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Tali Dekel

Tali Dekel

I'm a Senior Research Scientist at Google, Cambridge, developing algorithms at the intersection of computer vision and computer graphics. Before Google, I was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William T. Freeman. I completed my Ph.D studies at the school of electrical engineering, Tel-Aviv University, Israel, under the supervision of Prof. Shai Avidan, and Prof. Yael Moses. My research interests include computational photography, image synthesize, geometry and 3D reconstruction.

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    Teaching CLIP to Count to Ten
    Michal Irani
    Roni Paiss
    Shiran Zada
    Submission to CVPR 2023 (2023)
    Preview abstract Large vision-language models, such as CLIP, learn robust representations of text and images, facilitating advances in many downstream tasks, including zero-shot classification and text-to-image generation. However, these models have several well-documented limitations. They fail to encapsulate compositional concepts, such as counting objects in an image or the relations between objects. To the best of our knowledge, this work is the first to extend CLIP to handle object counting. We introduce a simple yet effective method to improve the quantitative understanding of vision-language models, while maintaining their overall performance on common benchmarks. Our method automatically augments image captions to create hard negative samples that differ from the original captions by only the number of objects. For example, an image of three dogs can be contrasted with the negative caption "Six dogs playing in the yard". A dedicated loss encourages discrimination between the correct caption and its negative variant. We introduce CountBench, a new benchmark for evaluating a model's understanding of object counting, and demonstrate significant improvement over baseline models on this task. Furthermore, we leverage our improved CLIP representations for image generation, and show that our model can produce specific counts of objects more reliably than existing ones. View details
    Preview abstract Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently limited to simple edits (e.g., painting something on an object), are applied to synthetically generated images, or require multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex non-rigid edits to a single real image -- i.e., change the pose of an object inside a real image, while preserving the remaining parts of the image. Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc. -- each within its single high-resolution natural image provided by the user. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the scene/object). Our method, which we call Imagic, leverages a pre-trained text-to-image diffusion model for this task. It modifies the text embedding to satisfy both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing high quality complex image edits. View details
    Self-Distilled StyleGAN: Towards Generation from Internet Photos
    Ron Mokady
    Michal Irani
    Proceedings of the 49th Annual Conference on Computer Graphics and Interactive Techniques (2022)
    Preview abstract StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited set of datasets, which are usually structurally aligned and well curated. In this paper, we show how StyleGAN can be adapted to work on raw uncurated images collected from the Internet. Such image collections impose two main challenges to StyleGAN: they contain many outlier images, and are characterized by a multi-modal distribution. Training StyleGAN on such raw image collections results in degraded image synthesis quality. To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate out-of-distribution images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN’s “truncation trick” in the image synthesis process. The presented technique enables the generation of high-quality images, while better reserving the diversity of the data. Through qualitative and quantitative evaluation, we demonstrate the power of our approach to new challenging and diverse domains collected from the Internet. New datasets and pre-trained models will be published upon acceptance. View details
    Preview abstract We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained object recognition model. Inspired by classical image pyramid representations, we construct our model as a Semantic Generation Pyramid -- a hierarchical framework which leverages the continuum of semantic information encapsulated in such deep features; this ranges from low level information contained in fine features to high level, semantic information contained in deeper features. More specifically, given a set of features extracted from a reference image, our model generates diverse image samples, each with matching features at each semantic level of the recognition model. We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks. These include: generating images with a controllable extent of semantic similarity to a reference image, and different manipulation tasks such as semantically-controlled inpainting and compositing; all achieved with the same model, with no further training. View details
    Semantic Pyramid for Image Generation
    Assaf Shocher
    Yossi Gandelsman
    Michal Irani
    Proc. IEEE Computer Vision and Pattern Recognition (CVPR) (2020)
    Preview abstract We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Inspired by classical image pyramid representations, we construct our model as a Semantic Generation Pyramid - a hierarchical framework which leverages the continuum of semantic information encapsulated in such deep features; this ranges from low level information contained in fine features to high level, semantic information contained in deeper features. More specifically, given a set of features extracted from a reference image, our model generates diverse image samples, each with matching features at each semantic level of the classification model. We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks. These include: generating images with a controllable extent of semantic similarity to a reference image, and different manipulation tasks such as semantically-controlled inpainting and compositing; all achieved with the same model, with no further training. View details
    Preview abstract We present a method for retiming people in an ordinary, natural video---manipulating and editing the time in which different motions of individuals in the video occur. We can temporally align different motions, change the speed of certain actions (speeding up/slowing down, or entirely "freezing" people), or "erase" selected people from the video altogether. We achieve these effects computationally via a dedicated learning-based layered video representation, where each frame in the video is decomposed into separate RGBA layers, representing the appearance of different people in the video. A key property of our model is that it not only disentangles the direct motions of each person in the input video, but also correlates each person automatically with the scene changes they generate---e.g., shadows, reflections, and motion of loose clothing. The layers can be individually retimed and recombined into a new video, allowing us to achieve realistic, high-quality renderings of retiming effects for real-world videos depicting complex actions and involving multiple individuals, including dancing, trampoline jumping, or group running. View details
    Preview abstract We wish to automatically predict the "speediness" of moving objects in videos---whether they move faster, at, or slower than their "natural" speed. The core component in our approach is SpeedNet---a novel deep network trained to detect if a video is playing at normal rate, or if it is sped up. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. We demonstrate prediction results by SpeedNet on a wide range of videos containing complex natural motions, and examine the visual cues it utilizes for making those predictions. Importantly, we show that through predicting the speed of videos, the model learns a powerful and meaningful space-time representation that goes beyond simple motion cues. We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly. View details
    Learning the Depths of Moving People by Watching Frozen People
    Zhengqi Li
    Ce Liu
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
    Preview abstract We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving. Existing methods for recovering depth for dynamic, non-rigid objects from monocular video impose strong assumptions on the objects' motion and often can recover only a sparse depth. In this paper, we take a data-driven approach and learn human depth priors from a large corpus of data. Specifically, we use a new source of data comprised of thousands of Internet videos in which people imitate mannequins, i.e., people freeze in diverse, natural poses, while a hand-held camera is touring the scene. We then create training data using modern Multi-View Stereo (MVS) methods, and design a model that is applied to dynamic scene at inference time. Our method makes use of motion parallax beyond single view and shows clear advantages over state-of-the-art monocular depth prediction methods. We demonstrate the applicability of our method on real-world sequences captured by a moving hand-held camera, depicting complex human actions. We show various 3D effects such as re-focusing, creating a stereoscopic video from a monocular one, and inserting virtual objects to the scene, all produced using our predicted depth maps. View details
    MoSculp: Interactive Visualization of Shape and Time
    Andrew Owens
    Jiajun Wu
    Qiurui He
    Tianfan Xue
    Xiuming Zhang
    stefanie mueller
    UIST'18 (2018)
    Preview abstract We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space. Given an input video, our system computes a motion sculpture and provides the user with an interface for rendering it in different styles, including the options to insert the sculpture back into the source video or render it in a synthetic scene. To provide this end-to-end workflow, we introduce an algorithm that estimates a human's 3D geometry over time and a 3D-aware image-based rendering approach that preserves the depth ordering of their body motions. By automating the process, our system takes motion sculpture creation out of the realm of professional artists, and makes it applicable to a wide range of existing video material. By providing viewers with 3D information, motion sculptures reveal space-time motion information that is difficult to perceive with the naked eye, and allow viewers to interpret how different parts of the object interact over time. We validate the effectiveness of this approach with user studies, finding that our motion sculpture visualizations are significantly more informative about motion than existing stroboscopic and space-time visualization methods. View details
    Preview abstract We present a joint audio-visual model for isolating a single speech signal from a mixture of sounds such as other speakers and background noise. Solving this task using only audio as input is extremely challenging and does not provide an association of the separated speech signals with speakers in the video. In this paper, we present a deep network-based model that incorporates both visual and auditory signals to solve this task. The visual features are used to "focus" the audio on desired speakers in a scene and to improve the speech separation quality. To train our joint audio-visual model, we introduce AVSpeech, a new dataset comprised of thousands of hours of video segments from the Web. We demonstrate the applicability of our method to classic speech separation tasks, as well as real-world scenarios involving heated interviews, noisy bars, and screaming children, only requiring the user to specify the face of the person in the video whose speech they want to isolate. Our method shows clear advantage over state-of-the-art audio-only speech separation in cases of mixed speech. In addition, our model, which is speaker-independent (trained once, applicable to any speaker), produces better results than recent audio-visual speech separation methods that are speaker-dependent (require training a separate model for each speaker of interest). View details
    Sparse, Smart Contours to Represent and Edit Images
    Ce Liu
    Chuang Gan
    Dilip Krishnan
    Computer Vision and Pattern Recognition (2018)
    Preview abstract We study the problem of reconstructing an image from information stored at sparse contour locations comprising less than $6\%$ of image pixels. This extremely sparse representation provides an intuitive interface for semantically-aware image manipulation. Local edits in contour domain translate to long-range and coherent changes in pixel space. We use generative adversarial networks to synthesize texture and structure even in regions where no input information is provided. With our setup, we can perform complex structural changes such as changing facial expression and interpolating animal fur texture by simple edits of contours such as scaling, moving and erasing. Experiments on a variety of datasets verify the versatility and convenience afforded by our models. View details
    Preview abstract We present a model for isolating and enhancing speech of desired speakers in a video. The input is a video with one or more people speaking, where the speech of interest is interfered by other speakers and/or background noise. We leverage both audio and visual features for this task, which are fed into a joint audio-visual source separation model we designed and trained using thousands of hours of video segments with clean speech from our new dataset, AVSpeech-90K. We present results for various real, practical scenarios involving heated debates and interviews, noisy bars and screaming children, only requiring users to specify the face of the person in the video whose speech they would like to isolate. View details
    On the Effectiveness of Visible Watermarks
    Ce Liu
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
    Preview abstract Visible watermarking is a widely-used technique for marking and protecting copyrights of many millions of images on the web, yet it suffers from an inherent security flaw—watermarks are typically added in a consistent manner to many images. We show that this consistency allows to automatically estimate the watermark and recover the original images with high accuracy. Specifically, we present a generalized multi-image matting algorithm that takes a watermarked image collection as input and automatically estimates the “foreground” (watermark), its alpha matte, and the “background” (original) images. Since such an attack relies on the consistency of watermarks across image collection, we explore and evaluate how it is affected by various types of inconsistencies in the watermark embedding that could potentially be used to make watermarking more secured. We demonstrate the algorithm on stock imagery available on the web, and provide extensive quantitative analysis on synthetic watermarked data. A key takeaway message of this paper is that visible watermarks should be designed to not only be robust against removal from a single image, but to be more resistant to mass-scale removal from image collections as well. View details
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