92 citations · 291 across the 39 of their papers we have counts for
14 papers · 1 filter
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization
Cheng Yu, Haoyu Xie, Lei Shang +4
In the field of human-centric personalized image generation, the adapter-based method obtains the ability to customize and generate portraits by text-to-image training on facial da…
ControLRM: Fast and Controllable 3D Generation via Large Reconstruction Model
Hongbin Xu, Weitao Chen, Zhipeng Zhou +4
Despite recent advancements in 3D generation methods, achieving controllability still remains a challenging issue. Current approaches utilizing score-distillation sampling are hind…
Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning
Siyuan Li, Juanxi Tian, Zedong Wang +6
This paper delves into the interplay between vision backbones and optimizers, unvealing an inter-dependent phenomenon termed \textit{\textbf{b}ackbone-\textbf{o}ptimizer \textbf{c}…
TopoFR: A Closer Look at Topology Alignment on Face Recognition
Jun Dan, Yang Liu, Jiankang Deng +4
The field of face recognition (FR) has undergone significant advancements with the rise of deep learning. Recently, the success of unsupervised learning and graph neural networks h…
RobustMVS: Single Domain Generalized Deep Multi-view Stereo
Hongbin Xu, Weitao Chen, Baigui Sun +2
Despite the impressive performance of Multi-view Stereo (MVS) approaches given plenty of training samples, the performance degradation when generalizing to unseen domains has not b…
Dataset Growth
Ziheng Qin, Zhaopan Xu, Yukun Zhou +10
Deep learning benefits from the growing abundance of available data. Meanwhile, efficiently dealing with the growing data scale has become a challenge. Data publicly available are…
TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
Jiazheng Xing, Chao Xu, Yijie Qian +5
Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the cl…
FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio
Chao Xu, Yang Liu, Jiazheng Xing +8
In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and I…
Beyond Talking -- Generating Holistic 3D Human Dyadic Motion for Communication
Mingze Sun, Chao Xu, Xinyu Jiang +3
In this paper, we introduce an innovative task focused on human communication, aiming to generate 3D holistic human motions for both speakers and listeners. Central to our approach…
StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields
Hongbin Xu, Weitao Chen, Feng Xiao +2
4D style transfer aims at transferring arbitrary visual style to the synthesized novel views of a dynamic 4D scene with varying viewpoints and times. Existing efforts on 3D style t…
FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation
Pengchong Qiao, Lei Shang, Chang Liu +3
Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's p…
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
Haoyu Xie, Changqi Wang, Jian Zhao +4
Tremendous breakthroughs have been developed in Semi-Supervised Semantic Segmentation (S4) through contrastive learning. However, due to limited annotations, the guidance on unlabe…
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Siyuan Li, Zicheng Liu, Juanxi Tian +9
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DN…
Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
Siyuan Li, Luyuan Zhang, Zedong Wang +8
As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and…