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Synthesizing high-fidelity head avatars is a central problem for computervision and graphics. While head avatar synthesis algorithms have advancedrapidly, the best ones still face great obstacles in real-world scenarios. Oneof the vital causes is inadequate datasets -- 1) current public datasets canonly support researchers to explore high-fidelity head avatars in one or twotask directions; 2) these datasets usually contain digital head assets withlimited data volume, and narrow distribution over different attributes. In thispaper, we present RenderMe-360, a comprehensive 4D human head dataset to driveadvance in head avatar research. It contains massive data assets, with 243+million complete head frames, and over 800k video sequences from 500 differentidentities captured by synchronized multi-view cameras at 30 FPS. It is alarge-scale digital library for head avatars with three key attributes: 1) HighFidelity: all subjects are captured by 60 synchronized, high-resolution 2Kcameras in 360 degrees. 2) High Diversity: The collected subjects vary fromdifferent ages, eras, ethnicities, and cultures, providing abundant materialswith distinctive styles in appearance and geometry. Moreover, each subject isasked to perform various motions, such as expressions and head rotations, whichfurther extend the richness of assets. 3) Rich Annotations: we provideannotations 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 avatarresearch, with 16 state-of-the-art methods performed on five main tasks: novelview synthesis, novel expression synthesis, hair rendering, hair editing, andtalking head generation. Our experiments uncover the strengths and weaknessesof current methods. RenderMe-360 opens the door for future exploration in headavatars.

RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars