While many animals exhibit impressive hopping capabilities, machines have struggled to match their performance. Current hopping robots face limitations in power density, energy efficiency, and control stability. Here, we present a parallel-elastic actuation mechanism with a reactive latch that optimizes energy transfer, enabling a legged robot to achieve hopping heights and continuity previously unattainable. This mechanism efficiently stores and releases energy, extending the actuation period over the aerial phase while minimizing stance time. Our robot achieves a maximum hopping height of 3.6 meters, surpassing both human and animal records while demonstrating sustained, high-frequency hopping cycles with minimal power requirement. By integrating inertia-based onboard sensorimotor autonomy, we demonstrate stable, controlled hopping in environments without external aid. These results represent a step toward bridging the performance gap between biological and robotic locomotion, with potential to influence the design of future legged systems.

This qualitative paper examines the needs of residents to remember a local cultural heritage site collaboratively.The study observes and then designs for intangible affordances supporting this memory recall using the photo-elicitation method with generative artificial intelligence imagery.Specifically, collaboration was observed when editing a panoramic photograph using generative artificial intelligence.These instances were rare, and were analyzed to highlight intangible affordances aiding collaborative memory recall and content creation.Afterward, five examples were correlated to influence the design of an immersive and responsive 360° theatre system using specific combinations of artificial intelligence models to aid subjective recall.In this immersive space, storyteller preferences for edited content are finally made available for new users, thereby affording new collaborative storytelling about a cultural heritage site.As a contribution, this study aids the work of designers and sociologists by showing the photo-elicitation method's crossdisciplinary relevance to designing for collaborative, yet intangible storytelling affordances.

We present I2V3D, a novel framework for animating static images into dynamic videos with precise 3D control, leveraging the strengths of both 3D geometry guidance and advanced generative models. Our approach combines the precision of a computer graphics pipeline, enabling accurate control over elements such as camera movement, object rotation, and character animation, with the visual fidelity of generative AI to produce high-quality videos from coarsely rendered inputs. To support animations with any initial start point and extended sequences, we adopt a two-stage generation process guided by 3D geometry: 1) 3D-Guided Keyframe Generation, where a customized image diffusion model refines rendered keyframes to ensure consistency and quality, and 2) 3D-Guided Video Interpolation, a training-free approach that generates smooth, high-quality video frames between keyframes using bidirectional guidance. Experimental results highlight the effectiveness of our framework in producing controllable, high-quality animations from single input images by harmonizing 3D geometry with generative models. The code for our framework will be publicly released.

As drones become interwoven in human activities, increasingly taking on tasks interpreted as creative and performative, such as choreographed light shows, there is emerging interest in understanding how drones and humans can perform together. Humans have different habits when performing with partners as opposed to solo. How do people adapt their behaviors and perspectives when improvising with robotic partners? To explore these questions, we conducted a study investigating dancer-drone interactions using a system of micro aerial vehicles designed to facilitate improvised solo and partnered dances. Through solo and tandem dances with one or two robots, we analyzed the performers’ perceived workflow from semi-structured interviews and quantified their movement patterns during the improvisation. We found that the dancers perceived drone movements through spatial metaphors like the ceiling and gravity, anthropomorphizing drones as props on a stage through position and generated sound. The dancers felt a greater connection in single-drone scenarios and showed heightened avoidance behavior in two-drone situations. Our work shows how a robotic system can energize human dancers to improvise individually and in pairs.

Scene graphs offer a structured, hierarchical representation of images, with nodes and edges symbolizing objects and the relationships among them. It can serve as a natural interface for image editing, dramatically improving precision and flexibility. Leveraging this benefit, we introduce a new framework that integrates large language model (LLM) with Text2Image generative model for scene graph-based image editing. This integration enables precise modifications at the object level and creative recomposition of scenes without compromising overall image integrity. Our approach involves two primary stages: 1) Utilizing a LLM-driven scene parser, we construct an image's scene graph, capturing key objects and their interrelationships, as well as parsing fine-grained attributes such as object masks and descriptions. These annotations facilitate concept learning with a fine-tuned diffusion model, representing each object with an optimized token and detailed description prompt. 2) During the image editing phase, a LLM editing controller guides the edits towards specific areas. These edits are then implemented by an attention-modulated diffusion editor, utilizing the fine-tuned model to perform object additions, deletions, replacements, and adjustments. Through extensive experiments, we demonstrate that our framework significantly outperforms existing image editing methods in terms of editing precision and scene aesthetics. Our code is available at https://bestzzhang.github.io/SGEdit.

Abstract Recent advancements in large‐scale text‐to‐image diffusion models have enabled many applications in image editing. However, none of these methods have been able to edit the layout of single existing images. To address this gap, we propose the first framework for layout editing of a single image while preserving its visual properties, thus allowing for continuous editing on a single image. Our approach is achieved through two key modules. First, to preserve the characteristics of multiple objects within an image, we disentangle the concepts of different objects and embed them into separate textual tokens using a novel method called masked textual inversion. Next, we propose a training‐free optimization method to perform layout control for a pre‐trained diffusion model, which allows us to regenerate images with learned concepts and align them with user‐specified layouts. As the first framework to edit the layout of existing images, we demonstrate that our method is effective and outperforms other baselines that were modified to support this task. Code is available at our project page.