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Binghao Zhao

Publications and source records attributed to Binghao Zhao.

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Unimode material based low-frequency underwater acoustic isolation

Extremal materials are a specific class of Cauchy materials whose elasticity tensor has one or more zero eigenvalues. Each zero eigenvalue corresponds to a soft mode requiring zero strain energy, while non-zero eigenvalues correspond to hard modes that cost energy. According to the number, N, of zero eigenvalues, these materials can be referred to as unimode (N=1), bimode (N=2), etc. Extremal materials have enabled novel functions beyond conventional Cauchy media, e.g., phonon polarizers, Rayleigh wave isolators and underwater acoustic cloaks. These functions typically require a single extremal material. Interfaces between two extremal materials exhibit rich wave behaviors, yet have been seldom explored. Here, we proposed the concept of complementary extremal materials, i.e., the soft mode of one extremal material is a hard mode of the other. As one example, we study the interface between an isotropic unimode material and an isotropic bimode material. We show that the interface allows perfect mode conversion from longitudinal waves to transverse waves. A low-frequency underwater acoustic insulator based on complementary extremal materials is proposed. Our finding has been verified with designed metamaterials and using effective-medium modeling. This work demonstrates the potential of complementary extremal materials in controlling elastic wave polarization and waterborne sound.

physics.class-ph

HumanDiffusion: a Coarse-to-Fine Alignment Diffusion Framework for Controllable Text-Driven Person Image Generation

Text-driven person image generation is an emerging and challenging task in cross-modality image generation. Controllable person image generation promotes a wide range of applications such as digital human interaction and virtual try-on. However, previous methods mostly employ single-modality information as the prior condition (e.g. pose-guided person image generation), or utilize the preset words for text-driven human synthesis. Introducing a sentence composed of free words with an editable semantic pose map to describe person appearance is a more user-friendly way. In this paper, we propose HumanDiffusion, a coarse-to-fine alignment diffusion framework, for text-driven person image generation. Specifically, two collaborative modules are proposed, the Stylized Memory Retrieval (SMR) module for fine-grained feature distillation in data processing and the Multi-scale Cross-modality Alignment (MCA) module for coarse-to-fine feature alignment in diffusion. These two modules guarantee the alignment quality of the text and image, from image-level to feature-level, from low-resolution to high-resolution. As a result, HumanDiffusion realizes open-vocabulary person image generation with desired semantic poses. Extensive experiments conducted on DeepFashion demonstrate the superiority of our method compared with previous approaches. Moreover, better results could be obtained for complicated person images with various details and uncommon poses.

cs.CV