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Boxin Shi

5 papers indexed

arxivcs.CV2026-07-23

Stokes-Informed Diffusion for Robust Linear Polarization Estimation

Yidong Luo, Chenggong Li, Yuchao Feng, Boxin Shi, Junchao Zhang, Xin Yuan

Polarization cues benefit applications such as material detection and de-reflection, yet acquiring them typically requires dedicated hardware. This motivates us to estimate the linear polarization from a single RGB image. However, the task is inherently ill-posed, with the Angle…

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arxivcs.ROcs.CV2026-07-07

Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulation

Jiaming Liu, Qingpo Wuwu, Nuowei Han, Hao Chen, Zhuoyang Liu, Fan Fei, et al.

Recently, Vision-Language-Action (VLA) models have demonstrated strong generalization across diverse tasks. However, effective robotic manipulation in physical environments fundamentally requires geometric understanding and spatial reasoning. While some VLA approaches attempt to…

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arxivcs.CV2026-07-06

Video Generation Models Are Inherent Lighting Estimators

Ziqi Cai, Shuchen Weng, Kaiqi Liu, Zifeng Wang, Zhiquan Zhang, Minggui Teng, et al.

Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge. Recent video generation models can produce photorealistic scenes with complex lighting, possessing an inherent understanding of lighting. In this…

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arxivcs.RO2026-07-03

TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training

Shengbang Liu, Yueru Jia, Yuyang Yan, Jiaming Liu, Xinran Zhang, Qiuxuan Feng, et al.

Vision-Language-Action (VLA) models have shown promising generalization in robotic manipulation, but they still struggle with contact-rich tasks, where minor contact perturbations can cause unrecoverable failures that are hard to detect from vision alone. Since these failures are…

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arxivcs.CV2026-06-28

MAVIN: Multi-Shot Audio-Visual Generation with Customized Narrative Control

Kaiqi Liu, Yunyao Mao, Ziqi Cai, Zheng Geng, Jing Wang, Qiulin Wang, et al.

While recent generative models produce high-fidelity videos, they struggle with the complex narrative control required for coherent multi-shot audio-visual generation. Existing methods suffer from temporal misalignment, limited controllability, and incomplete scripting. In this p…

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