AGILE: Hand-Object Interaction Reconstruction from Video via Agentic Generation

TL;DR

AGILE introduces a novel framework for reconstructing hand-object interactions from monocular videos by using agentic generation and robust tracking, overcoming occlusion and initialization challenges.

Contribution

It shifts from traditional reconstruction to agentic generation guided by vision-language models, enabling robust, complete, and physically plausible interaction reconstructions without fragile SfM.

Findings

Outperforms baselines in geometric accuracy.

Demonstrates robustness on challenging in-the-wild sequences.

Produces simulation-ready assets validated via real-to-sim retargeting.

Abstract

Reconstructing dynamic hand-object interactions from monocular videos is critical for dexterous manipulation data collection and creating realistic digital twins for robotics and VR. However, current methods face two prohibitive barriers: (1) reliance on neural rendering often yields fragmented, non-simulation-ready geometries under heavy occlusion, and (2) dependence on brittle Structure-from-Motion (SfM) initialization leads to frequent failures on in-the-wild footage. To overcome these limitations, we introduce AGILE, a robust framework that shifts the paradigm from reconstruction to agentic generation for interaction learning. First, we employ an agentic pipeline where a Vision-Language Model (VLM) guides a generative model to synthesize a complete, watertight object mesh with high-fidelity texture, independent of video occlusions. Second, bypassing fragile SfM entirely, we propose a robust anchor-and-track strategy. We initialize the object pose at a single interaction onset frame using a foundation model and propagate it temporally by leveraging the strong visual similarity between our generated asset and video observations. Finally, a contact-aware optimization integrates semantic, geometric, and interaction stability constraints to enforce physical plausibility. Extensive experiments on HO3D, DexYCB, ARCTIC, and in-the-wild videos reveal that AGILE outperforms baselines in global geometric accuracy while demonstrating exceptional robustness on challenging sequences where prior arts frequently collapse. By prioritizing physical validity, our method produces simulation-ready assets validated via real-to-sim retargeting for robotic applications. Project page: https://agile-hoi.github.io.