349 citations · 1.2k across the 27 of their papers we have counts for
28 papers
Unsupervised Low-light Image Enhancement with Decoupled Networks
Wei Xiong, Ding Liu, Xiaohui Shen +2
In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion. Conventional unsupervised learning-based approaches…
Human Motion Transfer from Poses in the Wild
Jian Ren, Menglei Chai, Sergey Tulyakov +3
In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video. It is a…
Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition
Tianlang Chen, Chen Fang, Xiaohui Shen +3
In this work, we propose a new solution to 3D human pose estimation in videos. Instead of directly regressing the 3D joint locations, we draw inspiration from the human skeleton an…
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Yifan Jiang, Xinyu Gong, Ding Liu +6
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As o…
Creative Procedural-Knowledge Extraction From Web Design Tutorials
Longqi Yang, Chen Fang, Hailin Jin +2
Complex design tasks often require performing diverse actions in a specific order. To (semi-)autonomously accomplish these tasks, applications need to understand and learn a wide r…
Multimodal Style Transfer via Graph Cuts
Yulun Zhang, Chen Fang, Yilin Wang +4
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alterna…
Dance Dance Generation: Motion Transfer for Internet Videos
Yipin Zhou, Zhaowen Wang, Chen Fang +2
This work presents computational methods for transferring body movements from one person to another with videos collected in the wild. Specifically, we train a personalized model o…
Im2Pencil: Controllable Pencil Illustration from Photographs
Yijun Li, Chen Fang, Aaron Hertzmann +2
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style. This is a challenging task due to multiple stroke types (e.g., outlin…
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Tao Zhou, Chen Fang, Zhaowen Wang +5
Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simula…
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Yijun Li, Chen Fang, Jimei Yang +3
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating co…
"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention
Tianlang Chen, Zhongping Zhang, Quanzeng You +4
Generating stylized captions for an image is an emerging topic in image captioning. Given an image as input, it requires the system to generate a caption that has a specific style…
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
Zheng Xu, Michael Wilber, Chen Fang +2
We propose a fast feed-forward network for arbitrary style transfer, which can generate stylized image for previously unseen content and style image pairs. Besides the traditional…
Visual to Sound: Generating Natural Sound for Videos in the Wild
Yipin Zhou, Zhaowen Wang, Chen Fang +2
As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world. Often correlate…
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Wang-Cheng Kang, Chen Fang, Zhaowen Wang +1
Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fas…
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Shuai Tang, Hailin Jin, Chen Fang +2
Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an a…
Trimming and Improving Skip-thought Vectors
Shuai Tang, Hailin Jin, Chen Fang +2
The skip-thought model has been proven to be effective at learning sentence representations and capturing sentence semantics. In this paper, we propose a suite of techniques to tri…
Rethinking Skip-thought: A Neighborhood based Approach
Shuai Tang, Hailin Jin, Chen Fang +2
We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as…
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
Wenqi Xian, Patsorn Sangkloy, Varun Agrawal +5
In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are t…
Universal Style Transfer via Feature Transforms
Yijun Li, Chen Fang, Jimei Yang +3
Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limite…
AMC: Attention guided Multi-modal Correlation Learning for Image Search
Kan Chen, Trung Bui, Fang Chen +2
Given a user's query, traditional image search systems rank images according to its relevance to a single modality (e.g., image content or surrounding text). Nowadays, an increasin…
BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
Michael J. Wilber, Chen Fang, Hailin Jin +3
Computer vision systems are designed to work well within the context of everyday photography. However, artists often render the world around them in ways that do not resemble photo…
Diversified Texture Synthesis with Feed-forward Networks
Yijun Li, Chen Fang, Jimei Yang +3
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generalit…
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
Patsorn Sangkloy, Jingwan Lu, Chen Fang +2
Recently, there have been several promising methods to generate realistic imagery from deep convolutional networks. These methods sidestep the traditional computer graphics renderi…
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Ruining He, Chen Fang, Zhaowen Wang +1
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand on…