9 citations · 27 across the 18 of their papers we have counts for
18 papers · 1 filter
FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-On
Shengyao Chen, Xianbing Sun, Liqing Zhang +1
Video virtual try-on aims to transfer a target garment onto a moving person across video frames. Current methods rely on human parsing masks or pose keypoints that frequently fail…
DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
Xianbing Sun, Jiahui Zhan, Liqing Zhang +1
Recent diffusion- and flow-based VTON methods achieve strong results with pretrained generative models, but their reliance on multi-step sampling incurs high inference cost, while…
Enhancing Domain Generalization in 3D Human Pose Estimation through Controllable Generative Augmentation
Xinhao Hu, Yiyi Zhang, Liqing Zhang +1
Pedestrian motion, due to its causal nature, is strongly influenced by domain gaps arising from discrepancies between training and testing data distributions. Focusing on 3D human…
Any3DAvatar: Fast and High-Quality Full-Head 3D Avatar Reconstruction from Single Portrait Image
Yujie Gao, Yao Xiao, Xiangnan Zhu +4
Reconstructing a complete 3D head from a single portrait remains challenging because existing methods still face a sharp quality-speed trade-off: high-fidelity pipelines often rely…
Towards Source-Aware Object Swapping with Initial Noise Perturbation
Jiahui Zhan, Xianbing Sun, Xiangnan Zhu +4
Object swapping aims to replace a source object in a scene with a reference object while preserving object fidelity, scene fidelity, and object-scene harmony. Existing methods eith…
AVGGT: Rethinking Global Attention for Accelerating VGGT
Xianbing Sun, Zhikai Zhu, Zhengyu Lou +5
Models such as VGGT and have shown strong multi-view 3D performance, but their heavy reliance on global self-attention results in high computational cost. Existing sparse-att…
FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on
Zheng Wang, Xianbing Sun, Shengyi Wu +5
Traditional virtual try-on methods primarily focus on the garment-to-person try-on task, which requires flat garment representations. In contrast, this paper introduces a novel app…
Self-Supervised Vision Transformer for Enhanced Virtual Clothes Try-On
Lingxiao Lu, Shengyi Wu, Haoxuan Sun +5
Virtual clothes try-on has emerged as a vital feature in online shopping, offering consumers a critical tool to visualize how clothing fits. In our research, we introduce an innova…
Dataset Distillation in Latent Space
Yuxuan Duan, Jianfu Zhang, Liqing Zhang
Dataset distillation (DD) is a newly emerging research area aiming at alleviating the heavy computational load in training models on large datasets. It tries to distill a large dat…
Virtual Accessory Try-On via Keypoint Hallucination
Junhong Gou, Bo Zhang, Li Niu +4
The virtual try-on task refers to fitting the clothes from one image onto another portrait image. In this paper, we focus on virtual accessory try-on, which fits accessory (e.g., g…
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
Yan Hong, Li Niu, Jianfu Zhang +1
Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-th…
Shadow Generation for Composite Image in Real-world Scenes
Yan Hong, Li Niu, Jianfu Zhang +1
Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible…
Deep Image Harmonization by Bridging the Reality Gap
Junyan Cao, Wenyan Cong, Li Niu +2
Image harmonization has been significantly advanced with large-scale harmonization dataset. However, the current way to build dataset is still labor-intensive, which adversely affe…
BargainNet: Background-Guided Domain Translation for Image Harmonization
Wenyan Cong, Li Niu, Jianfu Zhang +2
Image composition is a fundamental operation in image editing field. However, unharmonious foreground and background downgrade the quality of composite image. Image harmonization,…
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
Yan Hong, Li Niu, Jianfu Zhang +2
Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-th…
F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation
Yan Hong, Li Niu, Jianfu Zhang +3
In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and f…
Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets
Yan Hong, Li Niu, Jianfu Zhang +1
Multi-task Learning (MTL) for classification with disjoint datasets aims to explore MTL when one task only has one labeled dataset. In existing methods, for each task, the unlabele…
Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2night
Wenyan Cong, Jianfu Zhang, Li Niu +4
Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Imag…