Abstract
Soft boundaries, like thin hairs, are commonly observed in natural and computer-generated imagery, but they remain challenging for 3D vision due to the ambiguous mixing of foreground and background cues. This paper introduces Guardians of the Hair (HairGuard), a framework designed to recover fine-grained soft boundary details in 3D vision tasks. Specifically, we first propose a novel data curation pipeline that leverages image matting datasets for training and design a depth fixer network to automatically identify soft boundary regions. With a gated residual module, the depth fixer refines depth precisely around soft boundaries while maintaining global depth quality, allowing plug-and-play integration with state-of-the-art depth models. For view synthesis, we perform depth-based forward warping to retain high-fidelity textures, followed by a generative scene painter that fills disoccluded regions and eliminates redundant background artifacts within soft boundaries. Finally, a color fuser adaptively combines warped and inpainted results to produce novel views with consistent geometry and fine-grained details. Extensive experiments demonstrate that HairGuard achieves state-of-the-art performance across monocular depth estimation, stereo image/video conversion, and novel view synthesis, with significant improvements in soft boundary regions.
Abstract (translated)
软边界（如稀疏的头发）在自然和计算机生成的图像中很常见，但在3D视觉任务中仍然极具挑战性，因为前景和背景线索之间的模糊混合使得准确捕捉这些细节变得困难。本文介绍了一种名为“守护者之发”(HairGuard) 的框架，旨在恢复3D视觉任务中的细粒度软边界细节。 具体来说，我们首先提出了一种新颖的数据整理流水线，利用图像遮罩数据集进行训练，并设计了一个深度修复网络来自动识别软边界区域。通过一个门控残差模块，该深度修复网络能够在不牺牲整体深度质量的前提下精确调整软边界的深度信息，从而使HairGuard能够与最先进的深度模型无缝集成。 对于视点合成任务，我们首先采用基于深度的前向扭曲技术保留高保真度纹理，并随后使用生成场景绘画器填充被遮挡区域并消除冗余背景伪影。最后，一个颜色融合器会根据需要将扭曲和填充结果结合在一起，以产生几何一致且细节丰富的新型视图。 通过广泛的实验表明，HairGuard在单目深度估计、立体图像/视频转换以及新颖视点合成等多个任务中均实现了当前最佳性能，并特别在软边界区域显示了显著的改进。
URL
https://arxiv.org/abs/2601.03362
