2022-04-05 更新
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis
Authors:Tianhan Xu, Yasuhiro Fujita, Eiichi Matsumoto
We propose a new method for reconstructing controllable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene representation on the mesh surface points and signed distances from the surface of a human body mesh. We identify an indistinguishability issue that arises when a point in 3D space is mapped to its nearest surface point on a mesh for learning surface-aligned neural scene representation. To address this issue, we propose projecting a point onto a mesh surface using a barycentric interpolation with modified vertex normals. Experiments with the ZJU-MoCap and Human3.6M datasets show that our approach achieves a higher quality in a novel-view and novel-pose synthesis than existing methods. We also demonstrate that our method easily supports the control of body shape and clothes. Project page: https://pfnet-research.github.io/surface-aligned-nerf/.
PDF CVPR 2022. Project page: https://pfnet-research.github.io/surface-aligned-nerf/
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Animatable Neural Radiance Fields from Monocular RGB-D
Authors:Tiantian Wang, Nikolaos Sarafianos, Ming-Hsuan Yang, Tony Tung
This paper aims at representing animatable photo-realistic humans under novel views and poses. Recent work has shown significant progress with dynamic scenes by exploring shared canonical neural radiance fields. However learning a user-controlled model for novel poses remains a challenging task. To tackle this problem, we introduce a novel method to integrate observations across frames and encode the appearance at each individual frame by utilizing the human pose that models the body shape and point clouds which cover partial part of the human as the input. Specifically, our method simultaneously learns a shared set of latent codes anchored to the human pose among frames, and learns an appearance-dependent code anchored to incomplete point clouds generated by monocular RGB-D at each frame. A human pose-based code models the shape of the performer whereas a point cloud based code predicts details and reasons about missing structures at the unseen poses. To further recover non-visible regions in query frames, we utilize a temporal transformer to integrate features of points in query frames and tracked body points from automatically-selected key frames. Experiments on various sequences of humans in motion show that our method significantly outperforms existing works under unseen poses and novel views given monocular RGB-D videos as input.
PDF 16 pages
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SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image
Authors:Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang
Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. While several recent works have attempted to address this issue, they either operate with sparse views (yet still, a few of them) or on simple objects/scenes. In this work, we consider a more ambitious task: training neural radiance field, over realistically complex visual scenes, by “looking only once”, i.e., using only a single view. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. Specifically, SinNeRF constructs a semi-supervised learning process, where we introduce and propagate geometry pseudo labels and semantic pseudo labels to guide the progressive training process. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. We show that even without pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. Under the single image setting, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases. Project page: https://vita-group.github.io/SinNeRF/
PDF Project page: https://vita-group.github.io/SinNeRF/
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