Surfel-based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction from Monocular Video

• URL: http://arxiv.org/abs/2407.15212v2
• Date: Tue, 23 Jul 2024 12:57:32 GMT
• Title: Surfel-based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction from Monocular Video
• Authors: Yiqun Zhao, Chenming Wu, Binbin Huang, Yihao Zhi, Chen Zhao, Jingdong Wang, Shenghua Gao,
• Abstract summary: This paper introduces the Surfel-based Gaussian Inverse Avatar (SGIA) method, which introduces efficient training and rendering for relightable dynamic human reconstruction. SGIA advances previous Gaussian Avatar methods by comprehensively modeling Physically-Based Rendering (PBR) properties for clothed human avatars. Our approach integrates pre-integration and image-based lighting for fast light calculations that surpass the performance of existing implicit-based techniques.
• Score: 41.677560631206184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient and accurate reconstruction of a relightable, dynamic clothed human avatar from a monocular video is crucial for the entertainment industry. This paper introduces the Surfel-based Gaussian Inverse Avatar (SGIA) method, which introduces efficient training and rendering for relightable dynamic human reconstruction. SGIA advances previous Gaussian Avatar methods by comprehensively modeling Physically-Based Rendering (PBR) properties for clothed human avatars, allowing for the manipulation of avatars into novel poses under diverse lighting conditions. Specifically, our approach integrates pre-integration and image-based lighting for fast light calculations that surpass the performance of existing implicit-based techniques. To address challenges related to material lighting disentanglement and accurate geometry reconstruction, we propose an innovative occlusion approximation strategy and a progressive training approach. Extensive experiments demonstrate that SGIA not only achieves highly accurate physical properties but also significantly enhances the realistic relighting of dynamic human avatars, providing a substantial speed advantage. We exhibit more results in our project page: https://GS-IA.github.io.

Related papers

• URAvatar: Universal Relightable Gaussian Codec Avatars [42.25313535192927]
  We present a new approach to creating photorealistic and relightable head avatars from a phone scan with unknown illumination. The reconstructed avatars can be animated and relit in real time with the global illumination of diverse environments.
  arXiv  Detail & Related papers  (2024-10-31T17:59:56Z)
• HAVE-FUN: Human Avatar Reconstruction from Few-Shot Unconstrained Images [33.298962236215964]
  We study the reconstruction of human avatars from a few-shot unconstrained photo album. For handling dynamic data, we integrate a skinning mechanism with deep marching tetrahedra. Our framework, called HaveFun, can undertake avatar reconstruction, rendering, and animation.
  arXiv  Detail & Related papers  (2023-11-27T10:01:31Z)
• Towards Practical Capture of High-Fidelity Relightable Avatars [60.25823986199208]
  TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions. It can predict the appearance in real-time with a single forward pass, achieving high-quality relighting effects. Our framework achieves superior performance for photorealistic avatar animation and relighting.
  arXiv  Detail & Related papers  (2023-09-08T10:26:29Z)
• High-Fidelity Clothed Avatar Reconstruction from a Single Image [73.15939963381906]
  We propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction from a single image. We use an implicit model to learn the general shape in the canonical space of a person in a learning-based way. We refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way.
  arXiv  Detail & Related papers  (2023-04-08T04:01:04Z)
• Efficient Meshy Neural Fields for Animatable Human Avatars [87.68529918184494]
  Efficiently digitizing high-fidelity animatable human avatars from videos is a challenging and active research topic. Recent rendering-based neural representations open a new way for human digitization with their friendly usability and photo-varying reconstruction quality. We present EMA, a method that Efficiently learns Meshy neural fields to reconstruct animatable human Avatars.
  arXiv  Detail & Related papers  (2023-03-23T00:15:34Z)
• Real-time volumetric rendering of dynamic humans [83.08068677139822]
  We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos. Our method can reconstruct a dynamic human in less than 3h using a single GPU, compared to recent state-of-the-art alternatives that take up to 72h. A novel local ray marching rendering allows visualizing the neural human on a mobile VR device at 40 frames per second with minimal loss of visual quality.
  arXiv  Detail & Related papers  (2023-03-21T14:41:25Z)
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer [78.91753256634453]
  We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables. In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing. Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
  arXiv  Detail & Related papers  (2021-10-30T01:59:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.