cite_40 @cite_17
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We consider scenarios where we have zero instances of real pedestrian data (e.g., a newly installed surveillance system in a novel location in which no labeled real data or unsupervised real data exists yet) and a pedestrian detector must be developed prior to any observations of pedestrians. Given a single image and auxiliary scene information in the form of camera parameters and geometric layout of the scene, our approach infers and generates a large variety of geometrically and photometrically accurate potential images of synthetic pedestrians along with purely accurate ground-truth labels through the use of computer graphics rendering engine. We first present an efficient discriminative learning method that takes these synthetic renders and generates a unique spatially-varying and geometry-preserving pedestrian appearance classifier customized for every possible location in the scene. In order to extend our approach to multi-task learning for further analysis (i.e., estimating pose and segmentation of pedestrians besides detection), we build a more generalized model employing a fully convolutional neural network architecture for multi-task learning leveraging the “free" ground-truth annotations that can be obtained from our pedestrian synthesizer. We demonstrate that when real human annotated data is scarce or non-existent, our data generation strategy can provide an excellent solution for an array of tasks for human activity analysis including detection, pose estimation and segmentation. Experimental results show that our approach (1) outperforms classical models and hybrid synthetic-real models, (2) outperforms various combinations of off-the-shelf state-of-the-art pedestrian detectors and pose estimators that are trained on real data, and (3) surprisingly, our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited. We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited. The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data. Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in real-world images? Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the data set shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector.