Abstract Acquiring physically plausible motor skills across diverse and unconventional morphologies—from humanoids to ants—is crucial for robotics and simulation. We introduce No-data Imitation Learning (NIL), which: Generates a reference video with a pretrained video diffusion model from a single simulation frame + text prompt.

Abstract We present HaPTIC, an approach that infers coherent 4D hand trajectories from monocular videos. Current video-based hand pose reconstruction methods primarily focus on improving frame-wise 3D pose using adjacent frames rather than studying consistent 4D hand trajectories in space.

Abstract Estimating sewing patterns from images is a practical approach for creating high-quality 3D garments. Due to the lack of real-world pattern-image paired data, prior approaches fine-tune large vision-language models (VLMs) on synthetic garment datasets, limiting their generalization to real-world images.

Abstract Hands play a central role in daily life, yet modeling natural hand motions remains underexplored. Existing methods that tackle text-to-hand-motion generation or hand animation captioning rely on studio-captured datasets with limited actions and contexts, making them costly to scale to in-the-wild settings.

Abstract Hands are central to interacting with our surroundings and conveying gestures, making their inclusion essential for full-body motion synthesis. Yet existing methods either ignore hands entirely or operate under highly constrained conditions.

Abstract Human-like motion requires human-like perception. We introduce CLOPS, the first human avatar that solely uses egocentric vision to perceive its surroundings and navigate 3D scenes. Rather than relying on task-specific perception methods or privileged state information, CLOPS argues that human-like avatar behavior requires human-like perception.

Abstract While current general-purpose 3D human models (e.g., SMPL-X) efficiently represent accurate human shape and pose, they lack the ability to physically interact with the environment due to their kinematic nature.

Abstract We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images, enabling accurate human-object joint reconstruction in 3D. This is challenging due to occlusions, depth ambiguities, and widely varying object shapes.