RenderMe-360: A Large Digital Asset Library and Benchmarks Towards
  High-fidelity Head Avatars
Dongwei Pan Long Zhuo Jingtan Piao Huiwen Luo Wei Cheng Yuxin Wang Siming Fan Shengqi Liu Lei Yang Bo Dai Ziwei Liu Chen Change Loy Chen Qian Wayne Wu Dahua Lin Kwan-Yee Lin
Synthesizing high-fidelity head avatars is a central problem for computer
vision and graphics. While head avatar synthesis algorithms have advanced
rapidly, the best ones still face great obstacles in real-world scenarios. One
of the vital causes is inadequate datasets -- 1) current public datasets can
only support researchers to explore high-fidelity head avatars in one or two
task directions; 2) these datasets usually contain digital head assets with
limited data volume, and narrow distribution over different attributes. In this
paper, we present RenderMe-360, a comprehensive 4D human head dataset to drive
advance in head avatar research. It contains massive data assets, with 243+
million complete head frames, and over 800k video sequences from 500 different
identities captured by synchronized multi-view cameras at 30 FPS. It is a
large-scale digital library for head avatars with three key attributes: 1) High
Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K
cameras in 360 degrees. 2) High Diversity: The collected subjects vary from
different ages, eras, ethnicities, and cultures, providing abundant materials
with distinctive styles in appearance and geometry. Moreover, each subject is
asked to perform various motions, such as expressions and head rotations, which
further extend the richness of assets. 3) Rich Annotations: we provide
annotations with different granularities: cameras' parameters, matting, scan,
2D/3D facial landmarks, FLAME fitting, and text description.
  Based on the dataset, we build a comprehensive benchmark for head avatar
research, with 16 state-of-the-art methods performed on five main tasks: novel
view synthesis, novel expression synthesis, hair rendering, hair editing, and
talking head generation. Our experiments uncover the strengths and weaknesses
of current methods. RenderMe-360 opens the door for future exploration in head
avatars.