1 citations · 1 across the 8 of their papers we have counts for
10 papers
A training-free framework for high-fidelity appearance transfer via diffusion transformers
Shengrong Gu, Ye Wang, Song Wu +4
Diffusion Transformers (DiTs) excel at generation, but their global self-attention makes controllable, reference-image-based editing a distinct challenge. Unlike U-Nets, naively in…
RefracGS: Novel View Synthesis Through Refractive Water Surfaces with 3D Gaussian Ray Tracing
Yiming Shao, Qiyu Dai, Chong Gao +6
Novel view synthesis (NVS) through non-planar refractive surfaces presents fundamental challenges due to severe, spatially varying optical distortions. While recent representations…
OmniStyle2: Learning to Stylize by Learning to Destylize
Ye Wang, Zili Yi, Yibo Zhang +6
This paper introduces a scalable paradigm for supervised style transfer by inverting the problem: instead of learning to stylize directly, we learn to destylize, reducing stylistic…
FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization
Peng Zheng, Ye Wang, Rui Ma +1
Subject-driven image generation plays a crucial role in applications such as virtual try-on and poster design. Existing approaches typically fine-tune pretrained generative models…
OmniStyle: Filtering High Quality Style Transfer Data at Scale
Ye Wang, Ruiqi Liu, Jiang Lin +4
In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style c…
Interpretable Style Takagi-Sugeno-Kang Fuzzy Clustering
Suhang Gu, Ye Wang, Yongxin Chou +3
Clustering is an efficient and essential technique for exploring latent knowledge of data. However, limited attention has been given to the interpretability of the clusters detecte…
OSTAF: A One-Shot Tuning Method for Improved Attribute-Focused T2I Personalization
Ye Wang, Zili Yi, Rui Ma
Personalized text-to-image (T2I) models not only produce lifelike and varied visuals but also allow users to tailor the images to fit their personal taste. These personalization te…
TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization
Renyi Mao, Qingshan Xu, Peng Zheng +3
Coordinate-based neural implicit representation or implicit fields have been widely studied for 3D geometry representation or novel view synthesis. Recently, a series of efforts ha…
3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis
Ruiqi Liu, Peng Zheng, Ye Wang +1
Existing 3D-aware portrait synthesis methods can generate impressive high-quality images while preserving strong 3D consistency. However, most of them cannot support the fine-grain…
P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion
Linlian Jiang, Pan Chen, Ye Wang +2
Inferring missing regions from severely occluded point clouds is highly challenging. Especially for 3D shapes with rich geometry and structure details, inherent ambiguities of the…