SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry and Texture Annotations
<|reference_start|>SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry and Texture Annotations: Recovering photorealistic and drivable full-body avatars is crucial for numerous applications, including virtual reality, 3D games, and tele-presence. Most methods, whether reconstruction or generation, require large numbers of human motion sequences and corresponding textured meshes. To easily learn a drivable avatar, a reasonable parametric body model with unified topology is paramount. However, existing human body datasets either have images or textured models and lack parametric models which fit clothes well. We propose a new parametric model SMPLX-Lite-D, which can fit detailed geometry of the scanned mesh while maintaining stable geometry in the face, hand and foot regions. We present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with multi-view RGB sequences, keypoints annotations, textured scanned meshes, and textured SMPLX-Lite-D models. With the SMPLX-Lite dataset, we train a conditional variational autoencoder model that takes human pose and facial keypoints as input, and generates a photorealistic drivable human avatar.<|reference_end|>
@article{jiang2024smplx-lite:,
    title={SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry
  and Texture Annotations},
    author={Yujiao Jiang, Qingmin Liao, Zhaolong Wang, Xiangru Lin, Zongqing Lu,
  Yuxi Zhao, Hanqing Wei, Jingrui Ye, Yu Zhang, Zhijing Shao},
    journal={arXiv preprint arXiv:2405.19609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19609},
    primaryClass={cs.CV cs.GR}
}