Search Results for author: Guilin Liu

Found 21 papers, 10 papers with code

DiffiT: Diffusion Vision Transformers for Image Generation

1 code implementation4 Dec 2023 Ali Hatamizadeh, Jiaming Song, Guilin Liu, Jan Kautz, Arash Vahdat

We also introduce latent DiffiT which consists of transformer model with the proposed self-attention layers, for high-resolution image generation.

Denoising Image Generation

Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models

no code implementations ICCV 2023 Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia-Bin Huang, Ming-Yu Liu, Yogesh Balaji

Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy.

Image Generation Text-to-Video Generation +1

View Generalization for Single Image Textured 3D Models

no code implementations CVPR 2021 Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro

We describe a cycle consistency loss that encourages model textures to be aligned, so as to encourage sharing.

Neural FFTs for Universal Texture Image Synthesis

no code implementations NeurIPS 2020 Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro

The conventional CNNs, recently adopted for synthesis, require to train and test on the same set of images and fail to generalize to unseen images.

Image Generation Texture Synthesis

Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter

no code implementations14 Jul 2020 Guilin Liu, Rohan Taori, Ting-Chun Wang, Zhiding Yu, Shiqiu Liu, Fitsum A. Reda, Karan Sapra, Andrew Tao, Bryan Catanzaro

Specifically, we directly treat the whole encoded feature map of the input texture as transposed convolution filters and the features' self-similarity map, which captures the auto-correlation information, as input to the transposed convolution.

Texture Synthesis

Panoptic-based Image Synthesis

no code implementations CVPR 2020 Aysegul Dundar, Karan Sapra, Guilin Liu, Andrew Tao, Bryan Catanzaro

Conditional image synthesis for generating photorealistic images serves various applications for content editing to content generation.

Image Generation

Few-shot Video-to-Video Synthesis

6 code implementations NeurIPS 2019 Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Jan Kautz, Bryan Catanzaro

To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time.

Test Video-to-Video Synthesis

Unsupervised Video Interpolation Using Cycle Consistency

1 code implementation ICCV 2019 Fitsum A. Reda, Deqing Sun, Aysegul Dundar, Mohammad Shoeybi, Guilin Liu, Kevin J. Shih, Andrew Tao, Jan Kautz, Bryan Catanzaro

We further introduce a pseudo supervised loss term that enforces the interpolated frames to be consistent with predictions of a pre-trained interpolation model.

 Ranked #1 on Video Frame Interpolation on UCF101 (PSNR (sRGB) metric)

Video Frame Interpolation

Partial Convolution based Padding

4 code implementations28 Nov 2018 Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro

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.

General Classification Semantic Segmentation

SDCNet: Video Prediction Using Spatially-Displaced Convolution

1 code implementation2 Nov 2018 Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker, David Tarjan, Andrew Tao, Bryan Catanzaro

We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.

Optical Flow Estimation SSIM +1

Video-to-Video Synthesis

11 code implementations NeurIPS 2018 Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, Bryan Catanzaro

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 output photorealistic video that precisely depicts the content of the source video.

Semantic Segmentation Video Prediction +1

Image Inpainting for Irregular Holes Using Partial Convolutions

59 code implementations ECCV 2018 Guilin Liu, Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro

Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value).

Image Inpainting valid

Material Editing Using a Physically Based Rendering Network

no code implementations ICCV 2017 Guilin Liu, Duygu Ceylan, Ersin Yumer, Jimei Yang, Jyh-Ming Lien

We propose an end-to-end network architecture that replicates the forward image formation process to accomplish this task.

Image Generation

Symmetry-aware Depth Estimation using Deep Neural Networks

no code implementations20 Apr 2016 Guilin Liu, Chao Yang, Zimo Li, Duygu Ceylan, Qi-Xing Huang

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.

Depth Estimation

Continuous Visibility Feature

no code implementations CVPR 2015 Guilin Liu, Yotam Gingold, Jyh-Ming Lien

We say that a point q on the mesh is continuously visible from another point p if there exists a geodesic path connecting p and q that is entirely visible by p. In order to efficiently estimate the continuous visibility for all the vertices in a model, we propose two approaches that use specific CVF properties to avoid exhaustive visibility tests.


Dual-Space Decomposition of 2D Complex Shapes

no code implementations CVPR 2014 Guilin Liu, Zhonghua Xi, Jyh-Ming Lien

In this paper, we propose a new decomposition method, called Dual-space Decomposition that handles complex 2D shapes by recognizing the importance of holes and classifying holes as either topological noise or structurally important features.

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