1 citations · 1 across the 14 of their papers we have counts for
15 papers
PIE-PS: Photometric Stereo from Physical Irradiance Event Streams
Xiangze Meng, Guangyu Li, Jing Li +4
Event cameras record asynchronous log-image-irradiance changes with microsecond latency and high dynamic range. These properties are useful for photometric stereo under moving illu…
STBridge: Shared-Target Alignment for Bridging Understanding and Generation in UMMs
Ye Wang, Hongjun Wang, Hao Fang +7
Unified multimodal models (UMMs) aim to integrate visual understanding and generation within a single architecture, but architectural unification alone does not ensure semantic con…
RealSkin: Spatio-Spectral Partial Neural Adjoint Maps for Image-to-3D Attribute Transfer
Jing Li, Yawei Luo, Xiangze Meng +3
Creating photorealistic 3D assets requires bridging the appearance gap between real-world observations and synthetic models. A promising approach is to transfer visual attributes f…
Physics-Aware 3D Gaussian Editing for Driving Scene Generation
Feng Zhou, Jian Zhang, Yuhang Sun +5
3D Gaussian Splatting (3DGS) has shown great potential in autonomous driving simulation and data generation, enabling photorealistic reconstruction and flexible scene manipulation.…
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…
PairHuman: A High-Fidelity Photographic Dataset for Customized Dual-Person Generation
Ting Pan, Ye Wang, Peiguang Jing +3
Personalized dual-person portrait customization has considerable potential applications, such as preserving emotional memories and facilitating wedding photography planning. Howeve…
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…
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…
DP-Adapter: Dual-Pathway Adapter for Boosting Fidelity and Text Consistency in Customizable Human Image Generation
Ye Wang, Xuping Xie, Lanjun Wang +2
With the growing popularity of personalized human content creation and sharing, there is a rising demand for advanced techniques in customized human image generation. However, curr…
SigStyle: Signature Style Transfer via Personalized Text-to-Image Models
Ye Wang, Tongyuan Bai, Xuping Xie +3
Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despi…
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…
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation
Peng Zheng, Tao Liu, Zili Yi +1
With the development of neural radiance fields and generative models, numerous methods have been proposed for learning 3D human generation from 2D images. These methods allow contr…
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…