---
title: 'VAR-MIORe: A Multi-Task Motion Benchmark'
url: https://www.emergentmind.com/topics/var-miore
type: topic
---

# VAR-MIORe: A Multi-Task Motion Benchmark

VAR-MIORe is a multi-task, high-frame-rate real-world benchmark designed to evaluate restoration and motion-analysis methods under a continuously variable range of motion magnitudes, from minimal or sharp-like motion to extreme motion. Built from **1000 FPS, 1920×1080** video captured with an industrial camera and multiple professional lenses, it extends **MIORe** by making motion amplitude itself an explicit benchmark variable for **single-image motion deblurring**, **video frame interpolation (VFI)**, and **optical flow (OF) estimation**. Its central contribution is not merely additional data volume, but a protocol in which the same acquisition pipeline is used to sweep motion magnitude across a controlled range, thereby exposing how performance degrades as displacement and blur severity increase [2509.06803].

## 1. Benchmark definition and relation to MIORe

VAR-MIORe is the extension of MIORe that spans a **variable range of motion magnitudes**, whereas MIORe is a **regularized multi-task benchmark** in which blur magnitude is made relatively comparable across scenes by adaptively averaging a variable number of high-speed frames based on motion estimates [2509.06803]. MIORe targets a **mid-level blur regime**, approximately centered around **30 pixels of optical flow**, so that scenes with different true speeds produce roughly comparable blur severity. VAR-MIORe, by contrast, is positioned as a **stress-test benchmark** that probes the breaking points of current methods as motion magnitude increases from almost no motion to very large displacement or severe blur.

The design is motivated by limitations in prior restoration datasets. The paper identifies **limited motion variability**, insufficient real-world coupling between blur and scene dynamics, **fixed or weak control over blur generation**, and the absence of **explicit motion-amplitude benchmarking**. In that framing, VAR-MIORe is intended to answer questions such as how performance degrades as displacement grows, which methods fail first, and whether methods trained on moderate-motion datasets remain robust under large motion. The paper characterizes it as the **first benchmark with explicit control over motion amplitude** [2509.06803].

## 2. Acquisition pipeline, scene coverage, and corpus

The acquisition setup uses a **CHRONOS 2.1-HD** high-speed camera at **1920 × 1080** and **1000 FPS**, with a per-recording limit of up to **5516 frames** due to internal RAM limits [2509.06803]. Four professional lenses are used: **Tamron 15–30mm zoom**, **Canon 24mm**, **Sigma 85mm**, and **Laowa 100mm**. This lens diversity is intended to introduce realistic optical variation, including **lens degradation models**, **vignetting**, **chromatic aberration**, and **defocus or bokeh behavior**. Lenses were used mostly **wide open** to maximize light, with narrower apertures in bright scenes when needed for exposure control.

The capture is primarily **in-the-wild**, spanning **natural and urban** scenes, **all four seasons**, different **times of day**, and conditions including **clouds, fog, rain, snow**, as well as internal adverse conditions such as **defocus**, **overexposure**, and **underexposure**. The selected corpus contains **333 meticulously selected sequences** based on **1,147,507 raw frames**. Scene selection is described as maximizing diversity in **motion granularity**, **scene type**, **seasons**, **adverse conditions**, and **optics**.

Motion scenarios include both **ego-camera motion** and **scene/object motion**. Ego-motion categories include **static capture**, translations along \(O_x, O_y, O_z\), **radial translation or zooming**, **dolly zoom**, and **yaw, pitch, roll rotations**, as well as cinematic motions such as **panning**, **tilt shots**, and **barrel roll**. Scene motion includes independently moving **vehicles**, **humans**, **animals**, and, conceptually, **liquids, fire, and smoke**, together with both **flat background motion** and **parallax or depth-layered motion**. The paper also emphasizes **dynamic multi-subject interactions** and **depth-dependent blur**, so that motion blur, defocus blur, parallax, and occlusion can coexist in the same real captures [2509.06803].

## 3. Motion-amplitude construction and blur generation

VAR-MIORe operationalizes motion amplitude through **optical flow magnitude** and through the **temporal integration span** used to synthesize blur [2509.06803]. The paper explicitly says that both **mean optical flow** and **maximum optical flow** are used adaptively. A faithful reconstruction of the flow magnitude used in the benchmark is
\[
\|\mathbf{f}(\mathbf{x})\|_2 = \sqrt{u(\mathbf{x})^2 + v(\mathbf{x})^2},
\]
with image-level summaries
\[
\mu_f = \frac{1}{|\Omega|}\sum_{\mathbf{x}\in\Omega}\|\mathbf{f}(\mathbf{x})\|_2,
\qquad
f_{\max} = \max_{\mathbf{x}\in\Omega}\|\mathbf{f}(\mathbf{x})\|_2.
\]

Blur synthesis is based on averaging high-speed sharp frames while preserving sharp supervision frames. In symmetric form, a faithful reconstruction is
\[
B_t = \frac{1}{2K+1}\sum_{i=-K}^{K} I_{t+i},
\]
with the center sharp frame \(I_t\) retained as ground truth and the endpoints \(I_{t-K}\) and \(I_{t+K}\) preserved as sharp inputs for VFI and OF. The exact adaptive sampling rule is not published, but the paper states that MIORe targets a **mid-level blur** around **30 pixels of optical flow**, averaging fewer frames when motion is large and more frames when motion is small. A faithful rule template is
\[
N = \phi(\mu_f, f_{\max}),
\]
where \(N\) decreases as motion magnitude increases.

The distinction between MIORe and VAR-MIORe is quantitative as well as conceptual. For MIORe, blur is controlled to stay in a comparable regime by varying the number of averaged sharp frames roughly between **3 and 35 composed sharp frames per blurry image**; the table reports **Offset = [3, 35] ms** and **Final FPS = [28, 1000]**. For VAR-MIORe, the motion range is much wider: **Offset = [1, 249] ms**, with benchmark buckets corresponding to averaging **1, 5, 13, 29, 61, 125, 249** frames, **Final FPS = [4, 1000]**, and **Max OF up to 1932**, versus **95** for MIORe. The paper presents these buckets as the clearest protocol for controlled motion-magnitude variation [2509.06803].

## 4. Supported tasks, supervision, and annotations

For every generated sample, the benchmark stores a **left sharp frame**, **middle sharp frame**, **right sharp frame**, and a **blurred frame** produced by averaging [2509.06803]. This supports three supervised tasks with a single acquisition pipeline. In **motion deblurring**, the input is the blurred image and the supervision target is the sharp center frame. In **video frame interpolation**, the inputs are the left and right sharp frames and the target is again the center sharp frame. In **optical flow estimation**, the inputs are the left and right sharp frames, while the ground truth is a **carefully generated pseudo-ground truth** optical-flow map produced using a **DEQ-Flow-based approach**.

A notable design choice is that the endpoint frames used for VFI and OF are kept **completely sharp**. The paper explicitly argues that this makes interpolation more difficult because there are fewer blur-trajectory cues than in settings where endpoint frames are themselves motion-blurred. For evaluation, the benchmark uses **PSNR** and **SSIM** for deblurring, **PSNR** and **SSIM** for VFI, and **EPE** and **F1** for OF. At the same time, the paper states that the OF labels are **preliminary** and need further refinement, so OF results are presented as proof-of-concept rather than definitive evaluation [2509.06803].

Sequence annotations include motion-pattern metadata such as **subject motion**, **camera motion**, translations on \(O_x, O_y, O_z\), **yaw, pitch, roll**, and broader scene or acquisition diversity. This annotation structure is consistent with the benchmark’s role as both a restoration dataset and a controlled stress protocol over motion amplitude.

## 5. Benchmark structure and empirical behavior under increasing motion

The comparison table reports **Size = 83,250** for VAR-MIORe, together with **Max OF = 1932**, **Final FPS = [4, 1000]**, and **Offset = [1, 249] ms** [2509.06803]. The benchmarked protocol visible in the experiments uses **333 input entries per bucket** over the seven motion buckets \(\{1,5,13,29,61,125,249\}\), so the benchmarked subset visible there is **2331** entries. MIORe, by comparison, is reported as **Size = 52,218**, about **5%** of the original sharp-frame pool.

The quantitative experiments show that existing methods degrade substantially as motion increases. For deblurring on VAR-MIORe, the paper reports PSNR trends such as **AdaRevD: 39.61 \(\rightarrow\) 21.39**, **LoFormer: 41.41 \(\rightarrow\) 21.42**, **FFTformer: 35.33 \(\rightarrow\) 20.91**, **UFPNet: 37.83 \(\rightarrow\) 21.15**, and **NAFNet: 25.69 \(\rightarrow\) 20.03** across the seven blur buckets. The **Input baseline** goes from **inf at 1 frame**, then **34.32 \(\rightarrow\) 21.52**. The paper highlights that at extreme blur, learned methods approach or even underperform the blurred input baseline in PSNR, indicating severe failure to recover information.

For VFI on VAR-MIORe, the same monotonic degradation appears. Reported PSNR trends include **VFIMamba: 67.79 \(\rightarrow\) 18.91**, **SGM-VFI: 56.80 \(\rightarrow\) 18.71**, **PerVFI: 46.59 \(\rightarrow\) 18.63**, **EMA-VFI: 81.16 \(\rightarrow\) 18.91**, and **BiFormer: 43.60 \(\rightarrow\) 18.90**. The **AVG(L,R) baseline** goes from **inf at 1-frame equivalent**, then **30.41 \(\rightarrow\) 18.22**. The paper’s interpretation is that interpolation quality also collapses toward baseline quality as displacement grows.

The benchmark also provides split-wise analysis on MIORe itself. For deblurring, **AdaRevD, LoFormer, and UFPNet** are described as relatively balanced across MIORe splits; **FFTformer** shows a noticeable drop in the **Mild-Medium** group but appears to generalize better to **fast motion**, while **NAFNet** is more stable in slow scenes but weaker under **extreme motion**. For OF, the results are explicitly called **preliminary**; among reported MIORe numbers, **MemFlow** has the best total **EPE = 2.99**. No dedicated VAR-MIORe optical-flow quantitative table is shown in the provided text [2509.06803].

## 6. Limitations, significance, and research role

The paper states several limitations directly. The OF labels are **pseudo-ground truth** and require refinement. Although the corpus is captured in the wild, the final **333 sequences** are **selected**, so the scene distribution is not guaranteed to be statistically representative of all real-world motion. The **Chronos** camera’s internal RAM constrains continuous capture to **5516 frames**. Motion control is implemented indirectly through frame averaging and exposure offset over real captures rather than exact physical motion instrumentation. Finally, because motion blur coexists with **defocus**, **exposure issues**, **lens artifacts**, and adverse weather, failure cases cannot always be attributed purely to motion magnitude [2509.06803].

These caveats are integral to the benchmark’s meaning rather than peripheral to it. VAR-MIORe is presented as a **real-world 1000 FPS, FHD, multi-task benchmark with explicit and controllable motion magnitude variation from minimal to extreme**. Its importance lies in turning **motion amplitude itself into a benchmark axis** and in showing that methods that appear strong on conventional datasets often degrade sharply, or collapse toward baseline quality, when motion magnitude becomes large. The paper therefore frames VAR-MIORe not only as another dataset, but as a **stress-testing protocol for robustness to motion scale**, with direct implications for deblurring, VFI, and related restoration research.

Source: https://www.emergentmind.com/topics/var-miore