This paper presents a new dataset for Novel View Synthesis, generated from a high-quality, animated film with stunning realism and intricate detail. Our dataset captures a variety of dynamic scenes, complete with detailed textures, lighting, and motion, making it ideal for training and evaluating cutting-edge 4D scene reconstruction and novel view generation models. In addition to high-fidelity RGB images, we provide multiple complementary modalities, including depth, surface normals, object segmentation and optical flow, enabling a deeper understanding of scene geometry and motion. The dataset is organised into three distinct benchmarking scenarios: a dense multi-view camera setup, a sparse camera arrangement, and monocular video sequences, enabling a wide range of experimentation and comparison across varying levels of data sparsity. With its combination of visual richness, high-quality annotations, and diverse experimental setups, this dataset offers a unique resource for pushing the boundaries of view synthesis and 3D vision.

翻译：本文提出一个新的新视角合成数据集，该数据集源自一部高质量、动画电影，具有惊人的真实感和精细细节。我们的数据集捕捉了多种动态场景，包含详细的纹理、光照和运动，使其成为训练和评估前沿4D场景重建及新视角生成模型的理想选择。除了高保真RGB图像外，我们还提供了多种互补模态，包括深度、表面法线、对象分割和光流，以支持对场景几何和运动的更深入理解。该数据集被组织为三个不同的基准测试场景：密集多视角相机设置、稀疏相机布置和单目视频序列，从而能够在不同数据稀疏度水平上进行广泛的实验和比较。凭借其视觉丰富性、高质量标注和多样化的实验设置，该数据集为推动视角合成和3D视觉的边界提供了独特资源。