Search Results for author:
Found 12 papers, 4 papers with code
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
RealFill: Reference-Driven Generation for Authentic Image Completion
Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene.
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications.
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.
Learned Dual-View Reflection Removal
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.
Learning to Autofocus
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance.
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
Learning Single Camera Depth Estimation using Dual-Pixels
Using our approach, existing monocular depth estimation techniques can be effectively applied to dual-pixel data, and much smaller models can be constructed that still infer high quality depth.
Wireless Software Synchronization of Multiple Distributed Cameras
We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi.
Synthetic Depth-of-Field with a Single-Camera Mobile Phone
Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background.
Aperture Supervision for Monocular Depth Estimation
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.
