← Back to all news
arXiv Computer Vision By Minqiang Zou, Riqiang Jin, Zhi Lv, Dong Luo, Lianghai Tian, Zhenyu Zhao, Qi Xu, Tong Wu, Mochen Yu, Yao Tang

GOLF: Global Observation with Local Focus for Calibration-Aware Stereo Interaction Field Estimation

Read the original on arXiv Computer Vision →

GOLF is a method that won the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. It predicts a 3D vector from each of 21 hand joints to the nearest point on a manipulated object using synchronized egocentric stereo views. The approach fuses dense global context, locally sampled hand/object evidence, and Plücker‑ray geometry, and employs a DINOv3 ViT‑H+/16 backbone with LoRA and trainable LayerNorm, achieving top scores on the hidden test set.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Oct 15

ESTHER: Egocentric Stereo Hand Estimation and Reconstruction in the Wild

ESTHER is an end‑to‑end model for egocentric stereo hand reconstruction that integrates stereo geometry, temporal reasoning, and a suitable output representation for wearable cameras. It is trained on pseudo‑labels from a calibrated pipeline and introduces the ESTHER3D benchmark, which pairs a large in‑the‑wild training set with a motion‑capture test set of true metric ground truth. Experiments demonstrate state‑of‑the‑art accuracy, strong generalization, and robustness to missing views, dropped frames, and extreme lighting or motion blur, while preserving metric scale even when reduced to a single monocular view.

By Hongyu Ma, Hairong Qu, Shiqi Zhao, Yongsong Yang, Peng Yin
roboticsfine-tuningbenchmarks
More like this →
arXiv Computer Vision
5d ago

EgoHSI3R: Online 4D Hand-Scene Interaction Reconstruction from Egocentric Video

EgoHSI3R is a unified feed‑forward framework that reconstructs 4D hand‑scene interactions from egocentric monocular video. It jointly estimates camera motion, dense scene geometry, world‑space bilateral MANO hands, and per‑vertex hand contact by maintaining a recurrent scene representation and using hand‑centric prompts and MANO vertex queries. The method achieves the lowest world‑space hand joint error on ARCTIC while offering faster inference than existing pipelines.

By Mingzhi Pei, Zhangchen Ye, Ruoqu Chen, Liu Cao, Mengdi Xu
robotics
More like this →
arXiv AI
Oct 31

SituHOI: Scalable Capture of Scene-Situated Full-Body Human-Object Interaction

SituHOI is a large‑scale RGB‑D dataset that captures full‑body human‑object interactions in 16 indoor rooms, comprising 18,429 annotated clips and about 86 hours of activity. The dataset pairs moving‑camera recordings with static scene scans, object geometry, language descriptions, and estimated human and object trajectories, all registered in a shared floor‑aligned frame via a pipeline that integrates camera localization, depth‑based alignment, human mesh recovery, and object tracking. The data enable the FlexHOI‑Bench benchmark for goal‑conditioned joint motion prediction, and a reference predictor, SharedHOI, demonstrates strong performance on world‑space accuracy and human‑object coordination. whyItMatters":"The dataset provides scalable, scene‑aligned supervision for modeling human‑object interaction, addressing a key limitation of existing datasets and enabling more accurate motion prediction benchmarks."

By Qifan Wang, Qihang Fang, Boao Shi, Yushi Du, Pei Zhou, Siyan Dong, Yanchao Yang
benchmarkssafety
More like this →