123 citations · 253 across the 11 of their papers we have counts for
15 papers
Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models
Songwei Ge, Seungjun Nah, Guilin Liu +7
Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coheren…
View Generalization for Single Image Textured 3D Models
Anand Bhattad, Aysegul Dundar, Guilin Liu +2
Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view gene…
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
Shiyi Lan, Zhiding Yu, Christopher Choy +5
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensem…
Dual Contrastive Loss and Attention for GANs
Ning Yu, Guilin Liu, Aysegul Dundar +4
Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Yet generated images are still eas…
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
Guilin Liu, Rohan Taori, Ting-Chun Wang +6
Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and up/down-sampling layers, where each layer operates locally and lacks the ability to capture th…
Panoptic-based Image Synthesis
Aysegul Dundar, Karan Sapra, Guilin Liu +2
Conditional image synthesis for generating photorealistic images serves various applications for content editing to content generation. Previous conditional image synthesis algorit…
Few-shot Video-to-Video Synthesis
Ting-Chun Wang, Ming-Yu Liu, Andrew Tao +3
Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the sta…
Unsupervised Video Interpolation Using Cycle Consistency
Fitsum A. Reda, Deqing Sun, Aysegul Dundar +6
Learning to synthesize high frame rate videos via interpolation requires large quantities of high frame rate training videos, which, however, are scarce, especially at high resolut…
Neural Inverse Rendering of an Indoor Scene from a Single Image
Soumyadip Sengupta, Jinwei Gu, Kihwan Kim +3
Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single…
Partial Convolution based Padding
Guilin Liu, Kevin J. Shih, Ting-Chun Wang +5
In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks. We call it partial convolution base…
SDCNet: Video Prediction Using Spatially-Displaced Convolution
Fitsum A. Reda, Guilin Liu, Kevin J. Shih +5
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows. Previous approaches rely on resampling past frames, gu…
Video-to-Video Synthesis
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu +4
We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an outpu…
Image Inpainting for Irregular Holes Using Partial Convolutions
Guilin Liu, Fitsum A. Reda, Kevin J. Shih +3
Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid…
Material Editing Using a Physically Based Rendering Network
Guilin Liu, Duygu Ceylan, Ersin Yumer +2
The ability to edit materials of objects in images is desirable by many content creators. However, this is an extremely challenging task as it requires to disentangle intrinsic phy…
Symmetry-aware Depth Estimation using Deep Neural Networks
Guilin Liu, Chao Yang, Zimo Li +2
Due to the abundance of 2D product images from the Internet, developing efficient and scalable algorithms to recover the missing depth information is central to many applications.…