Abstract
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.
Abstract (translated)
本文介绍了一个新的用于新颖视图合成的数据集，该数据集源自一部具有惊人真实感和复杂细节的高质量动画电影。我们的数据集捕捉了各种动态场景，包括详细的纹理、光照和运动，非常适合训练和评估前沿的4D场景重建和新颖视图生成模型。除了高保真的RGB图像外，我们还提供了多种互补模式，如深度信息、表面法线、对象分割及光流，这有助于更深入地理解场景几何结构与运动。 数据集按三个不同的基准测试情景组织：密集多视角相机设置、稀疏相机排列以及单目视频序列。这使得在不同程度的数据稀疏性上进行广泛实验和对比成为可能。凭借其视觉丰富度、高质量注释及多样化的实验设定，该数据集为推进视图合成与3D视觉领域的边界提供了独特资源。
URL
https://arxiv.org/abs/2512.13639
