Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views
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.
695fa6e22450f142afb3c507
Xiang Zhang
claimed_verified
2026-01-09T08:36:00.258Z
657dc1576dc01435cd9029d8
/avatars/3bba11ac7659fce61aeaedf40e2057a8.svg
Xiang Zhang
XiangZ
user
XiangZ
695fa6e22450f142afb3c508
Yang Zhang
695fa6e22450f142afb3c509
Lukas Mehl
695fa6e22450f142afb3c50a
Markus Gross
695fa6e22450f142afb3c50b
Christopher Schroers
Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views
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.
HairGuard is a framework for recovering fine-grained soft boundary details in 3D vision tasks through specialized depth refinement and view synthesis techniques.
depth fixer network gated residual module depth-based forward warping generative scene painter color fuser monocular depth estimation stereo image conversion novel view synthesis