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arXiv · 2512.13639

Charge: A Comprehensive Novel View Synthesis Benchmark and Dataset to Bind Them All

Michal Nazarczuk·Thomas Tanay·Arthur Moreau·Zhensong Zhang·Eduardo Pérez-Pellitero
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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.

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Michal Nazarczuk, Thomas Tanay, Arthur Moreau, Zhensong Zhang, Eduardo Pérez-Pellitero. 2025-12-15. Charge: A Comprehensive Novel View Synthesis Benchmark and Dataset to Bind Them All. https://arxiv.org/abs/2512.13639

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