Abstract

Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, existing datasets, while large-scale, lack such diversities and are geographically biased towards mainly developed cities. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models due to the sheer degree of variations in the object types, densities, and locations. To facilitate better research toward accommodating such scenarios, we build a new dataset, IDD-3D, which consists of multimodal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames across various traffic scenarios. We discuss the need for this dataset through statistical comparisons with existing datasets and highlight benchmarks on standard 3D object detection and tracking tasks in complex layouts. Code and data available 1 .

Abstract

This work proposes a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We improve the quality of lip sync using audio as an additional input, helping the network to attend to the mouth region. We use additional priors using face segmentation and face mesh to improve the structure of the reconstructed faces. Finally, we improve the visual quality of the generations by incorporating a carefully designed identity-aware generator module. The identityaware generator takes the source image and the warped motion features as input to generate a high-quality output with fine-grained details. Our method produces state-ofthe-art results and generalizes well to unseen faces, languages, and voices. We comprehensively evaluate our approach using multiple metrics and outperforming the current techniques both qualitative and quantitatively. Our work opens up several applications, including enabling low bandwidth video calls. We release a demo video and additional information at http://cvit.iiit.ac.in/

Abstract

Annotation is a crucial first step in the semantic segmentation of urban images that facilitates the development of autonomous navigation systems. However, annotating complex urban images is time-consuming and challenging. It requires significant human effort making it expensive and error-prone. To reduce human effort during annotation, multiple images need to be annotated in a short time-span. In this paper, we introduce ScribbleNet, an interactive image segmentation algorithm to address this issue. Our approach provides users with a pre-segmented image that iteratively improves the segmentation using scribble as an annotation input. This method is based on conditional inference and exploits the learnt correlations in a deep neural network (DNN). ScribbleNet can: (1) work with urban city scenes captured in unseen environments, (2) annotate new classes not present in the training set, and (3) correct several labels at once. We compare this method with other interactive segmentation approaches on multiple datasets such as CityScapes, BDD, Mapillary Vistas, KITTI, and IDD. ScribbleNet reduces the annotation time of an image by up to 14.7 over manual annotation and up to 5.4 over the current approaches. The algorithm is integrated into the publicly available LabelMe image annotation tool and will be released as an open-source software.

Abstract

Many people with some form of hearing loss consider lipreading as their primary mode of day-to-day communication. However, finding resources to learn or improve one’s lipreading skills can be challenging. This is further exacerbated in the COVID19 pandemic due to restrictions on direct interactions with peers and speech therapists. Today, online MOOCs platforms like Coursera and Udemy have become the most effective form of training for many types of skill development. However, online lipreading resources are scarce as creating such resources is an extensive process needing months of manual effort to record hired actors. Because of the manual pipeline, such platforms are also limited in vocabulary, supported languages, accents, *Equal contribution and speakers and have a high usage cost. In this work, we investigate the possibility of replacing real human talking videos with synthetically generated videos. Synthetic data can easily incorporate larger vocabularies, variations in accent, and even local languages and many speakers. We propose an end-to-end automated pipeline to develop such a platform using state-of-the-art talking head video generator networks, text-to-speech models, and computer vision techniques. We then perform an extensive human evaluation using carefully thought out lipreading exercises to validate the quality of our designed platform against the existing lipreading platforms. Our studies concretely point toward the potential of our approach in developing a largescale lipreading MOOC platform that can impact millions of people with hearing loss

Abstract

Doubles play an indispensable role in the movie industry. They take the place of the actors in dangerous stunt scenes or scenes where the same actor plays multiple characters. The double’s face is later replaced with the actor’s face and expressions manually using expensive CGI technology, costing millions of dollars and taking months to complete. An automated, inexpensive, and fast way can be to use face-swapping techniques that aim to swap an identity from a source face video (or an image) to a target face video. However, such methods cannot preserve the source expressions of the actor important for the scene’s context. *Equal contribution To tackle this challenge, we introduce video-to-video (V2V) face-swapping, a novel task of face-swapping that can preserve (1) the identity and expressions of the source (actor) face video and (2) the background and pose of the target (double) video. We propose FaceOff, a V2V face-swapping system that operates by learning a robust blending operation to merge two face videos following the constraints above. It reduces the videos to a quantized latent space and then blends them in the reduced space. FaceOff is trained in a self-supervised manner and robustly tackles the nontrivial challenges of V2V face-swapping. As shown in the experimental section, FaceOff significantly outperforms alternate approaches qualitatively and quantitatively.

Abstract

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day long first-person videos. The highly redundant nature of these videos and extreme camera-shakes make them difficult to watch from beginning to end. These videos require efficient summarization tools for consumption. However, traditional summarization techniques developed for static surveillance videos or highly curated sports videos and movies are either not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. This paper presents a novel unsupervised reinforcement learning framework to summarize egocentric videos both in terms of length and the content. The proposed framework facilitates incorporating various prior preferences such as faces, places, or …

Abstract

A processor-implemented method for generating a lip-sync for a face to a target speech of a live session to a speech in one or more languages in-sync with improved visual quality using a machine learning model and a pre-trained lip-sync model is provided. The method includes (i) determining a visual representation of the face and an audio representation, the visual representation includes crops of the face; (ii) modifying the crops of the face to obtain masked crops; (iii) obtaining a reference frame from the visual representation at a second timestamp; (iv) combining the masked crops at the first timestamp with the reference to obtain lower half crops; (v) training the machine learning model by providing historical lower half crops and historical audio representations as training data; (vi) generating lip-synced frames for the face to the target speech, and (vii) generating an in-sync lip-synced frames by the pre-trained lip-sync model.

Abstract

Transfer learning is a popular method for obtaining deep trained models for data-scarce face tasks such as head pose and emotion. However, current transfer learning methods are inefficient and time-consuming as they do not fully account for the relationships between related tasks. Moreover, the transferred model is large and computationally expensive. As an alternative, we propose ETL: a technique that efficiently transfers a pre-trained model to a new task by retaining only cross-task aware filters, resulting in a sparse transferred model. We demonstrate the effectiveness of ETL by transferring VGGFace, a popular face recognition model to four diverse face tasks. Our experiments show that we attain a size reduction up to 97% and an inference time reduction up to 94% while retaining 99.5% of the baseline transfer learning accuracy

Abstract

Deep learning has shown remarkable performances in image classification, including those of plants and leaves. However, high-performing networks in terms of accuracy may not be using the salient regions for making the prediction and could be prone to biases. This work proposes a neural network architecture incorporating handcrafted features and fusing them with the learned features. Using hybrid features provides better control and understanding of the feature space while leveraging deep learning capabilities. Furthermore, a new IoU-based metric is introduced to assess the CNN-based classifier’s performance based on the regions focused on making predictions. Experiments over multiple leaf disease datasets demonstrate the performance improvement with the model using hybrid features. Classification using hybrid features performed better than the baseline models in terms of P@1 and also on the IoU …

Abstract

Note-taking methods and devices have improved tremendously over the past few decades, and people are finding new ways to write notes and take photos. Automatic extraction, recognition, and retrieval are necessary to process the huge chunk of digitized document data. However, an important step in all of these pipelines is the pre-processing step, mainly image enhancement or clean-up, which enhances the text regions and suppresses the non-text regions. In this article, we look at the problem of image enhancement or clean-up on one such important class of images (i.e., mobile captured glass board images). We present a simple yet efficient algorithm using the concepts of classical image processing techniques to solve the problem, and the obtained results are promising in comparison to the Office Lens.