3D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimation

• URL: http://arxiv.org/abs/2502.04074v1
• Date: Thu, 06 Feb 2025 13:37:09 GMT
• Title: 3D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimation
• Authors: Yihua Cheng, Hengfei Wang, Zhongqun Zhang, Yang Yue, Bo Eun Kim, Feng Lu, Hyung Jin Chang,
• Abstract summary: We introduce a novel cross-task 2D gaze estimation approach, aiming to adapt a pre-trained 3D gaze estimation network for 2D gaze prediction on unseen devices. This task is highly challenging due to the domain gap between 3D and 2D gaze, unknown screen poses, and limited training data. We evaluate our method on MPIIGaze, EVE, and GazeCapture datasets, collected respectively on laptops, desktop computers, and mobile devices.
• Score: 27.51272922798475
• License:
• Abstract: 3D and 2D gaze estimation share the fundamental objective of capturing eye movements but are traditionally treated as two distinct research domains. In this paper, we introduce a novel cross-task few-shot 2D gaze estimation approach, aiming to adapt a pre-trained 3D gaze estimation network for 2D gaze prediction on unseen devices using only a few training images. This task is highly challenging due to the domain gap between 3D and 2D gaze, unknown screen poses, and limited training data. To address these challenges, we propose a novel framework that bridges the gap between 3D and 2D gaze. Our framework contains a physics-based differentiable projection module with learnable parameters to model screen poses and project 3D gaze into 2D gaze. The framework is fully differentiable and can integrate into existing 3D gaze networks without modifying their original architecture. Additionally, we introduce a dynamic pseudo-labelling strategy for flipped images, which is particularly challenging for 2D labels due to unknown screen poses. To overcome this, we reverse the projection process by converting 2D labels to 3D space, where flipping is performed. Notably, this 3D space is not aligned with the camera coordinate system, so we learn a dynamic transformation matrix to compensate for this misalignment. We evaluate our method on MPIIGaze, EVE, and GazeCapture datasets, collected respectively on laptops, desktop computers, and mobile devices. The superior performance highlights the effectiveness of our approach, and demonstrates its strong potential for real-world applications.

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