Modern text-to-image generation systems have enabled the creation of remarkably realistic and high-quality visuals, yet they often falter when handling the inherent ambiguities in user prompts. In this work, we present Twin-Co, a framework that leverages synchronized, co-adaptive dialogue to progressively refine image generation. Instead of a static generation process, Twin-Co employs a dynamic, iterative workflow where an intelligent dialogue agent continuously interacts with the user. Initially, a base image is generated from the user's prompt. Then, through a series of synchronized dialogue exchanges, the system adapts and optimizes the image according to evolving user feedback. The co-adaptive process allows the system to progressively narrow down ambiguities and better align with user intent. Experiments demonstrate that Twin-Co not only enhances user experience by reducing trial-and-error iterations but also improves the quality of the generated images, streamlining creative process across various applications.

Residential design is a complex and open-ended problem that requires designers to integrate diverse types of input information while adhering to stringent energy consumption standards. However, most current research in this field focuses on generating floor plans from a limited set of input types, often neglecting to incorporate energy-related physical constraints. Existing approaches are limited by: (1) the lack of multimodal datasets in this domain, (2) the absence of comprehensive residential energy consumption data, and (3) the challenges associated with effectively integrating multiple input types into a unified model. To address these challenges, we propose MRED-14, the first large-scale Multimodal Residential Energy Dataset, comprising 14 input types, including energy consumption values, vector drawings, and textual descriptions, paired with 41,280 high-quality residential floor plans that have been scored and annotated by human experts. Based on this dataset, we introduce the LER-net model, which can flexibly adapt to various input types and generate low-energy residential floor plans. Experimental results demonstrate that LER-net outperforms existing models, achieving state-of-the-art performance under the same input conditions. In addition, the energy consumption of the generated floor plans is reduced by 5.1% compared to the actual residential designs. Further expert evaluations confirm the LER-net model's feasibility for use in residential design.

Images captured by robotics under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-light images is of paramount importance for practical applications in the computer vision domain. To effectively address these challenges, we present a novel low-light image enhancement model, termed Spatial Consistency Retinex Network (SCRNet), which leverages the Retinex-based structure and is guided by the principle of spatial consistency. Specifically, our proposed model incorporates three levels of consistency: channel level, semantic level, and texture level, inspired by the principle of spatial consistency. These levels of consistency enable our model to adaptively enhance image features, ensuring more accurate and visually pleasing results. Extensive experimental evaluations on various low-light image datasets demonstrate that our proposed SCRNet outshines existing state-of-the-art methods, highlighting the potential of SCRNet as an effective solution for enhancing low-light images.