Search Results for author: Jun Hao Liew

Found 29 papers, 16 papers with code

High Quality Human Image Animation using Regional Supervision and Motion Blur Condition

no code implementations29 Sep 2024 Zhongcong Xu, Chaoyue Song, Guoxian Song, Jianfeng Zhang, Jun Hao Liew, Hongyi Xu, You Xie, Linjie Luo, Guosheng Lin, Jiashi Feng, Mike Zheng Shou

Although generating reasonable results, existing methods often overlook the need for regional supervision in crucial areas such as the face and hands, and neglect the explicit modeling for motion blur, leading to unrealistic low-quality synthesis.

Human Animation Image Animation

DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention

1 code implementation28 May 2024 Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, Xinggang Wang

In this paper, we aim to incorporate the sub-quadratic modeling capability of Gated Linear Attention (GLA) into the 2D diffusion backbone.

Mamba

ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance

1 code implementation27 May 2024 Jiannan Huang, Jun Hao Liew, Hanshu Yan, Yuyang Yin, Yao Zhao, Yunchao Wei

Recent text-to-image customization works have been proven successful in generating images of given concepts by fine-tuning the diffusion models on a few examples.

Diffusion Personalization Video Generation

PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator

1 code implementation13 May 2024 Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, Jiashi Feng

We present Piecewise Rectified Flow (PeRFlow), a flow-based method for accelerating diffusion models.

MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation

no code implementations9 Jan 2024 Weimin WANG, Jiawei Liu, Zhijie Lin, Jiangqiao Yan, Shuo Chen, Chetwin Low, Tuyen Hoang, Jie Wu, Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng

The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.

MORPH Video Generation

SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process

1 code implementation NeurIPS 2023 Mengyu Wang, Henghui Ding, Jun Hao Liew, Jiajun Liu, Yao Zhao, Yunchao Wei

We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process.

Denoising Dichotomous Image Segmentation +4

Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method

1 code implementation19 Dec 2023 Jiachun Pan, Hanshu Yan, Jun Hao Liew, Jiashi Feng, Vincent Y. F. Tan

However, since the off-the-shelf pre-trained networks are trained on clean images, the one-step estimation procedure of the clean image may be inaccurate, especially in the early stages of the generation process in diffusion models.

Video Generation

AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text

no code implementations29 Nov 2023 Jianfeng Zhang, Xuanmeng Zhang, Huichao Zhang, Jun Hao Liew, Chenxu Zhang, Yi Yang, Jiashi Feng

We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions.

MagicAvatar: Multimodal Avatar Generation and Animation

no code implementations28 Aug 2023 Jianfeng Zhang, Hanshu Yan, Zhongcong Xu, Jiashi Feng, Jun Hao Liew

This report presents MagicAvatar, a framework for multimodal video generation and animation of human avatars.

Video Generation

MagicEdit: High-Fidelity and Temporally Coherent Video Editing

no code implementations28 Aug 2023 Jun Hao Liew, Hanshu Yan, Jianfeng Zhang, Zhongcong Xu, Jiashi Feng

In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task.

Translation Video Editing

AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models

1 code implementation20 Jul 2023 Jiachun Pan, Jun Hao Liew, Vincent Y. F. Tan, Jiashi Feng, Hanshu Yan

Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models (DPMs) with user-provided concepts.

Denoising

Delving Deeper into Data Scaling in Masked Image Modeling

no code implementations24 May 2023 Cheng-Ze Lu, Xiaojie Jin, Qibin Hou, Jun Hao Liew, Ming-Ming Cheng, Jiashi Feng

The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical.

Self-Supervised Learning

Global Knowledge Calibration for Fast Open-Vocabulary Segmentation

1 code implementation ICCV 2023 Kunyang Han, Yong liu, Jun Hao Liew, Henghui Ding, Yunchao Wei, Jiajun Liu, Yitong Wang, Yansong Tang, Yujiu Yang, Jiashi Feng, Yao Zhao

Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).

Knowledge Distillation Open Vocabulary Semantic Segmentation +4

PV3D: A 3D Generative Model for Portrait Video Generation

no code implementations13 Dec 2022 Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Wenqing Zhang, Song Bai, Jiashi Feng, Mike Zheng Shou

While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos.

Video Generation

MagicMix: Semantic Mixing with Diffusion Models

2 code implementations28 Oct 2022 Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng

Unlike style transfer, where an image is stylized according to the reference style without changing the image content, semantic blending mixes two different concepts in a semantic manner to synthesize a novel concept while preserving the spatial layout and geometry.

Denoising Style Transfer

SODAR: Segmenting Objects by DynamicallyAggregating Neighboring Mask Representations

1 code implementation15 Feb 2022 Tao Wang, Jun Hao Liew, Yu Li, Yunpeng Chen, Jiashi Feng

Unlike the original per grid cell object masks, SODAR is implicitly supervised to learn mask representations that encode geometric structure of nearby objects and complement adjacent representations with context.

Instance Segmentation Object +1

Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs

no code implementations CVPR 2021 Lile Cai, Xun Xu, Jun Hao Liew, Chuan Sheng Foo

Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.

Active Learning Semantic Segmentation +1

Body Meshes as Points

1 code implementation CVPR 2021 Jianfeng Zhang, Dongdong Yu, Jun Hao Liew, Xuecheng Nie, Jiashi Feng

In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.

3D Human Shape Estimation 3D Multi-Person Pose Estimation +1

AggMask: Exploring locally aggregated learning of mask representations for instance segmentation

1 code implementation1 Jan 2021 Tao Wang, Jun Hao Liew, Yu Li, Yunpeng Chen, Jiashi Feng

Recently proposed one-stage instance segmentation models (\emph{e. g.}, SOLO) learn to directly predict location-specific object mask with fully-convolutional networks.

Instance Segmentation Segmentation +1

Classification Calibration for Long-tail Instance Segmentation

1 code implementation29 Oct 2019 Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Jun Hao Liew, Sheng Tang, Steven Hoi, Jiashi Feng

In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals.

Classification General Classification +3

MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input

no code implementations ICCV 2019 Jun Hao Liew, Scott Cohen, Brian Price, Long Mai, Sim-Heng Ong, Jiashi Feng

Existing deep learning-based interactive image segmentation approaches typically assume the target-of-interest is always a single object and fail to account for the potential diversity in user expectations, thus requiring excessive user input when it comes to segmenting an object part or a group of objects instead.

Diversity Image Segmentation +4

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

5 code implementations ICCV 2019 Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, Jiashi Feng

In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.

Few-Shot Semantic Segmentation Metric Learning +2

Regional Interactive Image Segmentation Networks

no code implementations ICCV 2017 Jun Hao Liew, Yunchao Wei, Wei Xiong, Sim-Heng Ong, Jiashi Feng

The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained.

Ranked #10 on Interactive Segmentation on SBD (NoC@85 metric)

Image Segmentation Interactive Segmentation +2

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