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cs.CV2026

GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion

Yeji Choi, Jinhyeok Choi, Jaewon Min +3

We present GeoFace, a geometry-constrained multi-view diffusion framework for consistent face generation from a single input. % While recent multi-view diffusion models achieve pho…

cs.CV2026

DA-Flow: Degradation-Aware Optical Flow Estimation with Diffusion Models

Jaewon Min, Jaeeun Lee, Yeji Choi +7

Optical flow models trained on high-quality data often degrade severely when confronted with real-world corruptions such as blur, noise, and compression artifacts. To overcome this…

cs.CV2025

Unified Diffusion Transformer for High-fidelity Text-Aware Image Restoration

Jin Hyeon Kim, Paul Hyunbin Cho, Claire Kim +5

Text-Aware Image Restoration (TAIR) aims to recover high-quality images from low-quality inputs containing degraded textual content. While diffusion models provide strong generativ…

cs.CV2025

Text-Aware Image Restoration with Diffusion Models

Jaewon Min, Jin Hyeon Kim, Paul Hyunbin Cho +6

Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to fait…

cs.CV2025

Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models

Donghoon Ahn, Jiwon Kang, Sanghyun Lee +7

Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approa…

cs.CV2024

A Noise is Worth Diffusion Guidance

Donghoon Ahn, Jiwon Kang, Sanghyun Lee +9

Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free gu…