We introduce SemanticDraw, a new paradigm of interactive content creation where high-quality images are generated in near real-time from given multiple hand-drawn regions, each encoding prescribed semantic meaning. In order to maximize the productivity of content creators and to fully realize their artistic imagination, it requires both quick interactive interfaces and fine-grained regional controls in their tools. Despite astonishing generation quality from recent diffusion models, we find that existing approaches for regional controllability are very slow (52 seconds for 512 × 512 image) while not compatible with acceleration methods such as LCM, blocking their huge potential in interactive content creation. From this observation, we build our solution for interactive content creation in two steps: (1) we establish compatibility between region-based controls and acceleration techniques for diffusion models, maintaining high fidelity of multi-prompt image generation with ×10 reduced number of inference steps, (2) we increase the generation throughput with our new multi-prompt stream batch pipeline, enabling low-latency generation from multiple, region-based text prompts on a single RTX 2080 Ti GPU. Our proposed framework is generalizable to any existing diffusion models and acceleration schedulers, allowing sub-second (0.64 seconds) image content creation application upon well-established image diffusion models. The code is https://github.com/ironjr/semantic-draw.

Generating high-quality 3D scenes is a critical challenge in computer vision, driven by advances in 3D graphics and the growing demand for immersive environments. While object-centric 3D generation has achieved significant progress, scene generation remains difficult due to the scarcity of large-scale 3D scene datasets and scalability constraints of conventional 3D representations, which hinder efficient large-scale expansion. To address these challenges, we propose LucidDreamer, a novel pipeline that synthesizes diverse, high-quality, and expandable 3D scenes using a unified 3D Gaussian splatting representation. Our approach employs an iterative Navigation-Dreaming-Alignment process, leveraging 2D image generation and depth estimation to construct photorealistic, scalable 3D environments. By iteratively generating images and navigating through the scene, LucidDreamer fully utilizes the power of image generation models, enabling the creation of highly detailed and expandable 3D scenes. LucidDreamer supports various input modalities, including text, RGB, and RGBD, and enables dynamic modifications during generation. Experimental results demonstrate that LucidDreamer outperforms existing methods in generating high-quality, diverse, structurally consistent, and navigable 3D scenes.

Convolutional neural networks are data models. Their design should embrace the structural properties of the data being modeled, e.g., the natural images. We argue that this also holds for discriminators of adversarial training frameworks for photo-realistic image restoration. We develop this idea to highlight three essential structural features of natural images: translation equivariance, rotation invariance, and hierarchy of scale. The analysis leads to a new discriminator, Structure-Resonant Discriminator (SRD), which can capture image structures in need. The proposed SRD is demonstrated in the perceptual single image super-resolution task. By replacing only the discriminator, our method restores more visually pleasing high-resolution images than the previous state-of-the-art techniques, while exhibits the least distortions.