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AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture (2207.02031v2)

Published 5 Jul 2022 in cs.CV

Abstract: To address the ill-posed problem caused by partial observations in monocular human volumetric capture, we present AvatarCap, a novel framework that introduces animatable avatars into the capture pipeline for high-fidelity reconstruction in both visible and invisible regions. Our method firstly creates an animatable avatar for the subject from a small number (~20) of 3D scans as a prior. Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly recon-structs high-fidelity 3D textured models with dynamic details regardless of the visibility. To learn an effective avatar for volumetric capture from only few samples, we propose GeoTexAvatar, which leverages both geometry and texture supervisions to constrain the pose-dependent dynamics in a decomposed implicit manner. An avatar-conditioned volumetric capture method that involves a canonical normal fusion and a reconstruction network is further proposed to integrate both image observations and avatar dynamics for high-fidelity reconstruction in both observed and invisible regions. Overall, our method enables monocular human volumetric capture with detailed and pose-dependent dynamics, and the experiments show that our method outperforms state of the art. Code is available at https://github.com/lizhe00/AvatarCap.

Citations (30)

Summary

  • The paper presents AvatarCap, a novel framework that integrates animatable avatars with monocular RGB videos for precise 3D human volumetric capture.
  • It introduces GeoTexAvatar to condition geometry and texture on pose parameters, enabling detailed reconstructions despite occlusions.
  • The framework achieves superior accuracy over existing methods, as shown by improved Chamfer Distance and Scan-to-Mesh Distance metrics.

Animatable Avatar Conditioned Monocular Human Volumetric Capture: An Overview

The paper "AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture" presents a sophisticated framework designed to address the challenges inherent in monocular human volumetric capture. The central contribution of the paper is AvatarCap, a pipeline that integrates person-specific animatable avatars into the volumetric capture process. This approach leverages both observed information from monocular RGB videos and prior knowledge from pre-constructed animatable avatars to enable high-fidelity human reconstruction, even in occluded regions.

Key Contributions

  1. Framework Overview: The AvatarCap framework facilitates monocular volumetric capture by using animatable avatars created from a limited dataset of approximately 20 3D scans. These avatars serve as a data-driven prior, bridging the gap between observed image data and the unobserved dynamic details required for high-quality 3D reconstruction.
  2. GeoTexAvatar: A novel representation termed GeoTexAvatar is introduced, which is conditioning both geometry and texture on the SMPL pose parameters to enhance detail and generalization capability. This involves separating pose-agnostic and pose-dependent dynamics, thus overcoming limitations seen in existing avatar methods that rely heavily on numerous scans.
  3. Canonical Normal Fusion: A unique approach to integrating avatar and image-observed normal maps is proposed. This method involves canonical normal fusion, where high-frequency details from image observations are merged with the robust low-frequency orientations of the avatar normals, ensuring consistent and accurate reconstruction despite poor SMPL fitting.
  4. Reconstruction Network: The paper details the use of a reconstruction network pretrained on large-scale datasets to synthesize high-fidelity, full-body 3D models. Leveraging normal maps as intermediaries, the network capitalizes on the large-scale prior to enhance the geometric accuracy and detail of the reconstructions.

Numerical Evaluation

The paper provides numerical results demonstrating the superiority of the AvatarCap method over contemporary approaches. Metrics such as the Chamfer Distance and Scan-to-Mesh Distance showcase the framework's capacity for precision, with notable improvements over methods like PIFuHD and NormalGAN. The avatars created by GeoTexAvatar also outperform SCANimate, POP, and other entities in accurately modeling pose-dependent dynamics, achieving significantly lower error rates in comparisons to ground-truth scans.

Implications and Future Directions

The integration of animatable avatars into the monocular capture pipeline offers a pathway towards more dynamic and realistic 3D human models. This development is particularly significant for applications in the Metaverse, gaming, and virtual communication, where the quality of dynamic human representations is critical.

The framework still requires a dataset of 3D scans for avatar creation, a step that can be arduous without dedicated capture systems. Future research may explore reducing the reliance on extensive 3D scanning, possibly by utilizing self-portrait or RGBD-based capture methods. Additionally, improvements are needed in handling loose attire, such as flowing garments or draped clothing, which present challenges due to limitations in existing skeleton models.

In summary, AvatarCap and its underlying components represent a significant advancement in the field of computer vision, specifically in monocular human volumetric capture. The methods proposed in this paper not only extend the capabilities of current systems but also pave the way for future innovations that could further enhance the realism and applicability of 3D human modeling technologies.