Generating Fine Details of Entity Interactions

Images not only depict objects but also encapsulate rich interactions between them. However, generating faithful and high-fidelity images involving multiple entities interacting with each other, is a long-standing challenge. While pre-trained text-to-image models are trained on large-scale datasets to follow diverse text instructions, they struggle to generate accurate interactions, likely due to the scarcity of training data for uncommon object interactions. This paper introduces InterActing, an interaction-focused dataset with 1000 fine-grained prompts covering three key scenarios: (1) functional and action-based interactions, (2) compositional spatial relationships, and (3) multi-subject interactions. To address interaction generation challenges, we propose a decomposition-augmented refinement procedure. Our approach, DetailScribe, built on Stable Diffusion 3.5, leverages LLMs to decompose interactions into finer-grained concepts, uses a VLM to critique generated images, and applies targeted interventions within the diffusion process in refinement. Automatic and human evaluations show significantly improved image quality, demonstrating the potential of enhanced inference strategies. Our dataset and code are available at https://concepts-ai.com/p/detailscribe/ to facilitate future exploration of interaction-rich image generation.

Channel Coding Toward 6G: Technical Overview and Outlook

Channel coding plays a pivotal role in ensuring reliable communication over wireless channels. With the growing need for ultra-reliable communication in emerging wireless use cases, the significance of channel coding has amplified. Furthermore, minimizing decoding latency is crucial for critical-mission applications, while optimizing energy efficiency is paramount for mobile and the Internet of Things (IoT) communications. As the fifth generation (5G) of mobile communications is currently in operation and 5G-advanced is on the horizon, the objective of this paper is to assess prominent channel coding schemes in the context of recent advancements and the anticipated requirements for the sixth generation (6G). In this paper, after considering the potential impact of channel coding on key performance indicators (KPIs) of wireless networks, we review the evolution of mobile communication standards and the organizations involved in the standardization, from the first generation (1G) to the current 5G, highlighting the technologies integral to achieving targeted KPIs such as reliability, data rate, latency, energy efficiency, spectral efficiency, connection density, and traffic capacity. Following this, we delve into the anticipated requirements for potential use cases in 6G. The subsequent sections of the paper focus on a comprehensive review of three primary coding schemes utilized in past generations and their recent advancements: low-density parity-check (LDPC) codes, turbo codes (including convolutional codes), polar codes (alongside Reed-Muller codes). Additionally, we examine alternative coding schemes like Fountain codes and sparse regression codes. Our evaluation includes a comparative analysis of error correction performance and the performance of hardware implementation for these coding schemes, providing insights into their potential and suitability for the upcoming 6G era.