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(1/2) ๐Ÿ“ข๐Ÿ“ข๐Ÿ“ขNeRSemble #SIGGRAPH'23! We reconstruct dynamic radiance fields for high-quality novel view synthesis of human heads. Key is a deformation field and an ensemble of multi-resolution hash encodings to model coarse & fine-scale deformations.

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Matthias Niessner ็š„ๅคดๅƒ
Matthias Niessner3 ๅนดๅ‰

(2/2) To train our model, we record a novel multi-view video dataset from 16 cameras containing over 4700 sequences of human heads covering a variety of facial expressions. The dataset will be publicly available to advance research on high-fidelity human head reconstruction.

Tobias Kirschstein ็š„ๅคดๅƒ
Tobias Kirschstein3 ๅนดๅ‰

Super excited to present NeRSemble at #SIGGRAPH2023 in the NeRFs for Avatars session happening tomorrow (Thursday) 3:45pm in Petree Hall C! Come by to chat about human-centric dynamic radiance fields and other face-related 3D topics!

Bobcat ็š„ๅคดๅƒ
Bobcat3 ๅนดๅ‰

That is just insane. You'll be outperforming ground truth in no time

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SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes abs: paper page: Existing methods for the 4D reconstruction of general, non-rigidly deforming objects focus on novel-view synthesis and neglect correspondences. However, time consistency enables advanced downstream tasks like 3D editing, motion analysis, or virtual-asset creation. We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our dynamic-NeRF method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. It then reconstructs the deformations of an estimated canonical model of the geometry and appearance in an online fashion. Since this canonical model is time-invariant, we obtain correspondences even for long-term, long-range motions. We employ neural scene representations to parametrize the components of our method. Like prior dynamic-NeRF methods, we use a backwards deformation model. We find non-trivial adaptations of this model necessary to handle larger motions: We decompose the deformations into a strongly regularized coarse component and a weakly regularized fine component, where the coarse component also extends the deformation field into the space surrounding the object, which enables tracking over time. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.

AK

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