Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model
원문 초록
While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains largely unexplored. Due to the intricate tiny patterns and repeated geometric sub-structures in most ornaments, it is much more difficult to guarantee identity and appearance consistency under large pose and scale variances between ornaments and models. This paper proposes the task of virtual try-on for ornaments and presents a method to improve the geometric and appearance preservation of ornament virtual try-ons. Specifically, we estimate an accurate wearing mask to improve the alignments between ornaments and models in an iterative scheme alongside the denoising process. To preserve structure details, we further regularize attention layers to map the reference ornament mask to the wearing mask in an implicit way. Experimental results demonstrate that our method successfully wears ornaments from reference images onto target models, handling substantial differences in scale and pose while preserving identity and achieving realistic visual effects.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
Energy-Based Transformers are Scalable Learners and Thinkers
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
토론
이 논문과 연결된 포럼 글이 아직 없습니다.