{"ID":2831762,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07698","arxiv_id":"2512.07698","title":"sim2art: Accurate Articulated Object Modeling from a Single Video using Synthetic Training Data Only","abstract":"Understanding articulated objects from monocular video is a crucial yet challenging task in robotics and digital twin creation. Existing methods often rely on complex multi-view setups, high-fidelity object scans, or fragile long-term point tracks that frequently fail in casual real-world captures. In this paper, we present sim2art, a data-driven framework that recovers the 3D part segmentation and joint parameters of articulated objects from a single monocular video captured by a freely moving camera. Our core insight is a robust representation based on per-frame surface point sampling, which we augment with short-term scene flow and DINOv3 semantic features. Unlike previous works that depend on error-prone long-term correspondences, our representation is easy to obtain and exhibits a negligible difference between simulation and reality without requiring domain adaptation. Also, by construction, our method relies on single-viewpoint visibility, ensuring that the geometric representation remains consistent across synthetic and real data despite noise and occlusions. Leveraging a suitable Transformer-based architecture, sim2art is trained exclusively on synthetic data yet generalizes strongly to real-world sequences. To address the lack of standardized benchmarks in the field, we introduce two datasets featuring a significantly higher diversity of object categories and instances than prior work. Our evaluations show that sim2art effectively handles large camera motions and complex articulations, outperforming state-of-the-art optimization-based and tracking-dependent methods. sim2art offers a scalable solution that can be easily extended to new object categories without the need for cumbersome real-world annotations. Project webpage: https://aartykov.github.io/sim2art/","short_abstract":"Understanding articulated objects from monocular video is a crucial yet challenging task in robotics and digital twin creation. Existing methods often rely on complex multi-view setups, high-fidelity object scans, or fragile long-term point tracks that frequently fail in casual real-world captures. In this paper, we pr...","url_abs":"https://arxiv.org/abs/2512.07698","url_pdf":"https://arxiv.org/pdf/2512.07698v3","authors":"[\"Arslan Artykov\",\"Tom Ravaud\",\"Corentin Sautier\",\"Vincent Lepetit\"]","published":"2025-12-08T16:38:30Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\"]","methods":"[\"Transformer\"]","has_code":false}