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English(EN) DiTGaze: Geometry-Conditioned Diffusion Augmentation for Few-Shot Gaze Estimation

DiTGaze系统利用扩散模型增强少样本注视点估计

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DiTGaze，一种用于少样本注视点估计的新颖系统，该系统利用双流3D高斯泼溅骨干网络和几何条件扩散Transformer。该方法重新构建了渲染阶段，将高斯特征与潜在扩散模型集成，并通过自适应层归一化整合了时间步、身份和表情的条件。DiTGaze旨在通过改进少样本个性化来减轻收集大量用户特定注视点数据的负担，并在ETH-XGaze数据集上显示出显著的误差降低。 AI

影响 这项研究可能导致更高效、更准确的注视点跟踪系统，且所需的用户特定数据更少。

排序理由 这是一篇详细介绍注视点估计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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DiTGaze系统利用扩散模型增强少样本注视点估计

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这是一篇详细介绍注视点估计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Abiram Panchalingam, Indu P. Bodala, Stuart E. Middleton ·

    DiTGaze: Geometry-Conditioned Diffusion Augmentation for Few-Shot Gaze Estimation

    arXiv:2511.11231v2 Announce Type: replace Abstract: Accurate person-specific gaze estimation benefits from labeled user data, but collecting images that densely cover gaze directions is burdensome. We present DiTGaze, a calibration-efficient augmentation system that follows a two…

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