InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics

Camera intrinsics play a vital role in recovering 3D structurefrom 2D video. However, most 3D algorithms assume that intrinsics re-main fixed throughout input video, an assumption that frequently failsfor real-world in-the-wild videos. Consequently, estimating per-frame in-trinsics from RGB images is critical for enabling 3D methods to operaterobustly on dynamic intrinsics videos. Previously, InFlux has contributedto this research direction by establishing the first real-world benchmarkwith per-frame ground truth intrinsics for dynamic intrinsics videos, en-abling systematic evaluation of algorithms for this task. Nevertheless,existing methods remain inaccurate due to two key obstacles: (i) train-ing data for this task is scarce and lacks diversity in camera intrinsics;and (ii) existing benchmarks, including InFlux, are limited in scene andcamera motion diversity, making it difficult to properly evaluate methodperformance. To address both gaps, we present InFlux++, consisting oftwo components. InFlux++ Synth is a large-scale procedurally generatedsynthetic video dataset with 441K+ annotated frames from 1841 high-resolution videos, providing accurate per-frame ground truth intrinsicsfor training dynamic intrinsics prediction models; a subset additionallyincludes per-frame camera pose, depth, and surface normals. The videosfeature rich intrinsics diversity through changes in camera zoom andfocus over time, as well as dynamic objects and realistic rendering ef-fects such as lens distortion and defocus blur. InFlux++ Real is a large-scale real-world benchmark that extends InFlux with 514K+ newly cap-tured frames across 334 high-resolution videos, spanning a wider rangeof scenes and camera motions. Finetuning existing intrinsics predictionmethods on InFlux++ Synth consistently improves focal length estima-tion across both InFlux++ Real and InFlux, suggesting that syntheticsupervision is a promising direction for RGB-based intrinsics prediction.For the dataset, benchmark, code, videos, submission instructions, andlive leaderboard, please visit https://influx.cs.princeton.edu/.