Artemis: articulated neural pets with appearance and motion synthesis
Haimin Luo, Teng Xu, Yuheng Jiang, Chenglin Zhou, Qiwei Qiu, Yingliang Zhang, Wei Yang, Lan Xu, Jingyi Yu
摘要
We, humans, are entering into a virtual era and indeed want to bring animals to the virtual world as well for companion. Yet, computer-generated (CGI) furry animals are limited by tedious off-line rendering, let alone interactive motion control. In this paper, we present ARTEMIS, a novel neural modeling and rendering pipeline for generating ARTiculated neural pets with appEarance and Motion synthesIS. Our ARTEMIS enables interactive motion control, real-time animation, and photo-realistic rendering of furry animals. The core of our ARTEMIS is a neural-generated (NGI) animal engine, which adopts an efficient octree-based representation for animal animation and fur rendering. The animation then becomes equivalent to voxel-level deformation based on explicit skeletal warping. We further use a fast octree indexing and efficient volumetric rendering scheme to generate appearance and density features maps. Finally, we propose a novel shading network to generate high-fidelity details of appearance and opacity under novel poses from appearance and density feature maps. For the motion control module in ARTEMIS, we combine state-of-the-art animal motion capture approach with recent neural character control scheme. We introduce an effective optimization scheme to reconstruct the skeletal motion of real animals captured by a multi-view RGB and Vicon camera array. We feed all the captured motion into a neural character control scheme to generate abstract control signals with motion styles. We further integrate ARTEMIS into existing engines that support VR headsets, providing an unprecedented immersive experience where a user can intimately interact with a variety of virtual animals with vivid movements and photo-realistic appearance. Extensive experiments and showcases demonstrate the effectiveness of our ARTEMIS system in achieving highly realistic rendering of NGI animals in real-time, providing daily immersive and interactive experiences with digital animals unseen before. We make available our ARTEMIS model and dynamic furry animal dataset at https://haiminluo.github.io/publication/artemis/.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper16
- NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view ReconstructionYiming Wang, Qin Han, Marc Habermann, Kostas Daniilidis 等ICCV 2023 · 被引用 402 次
- HumanRF: High-Fidelity Neural Radiance Fields for Humans in MotionMustafa Isik, Martin Rünz, Markos Georgopoulos, Taras Khakhulin 等SIGGRAPH 2023 · 被引用 149 次
- LiveHand: Real-time and Photorealistic Neural Hand RenderingAkshay Mundra, Mallikarjun B. R., Jiayi Wang, Marc Habermann 等ICCV 2023 · 被引用 29 次
- VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video StreamsLiao Wang, Kaixin Yao, Chengcheng Guo, Zhirui Zhang 等CVPR 2024 · 被引用 14 次
- Replay: Multi-modal Multi-view Acted Videos for Casual HolographyRoman Shapovalov, Yanir Kleiman, Ignacio Rocco, David Novotný 等ICCV 2023 · 被引用 11 次
它引用的顶会 Paper30
- Implicit Neural Representations with Periodic Activation FunctionsVincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell 等NeurIPS 2020 · 被引用 4,008 次
- PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human DigitizationShunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima 等ICCV 2019 · 被引用 1,411 次
- PlenOctrees for Real-time Rendering of Neural Radiance FieldsAlex Yu, Ruilong Li, Matthew Tancik, Hao Li 等ICCV 2021 · 被引用 1,284 次
- MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View StereoAnpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang 等ICCV 2021 · 被引用 1,024 次
- KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPsChristian Reiser, Songyou Peng, Yiyi Liao, Andreas GeigerICCV 2021 · 被引用 963 次
相关 Paper
- Practical level-of-detail aggregation of fur appearanceJunqiu Zhu, Sizhe Zhao, Lu Wang, Yanning Xu 等SIGGRAPH 2022 · 被引用 15 次
- 4D Gaussian Splatting for Real-Time Dynamic Scene RenderingGuanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie 等CVPR 2024 · 被引用 513 次
- ASH: Animatable Gaussian Splats for Efficient and Photoreal Human RenderingHaokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt 等CVPR 2024 · 被引用 56 次
- HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance CaptureZiyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi 等CVPR 2022
- Neural Lumigraph RenderingPetr Kellnhofer, Lars Jebe, Andrew Jones, Ryan Spicer 等CVPR 2021
