43-Issue 2

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https://diglib.eg.org/handle/10.2312/3606944

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Browsing 43-Issue 2 by Subject "3D imaging"

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    HaLo-NeRF: Learning Geometry-Guided Semantics for Exploring Unconstrained Photo Collections
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Dudai, Chen; Alper, Morris; Bezalel, Hana; Hanocka, Rana; Lang, Itai; Averbuch-Elor, Hadar; Bermano, Amit H.; Kalogerakis, Evangelos
    Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine-grained understanding. In more constrained 3D domains, recent methods have leveraged modern vision-and-language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain and fail to exploit the geometric consistency of images capturing multiple views of such scenes. In this work, we present a localization system that connects neural representations of scenes depicting large-scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision-and-language models with adaptations for understanding landmark scene semantics. To bolster such models with fine-grained knowledge, we leverage large-scale Internet data containing images of similar landmarks along with weakly-related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D-compatible segmentation that ultimately lifts to a volumetric scene representation. To evaluate our method, we present a new benchmark dataset containing large-scale scenes with groundtruth segmentations for multiple semantic concepts. Our results show that HaLo-NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our code and data are publicly available at https://tau-vailab.github.io/HaLo-NeRF/.
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    ShellNeRF: Learning a Controllable High-resolution Model of the Eye and Periocular Region
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Gengyan; Sarkar, Kripasindhu; Meka, Abhimitra; Buehler, Marcel; Mueller, Franziska; Gotardo, Paulo; Hilliges, Otmar; Beeler, Thabo; Bermano, Amit H.; Kalogerakis, Evangelos
    Eye gaze and expressions are crucial non-verbal signals in face-to-face communication. Visual effects and telepresence demand significant improvements in personalized tracking, animation, and synthesis of the eye region to achieve true immersion. Morphable face models, in combination with coordinate-based neural volumetric representations, show promise in solving the difficult problem of reconstructing intricate geometry (eyelashes) and synthesizing photorealistic appearance variations (wrinkles and specularities) of eye performances. We propose a novel hybrid representation - ShellNeRF - that builds a discretized volume around a 3DMM face mesh using concentric surfaces to model the deformable 'periocular' region. We define a canonical space using the UV layout of the shells that constrains the space of dense correspondence search. Combined with an explicit eyeball mesh for modeling corneal light-transport, our model allows for animatable photorealistic 3D synthesis of the whole eye region. Using multi-view video input, we demonstrate significant improvements over state-of-the-art in expression re-enactment and transfer for high-resolution close-up views of the eye region.