Wonjung Woo

Junyoung Lee, Sookwan Han, Jeonghwan Kim et al.

Human-robot collaboration has been studied primarily in dyadic or sequential settings. However, real homes require multiadic collaboration, where multiple humans and robots share a workspace, acting concurrently on interleaved subtasks with tight spatial and temporal coupling. This regime remains underexplored because close-proximity interaction between humans, robots, and objects creates persistent occlusion and rapid state changes, making reliable real-time 3D tracking the central bottleneck. No existing platform provides the real-time, occlusion-robust, room-scale perception needed to make this regime experimentally tractable. We present OmniRobotHome, the first room-scale residential platform that unifies wide-area real-time 3D human and object perception with coordinated multi-robot actuation in a shared world frame. The system instruments a natural home environment with 48 hardware-synchronized RGB cameras for markerless, occlusion-robust tracking of multiple humans and objects, temporally aligned with two Franka arms that act on live scene state. Continuous capture within this consistent frame further supports long-horizon human behavior modeling from accumulated trajectories. The platform makes the multiadic collaboration regime experimentally tractable. We focus on two central problems: safety in shared human-robot environments and human-anticipatory robotic assistance, and show that real-time perception and accumulated behavior memory each yield measurable gains in both.

Hyunsoo Cha, Wonjung Woo, Byungjun Kim et al.

We present Vanast, a unified framework that generates garment-transferred human animation videos directly from a single human image, garment images, and a pose guidance video. Conventional two-stage pipelines treat image-based virtual try-on and pose-driven animation as separate processes, which often results in identity drift, garment distortion, and front-back inconsistency. Our model addresses these issues by performing the entire process in a single unified step to achieve coherent synthesis. To enable this setting, we construct large-scale triplet supervision. Our data generation pipeline includes generating identity-preserving human images in alternative outfits that differ from garment catalog images, capturing full upper and lower garment triplets to overcome the single-garment-posed video pair limitation, and assembling diverse in-the-wild triplets without requiring garment catalog images. We further introduce a Dual Module architecture for video diffusion transformers to stabilize training, preserve pretrained generative quality, and improve garment accuracy, pose adherence, and identity preservation while supporting zero-shot garment interpolation. Together, these contributions allow Vanast to produce high-fidelity, identity-consistent animation across a wide range of garment types.