1.6k citations · 3.8k across the 20 of their papers we have counts for
22 papers
AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
Yifan Jiang, Peter Hedman, Ben Mildenhall +4
Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent e…
Fast and High-Quality Image Denoising via Malleable Convolutions
Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall +3
Most image denoising networks apply a single set of static convolutional kernels across the entire input image. This is sub-optimal for natural images, as they often consist of het…
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
Shumian Xin, Neal Wadhwa, Tianfan Xue +5
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…
How to Train Neural Networks for Flare Removal
Yicheng Wu, Qiurui He, Tianfan Xue +4
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color ble…
Real-time Localized Photorealistic Video Style Transfer
Xide Xia, Tianfan Xue, Wei-sheng Lai +4
We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photore…
Learned Dual-View Reflection Removal
Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron +4
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 inconv…
Neural Light Transport for Relighting and View Synthesis
Xiuming Zhang, Sean Fanello, Yun-Ta Tsai +10
The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel…
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Xide Xia, Meng Zhang, Tianfan Xue +4
Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent app…
Handheld Mobile Photography in Very Low Light
Orly Liba, Kiran Murthy, Yun-Ta Tsai +10
Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise…
Stereoscopic Dark Flash for Low-light Photography
Jian Wang, Tianfan Xue, Jonathan T. Barron +1
In this work, we present a camera configuration for acquiring "stereoscopic dark flash" images: a simultaneous stereo pair in which one camera is a conventional RGB sensor, but the…
Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue +3
Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which a…
Seeing Tree Structure from Vibration
Tianfan Xue, Jiajun Wu, Zhoutong Zhang +3
Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance…
MoSculp: Interactive Visualization of Shape and Time
Xiuming Zhang, Tali Dekel, Tianfan Xue +5
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…
Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
Tianfan Xue, Jiajun Wu, Katherine L. Bouman +1
We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministi…
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Xingyuan Sun, Jiajun Wu, Xiuming Zhang +5
We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pix…
3D Interpreter Networks for Viewer-Centered Wireframe Modeling
Jiajun Wu, Tianfan Xue, Joseph J. Lim +4
Understanding 3D object structure from a single image is an important but challenging task in computer vision, mostly due to the lack of 3D object annotations to real images. Previ…
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu, Yifan Wang, Tianfan Xue +3
3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-…
Video Enhancement with Task-Oriented Flow
Tianfan Xue, Baian Chen, Jiajun Wu +2
Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a su…
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue +2
We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space…
Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors
Shaul Oron, Tali Dekel, Tianfan Xue +2
We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity mea…
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
Tianfan Xue, Jiajun Wu, Katherine L. Bouman +1
We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a determinis…
Single Image 3D Interpreter Network
Jiajun Wu, Tianfan Xue, Joseph J. Lim +4
Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previou…