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HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
Nataniel Ruiz;Yuanzhen Li;Varun Jampani;Wei Wei;Tingbo Hou;Yael Pritch;Neal Wadhwa;Michael Rubinstein;Kfir Aberman
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (14)
Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fidelity to their identities. However, the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment, and sto...Show More
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Soo Ye Kim;Kfir Aberman;Nori Kanazawa;Rahul Garg;Neal Wadhwa;Huiwen Chang;Nikhil Karnad;Munchurl Kim;Orly Liba
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (12)
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details. In this paper, we propose applying super-resolution to coarsely reconstructed outputs, refining them at high resolution, and then downscaling the output to the original resolution. By introducing high-resolution images to the refinement networ...Show More
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
Shumian Xin;Neal Wadhwa;Tianfan Xue;Jonathan T. Barron;Pratul P. Srinivasan;Jiawen Chen;Ioannis Gkioulekas;Rahul Garg
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (27)
We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image’s defocus map—the amount of defocus blur at each pixel—and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to assist with autofocus, and use them for recovery of defocus maps or all-in-focus images...Show More
Learned Dual-View Reflection Removal
Simon Niklaus;Xuaner Cecilia Zhang;Jonathan T. Barron;Neal Wadhwa;Rahul Garg;Feng Liu;Tianfan Xue
2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2021 | Conference Paper |
Cited by: Papers (10)
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remo...Show More
Learning to Autofocus
Charles Herrmann;Richard Strong Bowen;Neal Wadhwa;Rahul Garg;Qiurui He;Jonathan T. Barron;Ramin Zabih
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (36)
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, following [9]. Using this dataset, we apply modern deep classification models and an o...Show More
Learning Single Camera Depth Estimation Using Dual-Pixels
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (83)
Deep learning techniques have enabled rapid progress in monocular depth estimation, but their quality is limited by the ill-posed nature of the problem and the scarcity of high quality datasets. We estimate depth from a single cam-era by leveraging the dual-pixel auto-focus hardware that is increasingly common on modern camera sensors. Classic stereo algorithms and prior learning-based depth estim...Show More
Wireless Software Synchronization of Multiple Distributed Cameras
2019 IEEE International Conference on Computational Photography (ICCP)
Year: 2019 | Conference Paper |
Cited by: Papers (18)
We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi. Our method is entirely software-based, has only modest hardware requirements, and achieves an accuracy of less than 250 µs on unmodified commodity hardware. It does not use image content and synchronizes cameras prior to capture. The algorithm operates in...Show More
Aperture Supervision for Monocular Depth Estimation
Pratul P. Srinivasan;Rahul Garg;Neal Wadhwa;Ren Ng;Jonathan T. Barron
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enabl...Show More
Riesz pyramids for fast phase-based video magnification
Neal Wadhwa;Michael Rubinstein;Frédo Durand;William T. Freeman
2014 IEEE International Conference on Computational Photography (ICCP)
Year: 2014 | Conference Paper |
We present a new compact image pyramid representation, the Riesz pyramid, that can be used for real-time phase-based motion magnification. Our new representation is less overcomplete than even the smallest two orientation, octave-bandwidth complex steerable pyramid, and can be implemented using compact, efficient linear filters in the spatial domain. Motion-magnified videos produced with this new ...Show More
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