InFlux-Real

InFlux-Real is the unified real-world benchmark release for the InFlux project. It combines the original InFlux benchmark with the newly captured InFlux++ Real extension, providing per-frame ground truth camera intrinsics for videos with dynamic intrinsics.

Together, the two benchmark partitions contain 657,648 annotated frames from 720 high-resolution indoor and outdoor videos. The videos span diverse scenes, camera motions, changes in intrinsics, and dynamic objects.

Naming note: InFlux-Real refers to this complete Hugging Face repository. InFlux++ Real refers specifically to the newly captured extension introduced in the InFlux++ paper.

Project Website · Download, Extraction, and Submission Utilities · Live Leaderboard · Synthetic Training Data

Benchmark Partitions

InFlux-Real is organized into two top-level partitions:

• influx/ contains the original benchmark introduced in InFlux at NeurIPS 2025.
• influx_pp_real/ contains InFlux++ Real, the benchmark extension introduced in InFlux++ at ECCV 2026.

Each partition is further divided into validation and test splits. Public ground truth intrinsics are provided for the validation split. Follow the official submission instructions to evaluate on the test split.

Partition	Videos	Frames	Validation videos / frames	Test videos / frames	Avg. frames/video
influx/	386	143,513	70 / 21,546	316 / 121,967	~372
influx_pp_real/	334	514,135	50 / 77,449	284 / 436,686	~1,539
Combined	720	657,648	120 / 98,995	600 / 558,653	~913

File Structure

InFlux-Real/
├── influx/
│   ├── videos/                                  # 386 .mp4 files
│   ├── video_frame_count_and_split_v1.json
│   └── gt_validation_dict_v1.json
└── influx_pp_real/
    ├── videos/                                  # 334 .mp4 files
    ├── video_frame_count_and_split_v2.json
    └── gt_validation_dict_v2.json

Path	Description
influx/videos/*.mp4	Videos from InFlux
influx/video_frame_count_and_split_v1.json	Frame counts and validation/test assignments for InFlux
influx/gt_validation_dict_v1.json	Per-frame annotations for the validation split of InFlux
influx_pp_real/videos/*.mp4	Videos from InFlux++ Real
influx_pp_real/video_frame_count_and_split_v2.json	Frame counts and validation/test assignments for InFlux++ Real
influx_pp_real/gt_validation_dict_v2.json	Per-frame annotations for the validation split of InFlux++ Real

Downloading and Extracting the Dataset

InFlux-Real is distributed as compressed .mp4 video files. Because the complete benchmark is large, we provide utilities in the official InFlux repository for downloading the dataset from Hugging Face and decoding the videos into per-frame .tiff images.

The utilities support downloading either benchmark partition independently or downloading the complete InFlux-Real release.

Approximate storage requirements are shown below:

Partition	Compressed .mp4 videos	Decoded .tiff frames
influx/	124 GiB	3.0 TiB
influx_pp_real/	537 GiB	7.0 TiB
Complete release	661 GiB	10.0 TiB

The compressed-video column reports the approximate size of the .mp4 files as distributed through Hugging Face. The decoded-frame column reports the approximate disk space required after converting all videos in the corresponding partition into .tiff frames.

Storage requirements may vary slightly depending on filesystem overhead and extraction settings. If retaining both the downloaded videos and the decoded frames, ensure that sufficient space is available for both representations.

Users who need only one benchmark partition may download and decode influx/ or influx_pp_real/ independently. See the official download and extraction utilities for the latest commands, script documentation, and output-directory structure.

Video Files

The .mp4 files contain videos featuring dynamic camera intrinsics, camera motion, and dynamic objects. The videos are encoded using YUV 4:4:4 chroma sampling.

This format may not be supported by every browser or default system video player. For local playback, we recommend using VLC.

JSON Schemas

Frame Counts and Benchmark Splits

Files:

• influx/video_frame_count_and_split_v1.json
• influx_pp_real/video_frame_count_and_split_v2.json

Each top-level key is a video identifier matching the stem of its corresponding .mp4 filename. For example, bike_shot11 corresponds to bike_shot11.mp4.

{
  "bike_shot11": {
    "frame_count": 338,
    "split": "val"
  }
}

Field	Description
frame_count	Number of frames in the video
split	Benchmark split; either "val" or "test"

Validation Ground Truth

Files:

• influx/gt_validation_dict_v1.json
• influx_pp_real/gt_validation_dict_v2.json

Each top-level key is a video identifier matching the stem of its corresponding .mp4 filename. Only validation videos and their annotations are included in these files. Frames are keyed by their frame indices, represented as strings.

The numeric values below are placeholders illustrating the schema:

{
  "bike_shot11": {
    "0": {
      "intrinsics_gt": {
        "fx": 0.0,
        "fy": 0.0,
        "cx": 0.0,
        "cy": 0.0,
        "k1": 0.0,
        "k2": 0.0,
        "p1": 0.0,
        "p2": 0.0
      },
      "intrinsics_gt_extrapolated": {
        "fx": 0.0,
        "fy": 0.0,
        "cx": 0.0,
        "cy": 0.0,
        "k1": 0.0,
        "k2": 0.0,
        "p1": 0.0,
        "p2": 0.0
      },
      "lens_metadata": {
        "focal_length_mm": 0.0,
        "focus_distance_m": 0.0
      }
    }
  }
}

Per-frame Annotation Fields

intrinsics_gt

Ground truth camera intrinsics obtained through interpolation of a calibration lookup table (LUT). For frames whose lens metadata falls outside the calibrated LUT bounds, the reported intrinsics values are replaced with NaN.

The parameters follow the radial-tangential Brown–Conrady distortion model:

Field	Description
fx, fy	Horizontal and vertical focal lengths
cx, cy	Principal-point coordinates
k1, k2	Radial distortion coefficients
p1, p2	Tangential distortion coefficients

intrinsics_gt_extrapolated

Contains the same fields as intrinsics_gt, but also provides extrapolated intrinsics for frames whose lens metadata falls outside the calibrated LUT bounds.

lens_metadata

Contains the physical lens metadata used to query the LUTs to obtain per-frame intrinsics:

Field	Description
focal_length_mm	Recorded lens focal length in millimeters; denoted as LFL in the papers
focus_distance_m	Distance from the camera sensor to the focus plane, in meters; denoted as FD in the papers

Test-Set Evaluation and Submission

Ground truth annotations for the validation splits are included in this release, while ground truth for the test splits is withheld.

To evaluate a model on the test split of either benchmark partition, generate predictions in the required submission format and follow the instructions in the official InFlux repository. The repository describes the evaluation protocol, required prediction format, and submission procedure.

Benchmark results are displayed on the live InFlux leaderboard.

For model training or finetuning, see InFlux-Synth, our large-scale synthetic dataset with per-frame ground truth camera intrinsics.

Citation

Please cite the papers associated with the data you use:

• If you use only the original influx/ partition, cite the InFlux paper.
• If you use only the influx_pp_real/ extension, cite the InFlux++ paper.
• If you use the complete InFlux-Real repository or report results on both partitions, cite both papers.

InFlux

InFlux: A Benchmark for Self-Calibration of Dynamic Intrinsics of Video Cameras
Neural Information Processing Systems, Datasets and Benchmarks Track, 2025

NeurIPS paper · arXiv

@inproceedings{liang2025influx,
    author = {Liang, Erich and Bhattacharjee, Roma and Dey, Sreemanti and Moschopoulos, Rafael and Wang, Caitlin and Liao, Michel and Tan, Grace and Wang, Andrew and Kayan, Karhan and Alexandropoulos, Stamatis and Deng, Jia},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {D. Belgrave and C. Zhang and H. Lin and R. Pascanu and P. Koniusz and M. Ghassemi and N. Chen},
    pages = {},
    publisher = {Curran Associates, Inc.},
    title = {InFlux: A Benchmark for Self-Calibration of Dynamic Intrinsics of Video Cameras},
    url = {https://proceedings.neurips.cc/paper_files/paper/2025/file/8a8eca190088852067b4e8cc1b907122-Paper-Datasets_and_Benchmarks_Track.pdf},
    volume = {38},
    year = {2025}
}

InFlux++

InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics
European Conference on Computer Vision, 2026

arXiv

@misc{liang2026influxrealsyntheticdata,
    title = {InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics},
    author = {Erich Liang and Caleb Kha-Uong and Chinmaya Saran and Sreemanti Dey and David W. Liu and Junhan Ouyang and Benjamin Zhou and Jia Deng},
    year = {2026},
    eprint = {2607.05389},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV},
    url = {https://arxiv.org/abs/2607.05389}
}