title: DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity
    Human-centric Rendering
summary: 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/
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paper_title: DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity
    Human-centric Rendering
paper_summary: 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/
paper_ai_summary: DNA-Rendering is a comprehensive dataset of human performance data that enhances neural actor rendering accuracy by providing diverse assets and a professional multi-view capture system.
paper_ai_keywords: neural actor rendering SMPLX novel view synthesis novel pose animation synthesis novel identity rendering multi-view images videos camera calibration