DNA渲染：用于高保真度人类中心渲染的多样神经演员存储库

摘要

在计算机视觉和计算机图形学中，逼真的以人为中心的渲染起着关键作用。多年来，在算法方面取得了快速进展，然而现有的以人为中心的渲染数据集和基准测试在多样性方面相对匮乏，而多样性对于渲染效果至关重要。研究人员通常受限于在当前数据集上探索和评估少量渲染问题，而实际应用需要方法能够在不同场景下具有稳健性。在这项工作中，我们提出了DNA-Rendering，这是一个大规模、高保真度的用于神经演员渲染的人类表现数据库。DNA-Rendering具有几个吸引人的特点。首先，我们的数据集包含超过1500名人类主体，5000个运动序列和6750万帧的数据量。其次，我们为每个主体提供丰富的资源--2D/3D人体关键点、前景蒙版、SMPLX模型、服装/配饰材料、多视角图像和视频。这些资源提升了当前方法在下游渲染任务上的准确性。第三，我们构建了一个专业的多视角系统来捕捉数据，其中包含60台同步摄像头，最高分辨率为4096 x 3000，帧率为15fps，以及严格的相机校准步骤，确保了用于任务训练和评估的高质量资源。除了数据集，我们还提供了一个大规模且定量的基准测试，涵盖多项任务，用于评估新颖视角合成、新颖姿势动画合成和新颖身份渲染方法的现有进展。在本文中，我们描述了我们的DNA-Rendering工作，揭示了新的观察结果、挑战和未来方向，以人为中心的渲染。数据集、代码和基准测试将在https://dna-rendering.github.io/ 上公开提供。

English

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/

DNA渲染：用于高保真度人类中心渲染的多样神经演员存储库

DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering

摘要

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