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arXiv · 2408.10846

Harmonizing Attention: Training-free Texture-aware Geometry Transfer

Eito Ikuta·Yohan Lee·Akihiro Iohara·Yu Saito·Toshiyuki Tanaka
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Abstract

Extracting geometry features from photographic images independently of surface texture and transferring them onto different materials remains a complex challenge. In this study, we introduce Harmonizing Attention, a novel training-free approach that leverages diffusion models for texture-aware geometry transfer. Our method employs a simple yet effective modification of self-attention layers, allowing the model to query information from multiple reference images within these layers. This mechanism is seamlessly integrated into the inversion process as Texture-aligning Attention and into the generation process as Geometry-aligning Attention. This dual-attention approach ensures the effective capture and transfer of material-independent geometry features while maintaining material-specific textural continuity, all without the need for model fine-tuning.

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Eito Ikuta, Yohan Lee, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka. 2024-08-19. Harmonizing Attention: Training-free Texture-aware Geometry Transfer. https://arxiv.org/abs/2408.10846

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