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Eurecat

Academic institutioneurope · es
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Research library4linked papers
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Selected work

Representative Papers

PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions

Mar 05, 2026

This work addresses the challenge of efficiently adapting general-purpose imitation policies to novel task objectives and constraints while maintaining data efficiency and deployment robustness. The authors propose an instruction-conditioned policy optimization framework that integrates imitation learning with reinforcement learning, leveraging natural language task descriptions to automatically generate reward functions. For the first time, this approach combines human feedback on intermediate trajectories with a Eureka-style reward generation mechanism to enable personalized policy refinement. Evaluated on simulated pick-and-place tasks, the method significantly outperforms feedback-free baselines, achieving enhanced robustness with reduced computational overhead and enabling efficient reuse of general policies across diverse task configurations.

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MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration

Aug 21, 2025

This work addresses the challenging problem of 3D human body reconstruction from sparse, uncalibrated multi-view RGB images depicting clothed subjects. Methodologically, we propose a retraining-free multi-view joint optimization framework built upon the SMPL-X parametric model. We design a cross-view consistent body optimization algorithm and introduce a front-back normal map integration mechanism to explicitly capture geometric details such as clothing wrinkles and hairstyles. Surface fidelity is further enhanced via multi-view joint fitting coupled with normal-guided geometric refinement. Experiments demonstrate that our method achieves superior reconstruction quality compared to single-view baselines—using only 2–4 uncalibrated views—and attains state-of-the-art performance among few-shot 3D human reconstruction approaches. The framework is both computationally efficient and highly generalizable across diverse clothing and pose configurations.

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Recent publications

Latest Papers

PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions

Mar 05, 2026

This work addresses the challenge of efficiently adapting general-purpose imitation policies to novel task objectives and constraints while maintaining data efficiency and deployment robustness. The authors propose an instruction-conditioned policy optimization framework that integrates imitation learning with reinforcement learning, leveraging natural language task descriptions to automatically generate reward functions. For the first time, this approach combines human feedback on intermediate trajectories with a Eureka-style reward generation mechanism to enable personalized policy refinement. Evaluated on simulated pick-and-place tasks, the method significantly outperforms feedback-free baselines, achieving enhanced robustness with reduced computational overhead and enabling efficient reuse of general policies across diverse task configurations.

0 citationsRead paper

MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration

Aug 21, 2025

This work addresses the challenging problem of 3D human body reconstruction from sparse, uncalibrated multi-view RGB images depicting clothed subjects. Methodologically, we propose a retraining-free multi-view joint optimization framework built upon the SMPL-X parametric model. We design a cross-view consistent body optimization algorithm and introduce a front-back normal map integration mechanism to explicitly capture geometric details such as clothing wrinkles and hairstyles. Surface fidelity is further enhanced via multi-view joint fitting coupled with normal-guided geometric refinement. Experiments demonstrate that our method achieves superior reconstruction quality compared to single-view baselines—using only 2–4 uncalibrated views—and attains state-of-the-art performance among few-shot 3D human reconstruction approaches. The framework is both computationally efficient and highly generalizable across diverse clothing and pose configurations.

0 citationsRead paper