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20232026
most citedFine-grained Appearance Transfer with Diffusion Models

1 citations · 3 across the 8 of their papers we have counts for

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cs.CV2026

SceneReGen: Generative Reconstruction of 3D Scenes from a Single Image

Zefan Tian, Yuteng Ye, Yiheng Zhang +5

Single-image 3D scene reconstruction must complete partially observed objects and place them coherently in a shared observation-aligned scene frame. Object-level generative priors…

cs.CV2026

MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts

Zheng Zhang, Qinchuan Zhang, Yuteng Ye +5

Generating high-quality textures for 3D assets is a challenging task. Existing multiview texture generation methods suffer from the multiview inconsistency and missing textures on…

cs.CV2025

Jigsaw3D: Disentangled 3D Style Transfer via Patch Shuffling and Masking

Yuteng Ye, Zheng Zhang, Qinchuan Zhang +5

Controllable 3D style transfer seeks to restyle a 3D asset so that its textures match a reference image while preserving the integrity and multi-view consistency. The prevalent met…

cs.CV2024

Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model

Hang Zhou, Jiale Cai, Yuteng Ye +5

A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recove…

cs.CV20231 cited

Fine-grained Appearance Transfer with Diffusion Models

Yuteng Ye, Guanwen Li, Hang Zhou +7

Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coher…