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arXiv 2407.04345cs.CV

CanonicalFusion:从多张图像生成可驱动的3D人体化身

CanonicalFusion: Generating Drivable 3D Human Avatars from Multiple Images

GIST AI Graduate School(光州科学技术院人工智能研究生院);Korea Electronics Technology Institute (KETI)(韩国电子技术研究院);polygom;POSTECH(浦项科技大学)

Jisu Shin, Junmyeong Lee, Seongmin Lee, Min-Gyu Park, Ju-Mi Kang, Ju Hong Yoon, Hae-Gon Jeon

AI总结:

该研究提出名为CanonicalFusion的人体化身重建框架,通过共享编码器-双解码器网络预测压缩蒙皮权重与深度图,结合前向蒙皮可微渲染融合多图结果,优化网格与姿态参数,提升多视图可驱动3D人体重建效果。

摘要:

我们提出了一种从多张图像重建可驱动人体化身的新型框架,名为CanonicalFusion。我们的核心思路是将单张图像的重建结果整合到规范空间中。具体而言,我们首先使用共享编码器-双解码器网络预测线性混合蒙皮(Linear Blend Skinning,LBS)权重图和深度图,从而能够根据预测的深度图直接对3D网格进行规范化处理。在此过程中,我们并非预测高维权重,而是借助预训练的MLP网络推断压缩后的蒙皮权重,即三维向量。我们还提出了一种基于前向蒙皮的可微渲染方案,用于融合多张图像的重建结果。该方案通过前向蒙皮对规范网格进行重姿态调整,并最小化渲染结果与预测结果之间的光度误差和几何误差,以此优化初始网格。我们的优化方案同时考虑顶点的位置和颜色,以及每张图像对应的关节角度,从而减轻姿态误差带来的负面影响。我们开展了大量实验以验证所提方法的有效性,并将CanonicalFusion与当前最先进的方法进行了对比。我们的源代码已发布于https://github.com/jsshin98/CanonicalFusion。

英文摘要:

We present a novel framework for reconstructing animatable human avatars from multiple images, termed CanonicalFusion. Our central concept involves integrating individual reconstruction results into the canonical space. To be specific, we first predict Linear Blend Skinning (LBS) weight maps and depth maps using a shared-encoder-dual-decoder network, enabling direct canonicalization of the 3D mesh from the predicted depth maps. Here, instead of predicting high-dimensional skinning weights, we infer compressed skinning weights, i.e., 3-dimensional vector, with the aid of pre-trained MLP networks. We also introduce a forward skinning-based differentiable rendering scheme to merge the reconstructed results from multiple images. This scheme refines the initial mesh by reposing the canonical mesh via the forward skinning and by minimizing photometric and geometric errors between the rendered and the predicted results. Our optimization scheme considers the position and color of vertices as well as the joint angles for each image, thereby mitigating the negative effects of pose errors. We conduct extensive experiments to demonstrate the effectiveness of our method and compare our CanonicalFusion with state-of-the-art methods. Our source codes are available at https://github.com/jsshin98/CanonicalFusion.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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