  Realistic human-centric rendering plays a key role in both computer vision
and computer graphics. Rapid progress has been made in the algorithm aspect
over the years, yet existing human-centric rendering datasets and benchmarks
are rather impoverished in terms of diversity, which are crucial for rendering
effect. Researchers are usually constrained to explore and evaluate a small set
of rendering problems on current datasets, while real-world applications
require methods to be robust across different scenarios. In this work, we
present DNA-Rendering, a large-scale, high-fidelity repository of human
performance data for neural actor rendering. DNA-Rendering presents several
alluring attributes. First, our dataset contains over 1500 human subjects, 5000
motion sequences, and 67.5M frames' data volume. Second, we provide rich assets
for each subject -- 2D/3D human body keypoints, foreground masks, SMPLX models,
cloth/accessory materials, multi-view images, and videos. These assets boost
the current method's accuracy on downstream rendering tasks. Third, we
construct a professional multi-view system to capture data, which contains 60
synchronous cameras with max 4096 x 3000 resolution, 15 fps speed, and stern
camera calibration steps, ensuring high-quality resources for task training and
evaluation. Along with the dataset, we provide a large-scale and quantitative
benchmark in full-scale, with multiple tasks to evaluate the existing progress
of novel view synthesis, novel pose animation synthesis, and novel identity
rendering methods. In this manuscript, we describe our DNA-Rendering effort as
a revealing of new observations, challenges, and future directions to
human-centric rendering. The dataset, code, and benchmarks will be publicly
available at https://dna-rendering.github.io/
