EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos

• URL: http://arxiv.org/abs/2405.20030v2
• Date: Fri, 23 Aug 2024 10:55:54 GMT
• Title: EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos
• Authors: Masashi Hatano, Ryo Hachiuma, Hideo Saito,
• Abstract summary: Existing methods for forecasting 2D hand positions rely on visual representations and mainly focus on hand-object interactions. We propose EMAG, an ego-motion-aware and generalizable 2D hand forecasting method. Our model outperforms prior methods by 1.7% and 7.0% on intra and cross-dataset evaluations.
• Score: 9.340890244344497
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Predicting future human behavior from egocentric videos is a challenging but critical task for human intention understanding. Existing methods for forecasting 2D hand positions rely on visual representations and mainly focus on hand-object interactions. In this paper, we investigate the hand forecasting task and tackle two significant issues that persist in the existing methods: (1) 2D hand positions in future frames are severely affected by ego-motions in egocentric videos; (2) prediction based on visual information tends to overfit to background or scene textures, posing a challenge for generalization on novel scenes or human behaviors. To solve the aforementioned problems, we propose EMAG, an ego-motion-aware and generalizable 2D hand forecasting method. In response to the first problem, we propose a method that considers ego-motion, represented by a sequence of homography matrices of two consecutive frames. We further leverage modalities such as optical flow, trajectories of hands and interacting objects, and ego-motions, thereby alleviating the second issue. Extensive experiments on two large-scale egocentric video datasets, Ego4D and EPIC-Kitchens 55, verify the effectiveness of the proposed method. In particular, our model outperforms prior methods by 1.7% and 7.0% on intra and cross-dataset evaluations, respectively. Project page: https://masashi-hatano.github.io/EMAG/

Related papers

• EgoAvatar: Egocentric View-Driven and Photorealistic Full-body Avatars [56.56236652774294]
  We propose a person-specific egocentric telepresence approach, which jointly models the photoreal digital avatar while also driving it from a single egocentric video. Our experiments demonstrate a clear step towards egocentric and photoreal telepresence as our method outperforms baselines as well as competing methods.
  arXiv  Detail & Related papers  (2024-09-22T22:50:27Z)
• MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos [27.766405152248055]
  Hand trajectory prediction plays a vital role in comprehending human motion patterns. However, capturing high-level human intentions consistent with reasonable temporal causality is challenging when only egocentric videos are available. We propose a novel hand trajectory prediction method dubbed MADiff, which forecasts future hand waypoints with diffusion models.
  arXiv  Detail & Related papers  (2024-09-04T12:06:33Z)
• Object Aware Egocentric Online Action Detection [23.504280692701272]
  We introduce an Object-Aware Module that integrates egocentric-specific priors into existing Online Action Detection frameworks. Our work can be seamlessly integrated into existing models with minimal overhead and bring consistent performance enhancements.
  arXiv  Detail & Related papers  (2024-06-03T07:58:40Z)
• Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects [89.95728475983263]
  holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation. We design the HANDS23 challenge based on the AssemblyHands and ARCTIC datasets with carefully designed training and testing splits. Based on the results of the top submitted methods and more recent baselines on the leaderboards, we perform a thorough analysis on 3D hand(-object) reconstruction tasks.
  arXiv  Detail & Related papers  (2024-03-25T05:12:21Z)
• EgoGen: An Egocentric Synthetic Data Generator [53.32942235801499]
  EgoGen is a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. We demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views.
  arXiv  Detail & Related papers  (2024-01-16T18:55:22Z)
• 3D Human Pose Perception from Egocentric Stereo Videos [67.9563319914377]
  We propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. We will release UnrealEgo2, UnrealEgo-RW, and trained models on our project page.
  arXiv  Detail & Related papers  (2023-12-30T21:21:54Z)
• Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment [71.16699226211504]
  We propose to learn fine-grained action features that are invariant to the viewpoints by aligning egocentric and exocentric videos in time. To this end, we propose AE2, a self-supervised embedding approach with two key designs. For evaluation, we establish a benchmark for fine-grained video understanding in the ego-exo context.
  arXiv  Detail & Related papers  (2023-06-08T19:54:08Z)
• EgoHumans: An Egocentric 3D Multi-Human Benchmark [37.375846688453514]
  We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking. We propose a novel 3D capture setup to construct a comprehensive egocentric multi-human benchmark in the wild. We leverage consumer-grade wearable camera-equipped glasses for the egocentric view, which enables us to capture dynamic activities like playing tennis, fencing, volleyball, etc.
  arXiv  Detail & Related papers  (2023-05-25T21:37:36Z)
• Ego-Body Pose Estimation via Ego-Head Pose Estimation [22.08240141115053]
  Estimating 3D human motion from an egocentric video sequence plays a critical role in human behavior understanding and has various applications in VR/AR. We propose a new method, Ego-Body Pose Estimation via Ego-Head Pose Estimation (EgoEgo), which decomposes the problem into two stages, connected by the head motion as an intermediate representation. This disentanglement of head and body pose eliminates the need for training datasets with paired egocentric videos and 3D human motion.
  arXiv  Detail & Related papers  (2022-12-09T02:25:20Z)
• Egocentric Video-Language Pretraining [74.04740069230692]
  Video-Language Pretraining aims to learn transferable representation to advance a wide range of video-text downstream tasks. We exploit the recently released Ego4D dataset to pioneer Egocentric training along three directions. We demonstrate strong performance on five egocentric downstream tasks across three datasets.
  arXiv  Detail & Related papers  (2022-06-03T16:28:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.