COAP: Compositional Articulated Occupancy of People
- URL: http://arxiv.org/abs/2204.06184v1
- Date: Wed, 13 Apr 2022 06:02:20 GMT
- Title: COAP: Compositional Articulated Occupancy of People
- Authors: Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhoefer,
Siyu Tang
- Abstract summary: We present a novel neural implicit representation for articulated human bodies.
We employ a part-aware encoder-decoder architecture to learn neural articulated occupancy.
Our method largely outperforms existing solutions in terms of both efficiency and accuracy.
- Score: 28.234772596912162
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a novel neural implicit representation for articulated human
bodies. Compared to explicit template meshes, neural implicit body
representations provide an efficient mechanism for modeling interactions with
the environment, which is essential for human motion reconstruction and
synthesis in 3D scenes. However, existing neural implicit bodies suffer from
either poor generalization on highly articulated poses or slow inference time.
In this work, we observe that prior knowledge about the human body's shape and
kinematic structure can be leveraged to improve generalization and efficiency.
We decompose the full-body geometry into local body parts and employ a
part-aware encoder-decoder architecture to learn neural articulated occupancy
that models complex deformations locally. Our local shape encoder represents
the body deformation of not only the corresponding body part but also the
neighboring body parts. The decoder incorporates the geometric constraints of
local body shape which significantly improves pose generalization. We
demonstrate that our model is suitable for resolving self-intersections and
collisions with 3D environments. Quantitative and qualitative experiments show
that our method largely outperforms existing solutions in terms of both
efficiency and accuracy. The code and models are available at
https://neuralbodies.github.io/COAP/index.html
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