2023-02-08 更新
AniPixel: Towards Animatable Pixel-Aligned Human Avatar
Authors:Jinlong Fan, Jing Zhang, Zhi Hou, Dacheng Tao
Neural radiance field using pixel-aligned features can render photo-realistic novel views. However, when pixel-aligned features are directly introduced to human avatar reconstruction, the rendering can only be conducted for still humans, rather than animatable avatars. In this paper, we propose AniPixel, a novel animatable and generalizable human avatar reconstruction method that leverages pixel-aligned features for body geometry prediction and RGB color blending. Technically, to align the canonical space with the target space and the observation space, we propose a bidirectional neural skinning field based on skeleton-driven deformation to establish the target-to-canonical and canonical-to-observation correspondences. Then, we disentangle the canonical body geometry into a normalized neutral-sized body and a subject-specific residual for better generalizability. As the geometry and appearance are closely related, we introduce pixel-aligned features to facilitate the body geometry prediction and detailed surface normals to reinforce the RGB color blending. Moreover, we devise a pose-dependent and view direction-related shading module to represent the local illumination variance. Experiments show that our AniPixel renders comparable novel views while delivering better novel pose animation results than state-of-the-art methods. The code will be released.
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RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes
Authors:Bingchen Gong, Yuehao Wang, Xiaoguang Han, Qi Dou
Radiance fields have gradually become a main representation of media. Although its appearance editing has been studied, how to achieve view-consistent recoloring in an efficient manner is still under explored. We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields. Our key idea is to decompose the scene into a set of pure-colored layers, forming a palette. By this means, color manipulation can be conducted by altering the color components of the palette directly. To support efficient palette-based editing, the color of each layer needs to be as representative as possible. In the end, the problem is formulated as an optimization problem, where the layers and their blending weights are jointly optimized with the NeRF itself. Extensive experiments show that our jointly-optimized layer decomposition can be used against multiple backbones and produce photo-realistic recolored novel-view renderings. We demonstrate that RecolorNeRF outperforms baseline methods both quantitatively and qualitatively for color editing even in complex real-world scenes.
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