225 citations · 1.4k across the 41 of their papers we have counts for
30 papers · 1 filter
MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes
Zhenhui Ye, Tianyun Zhong, Yi Ren +10
Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. Personalized TFG is a variant that emphasizes the perceptual identity sim…
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
Zhenhui Ye, Tianyun Zhong, Yi Ren +11
One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait vid…
GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation
Zhenhui Ye, Jinzheng He, Ziyue Jiang +7
Generating talking person portraits with arbitrary speech audio is a crucial problem in the field of digital human and metaverse. A modern talking face generation method is expecte…
Instance-aware Image Colorization
Jheng-Wei Su, Hung-Kuo Chu, Jia-Bin Huang
Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible col…
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Yun-Chun Chen, Po-Hsiang Huang, Li-Yu Yu +3
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intr…
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do no…
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang +1
Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories b…
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
Jinwoo Choi, Chen Gao, Joseph C. E. Messou +1
Human activities often occur in specific scene contexts, e.g., playing basketball on a basketball court. Training a model using existing video datasets thus inevitably captures and…
Guided Image-to-Image Translation with Bi-Directional Feature Transformation
Badour AlBahar, Jia-Bin Huang
We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided g…
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the t…
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao +4
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: 1) lack of aligned training pairs and 2) multiple poss…
A Closer Look at Few-shot Classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing…
Deep Paper Gestalt
Jia-Bin Huang
Recent years have witnessed a significant increase in the number of paper submissions to computer vision conferences. The sheer volume of paper submissions and the insufficient num…
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
Yuliang Zou, Zelun Luo, Jia-Bin Huang
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences. Existing…
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing
Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and…
VideoMatch: Matching based Video Object Segmentation
Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing
Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task u…
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
Chen Gao, Yuliang Zou, Jia-Bin Huang
Recent years have witnessed rapid progress in detecting and recognizing individual object instances. To understand the situation in a scene, however, computers need to recognize ho…
Learning Blind Video Temporal Consistency
Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang +3
Applying image processing algorithms independently to each frame of a video often leads to undesired inconsistent results over time. Developing temporally consistent video-based ex…
DeepMVS: Learning Multi-view Stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf +2
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set…
MaskRNN: Instance Level Video Object Segmentation
Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing
Instance level video object segmentation is an important technique for video editing and compression. To capture the temporal coherence, in this paper, we develop MaskRNN, a recurr…
Progressive Representation Adaptation for Weakly Supervised Object Localization
Dong Li, Jia-Bin Huang, Yali Li +2
We address the problem of weakly supervised object localization where only image-level annotations are available for training object detectors. Numerous methods have been proposed…
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Shun Zhang, Jia-Bin Huang, Jongwoo Lim +4
Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in sc…
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
Yen-Chen Lin, Ming-Yu Liu, Min Sun +1
Deep reinforcement learning has shown promising results in learning control policies for complex sequential decision-making tasks. However, these neural network-based policies are…
Joint Image Filtering with Deep Convolutional Networks
Yijun Li, Jia-Bin Huang, Narendra Ahuja +1
Joint image filters leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatia…