What’s new

07.04.17Subjective study results are now available.
26.12.16We published evaluation results for Self-Adaptive Matting [17].
16.11.16We published evaluation results for Deep Matting [16].
6.04.16We published evaluation results for Sparse Sampling Matting [14].
15.12.151) New sequences with natural hairs including 3 public sequences.
2) New metrics of temporal coherency chosen by careful analysis (see [3]).
3) New trimap generation method (more natural-looking and accurate).
4) Better ground-truth quality owing to correction of lightning changes during capturing.
5) Improvement in website loading speed and interface.
7.09.15We published the paper with our benchmark description [3].
30.12.14We published results for multiple levels of trimaps; use drop-down menu at the top left corner to switch levels.
29.12.14We added general ranking to the rating table.
10.11.14“Sparse codes as Alpha Matte” was added.
26.09.14Source sequences are available for online view now. Full screen mode was added.
30.08.14Composite sequences are available now.
27.08.14“Refine edge tool in Adobe After Effects” was added.
25.08.14The official opening.



The Video­Mat­ting pro­ject is the first pub­lic ob­jec­tive bench­mark for video-mat­ting meth­ods. It con­tains scat­ter plots and rat­ing ta­bles for dif­fer­ent qual­ity met­rics. In ad­di­tion, re­sults for par­tic­i­pat­ing meth­ods are avail­able for view­ing on a player equipped with a mov­able zoom re­gion. We be­lieve our work will help rank ex­ist­ing meth­ods and aid de­vel­op­ers of new meth­ods in im­prov­ing their re­sults.


The data set con­sists of five mov­ing ob­jects cap­tured in front of a green plate and seven cap­tured us­ing the stop-mo­tion pro­ce­dure de­scribed be­low. We com­posed the ob­jects over a set of back­ground videos with var­i­ous lev­els of 3D cam­era mo­tion, color bal­ance, and noise. We pub­lished ground-truth data for two stop-mo­tion se­quences and hid the rest to en­sure fair­ness of the com­par­i­son.

Us­ing thresh­old­ing and mor­pho­log­i­cal op­er­a­tions on ground-truth al­pha mattes, we gen­er­ated nar­row trimaps. Then, we di­lated the re­sults us­ing graph­cut-based en­ergy min­i­miza­tion which pro­vides us with more hand­made-look­ing trimaps than com­mon mor­pho­log­i­cal di­la­tion.

Chroma Keying

green screen
stop motion
Alpha mattes from chroma keying and stop-motion capture for the same image region. The stop-motion result is significantly better at preserving details.

Chroma key­ing is a com­mon prac­tice of the cin­ema in­dus­try: the cin­e­matog­ra­pher cap­tures an ac­tor in front of a green or blue screen, then the VFX ex­pert re­places the back­ground us­ing spe­cial soft­ware. Our eval­u­a­tion uses five green-screen video se­quences with a sig­nif­i­cant amount of semi­trans­parency (e.g., hair or mo­tion blur), pro­vided to us by Hol­ly­wood cam­era work. We ex­tract al­pha mattes and cor­re­spond­ing fore­grounds us­ing The Foundry Key­light. Chroma key­ing en­ables us to get al­pha mattes of nat­ural-look­ing ob­jects with ar­bi­trary mo­tion. Nev­er­the­less, this tech­nique can’t guar­an­tee that the al­pha maps are nat­ural, be­cause it as­sumes the screen color is ab­sent from the fore­ground ob­ject. To get al­pha maps that have a very nat­ural ap­pear­ance, we use the stop-mo­tion method.

Stop Motion

One-step capture over different backgrounds. We use checkerboard backgrounds instead of solid ones to eliminate screen reflection.

We de­signed the fol­low­ing pro­ce­dure to per­form stop-mo­tion cap­ture: A fuzzy toy is placed on the plat­form in front of an LCD mon­i­tor. The toy ro­tates in small, dis­crete steps along a pre­de­fined 3D tra­jec­tory, con­trolled by two ser­vos con­nected to a com­puter. Af­ter each step the dig­i­tal cam­era in front of the setup cap­tures the mo­tion­less toy against a set of back­ground im­ages. At the end of this process, the toy is re­moved and the cam­era again cap­tures all of the back­ground im­ages.

We paid spe­cial at­ten­tion to avoid­ing re­flec­tions of the back­ground screen in the fore­ground ob­ject. These re­flec­tions can lead to false trans­parency that is es­pe­cially no­tice­able in non­trans­par­ent re­gions. To re­duce the amount of re­flec­tion we used checker­board back­ground im­ages in­stead of solid col­ors, thereby ad­just­ing the mean color of the screen to be the same for each back­ground.

At the end we cor­rected global light­ing changes caused by light bulb flick­er­ing. Thus fi­nally we ob­tain al­pha mattes with less than 1% of noise level. The de­tailed de­scrip­tion of ground-truth ex­trac­tion meth­ods is given in [3].

Evaluation Methodology

Our com­par­i­son in­cludes both im­age- and video-mat­ting meth­ods. We ap­ply each mat­ting method to the videos in our data set, and then com­pare the re­sults us­ing the fol­low­ing met­rics of per-pixel ac­cu­racy and tem­po­ral co­herency (look into our pa­per [3] for com­par­i­son of dif­fer­ent met­rics):

Equationmatrix left-parenthesis 1 right-parenthesis SSDA equals 1 number-sign sigma-summation t sigma-summation p left-parenthesis alpha p comma t minus alpha caret p comma t right-parenthesis 2 period
Equationmatrix left-parenthesis 2 right-parenthesis dtSSD equals 1 number-sign sigma-summation t sigma-summation p left-parenthesis d alpha p comma tdt minus d alpha p comma tGTdt right-parenthesis 2 comma
Equationmatrix left-parenthesis 3 right-parenthesis MESSDdt equals 1 number-sign sigma-summation t comma p vertical-bar left-parenthesis alpha p comma t minus alpha caret p comma t right-parenthesis 2 minus left-parenthesis alpha p plus vp comma t plus 1 minus alpha caret p plus vp comma t plus 1 right-parenthesis 2 vertical-bar comma

Here Equationnumber-sign de­notes to­tal num­ber of pix­els, Equationalpha Subscript p comma t and EquationModifyingAbove alpha With caret Subscript p comma t de­note trans­parency val­ues of video mat­ting un­der con­sid­er­a­tion and ground truth cor­re­spond­ingly at pixel Equationp of frame Equationt, and Equationv Subscript p de­notes mo­tion vec­tor at pixel Equationp. We use op­ti­cal-flow al­go­rithm [11] com­puted for ground-truth se­quences. It is worth not­ing that mo­tion-aware met­rics will not give un­fair ad­van­tage to mat­ting meth­ods based on the sim­i­lar mo­tion es­ti­ma­tion method since they do not have ground truth se­quence. The de­tailed de­scrip­tion of used qual­ity met­rics is given in [3].

Public Sequences

For the train­ing pur­poses we pub­lish here three test se­quences with their ground-truth trans­parency maps. De­vel­op­ers and re­searchers are wel­come to use these se­quences, but we ask to cite us [3]


We in­vite de­vel­op­ers of video-mat­ting meth­ods to use our bench­mark. We will eval­u­ate the sub­mit­ted data and re­port scores to the de­vel­oper. In cases where the de­vel­oper specif­i­cally grants per­mis­sion, we will pub­lish the re­sults on our site. We can also pub­lish anony­mous scores for blind-re­viewed pa­pers. To par­tic­i­pate, sim­ply fol­low these steps:

  1. Download the data set containing our sequences: City, Flowers, Concert, Rain, Snow, Vitaliy, Artem, Slava, Juneau, Woods,
  2. Apply your method to each of our test cases
  3. Upload the alpha and foreground sequences to any file-sharing service. We kindly ask you to maintain these naming and directory-structure conventions. If your method doesn't explicitly produce the foreground images you can skip uploading them; in this case, we will generate them using method proposed in [7].
  4. Fill in this form to provide information about your method

Con­tact us by email with any ques­tions or sug­ges­tions at ques­tions@video­mat­ting.com.

Cite Us

To re­fer to our eval­u­a­tion or test se­quences in your work cite our pa­per [3].

	title={Perceptually Motivated Benchmark for Video Matting},
	author={Mikhail Erofeev and Yury Gitman and  Dmitriy Vatolin and Alexey Fedorov and Jue Wang},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},



Trimap size:
Trimap available for each frame
Bayesian Matting [2]20019.858.761151.661010.796144.531030.361053.281043.701070.501196.699140.1211
Robust Matting [12]20076.137.54826.1529.61388.15518.52633.31834.15546.91978.56880.417
Refine Edge [15]20137.737.78938.4888.642129.56827.47931.36736.65938.756102.021094.509
Closed Form [7]20085.631.34431.80510.968149.481115.86323.67530.15330.60369.94682.138
Learning Based [13]20093.829.98233.08610.847134.82915.23223.35331.46430.20158.38256.602
Nonlocal mattings [5]201111.754.211092.371233.8612243.471345.131264.051261.751267.3010135.0812171.7912
Shared Matting [4]20106.736.79746.72910.30587.64424.41835.18935.26745.23868.52567.175
Comprehensive Samplings [10]20085.734.48630.18412.10979.60216.17425.02635.25641.05777.72770.186
KNN Matting [1]201210.967.831292.221164.7313112.46732.961161.501150.871179.2012113.4111123.6310
Spectral Mattings [6]201212.686.3213105.241313.1510210.041248.761365.721368.8313109.4413157.1813182.8213
Sparse Samplings [14]20164.833.42533.5079.94481.06318.42523.64435.83838.00565.17364.124
Deep Matting [16]20162.827.33121.40120.331144.93119.23719.99130.00230.55246.18146.391
Self-Adaptive [17]20162.830.05329.3038.02196.44614.32121.96229.92135.97466.05461.493
Bayesian Matting [2]200110.873.181167.921016.0610157.281139.591160.371056.051185.5312115.8811169.3311
Robust Matting [12]20077.448.19932.89514.72899.81525.96742.51838.88759.08993.79896.138
Refine Edge [15]20137.342.23841.74810.451127.02831.40935.08739.46842.916108.71997.769
Closed Form [7]20085.434.53435.86613.355154.121021.64627.19533.01335.45381.48592.757
Learning Based [13]20094.133.44337.63713.234138.31919.95526.51334.51434.70268.41263.532
Nonlocal mattings [5]201111.555.041097.851131.8111242.751346.941267.111265.091269.0110144.6612176.6112
Shared Matting [4]20107.241.44756.75914.14696.71427.66842.92940.05958.17883.02678.916
Comprehensive Samplings [10]20084.938.43628.29214.45783.85216.84227.29637.07547.38789.96778.455
KNN Matting [1]201210.679.351298.711266.2413112.81734.601064.171154.441084.3811112.1310128.8610
Spectral Mattings [6]201212.599.0513125.291316.049214.421259.611374.551377.2513122.8913162.7313188.1613
Sparse Samplings [14]20163.233.20231.92312.34385.52317.66324.64237.78639.87471.05369.943
Deep Matting [16]20162.128.67121.10141.801244.81116.57121.04130.57130.94146.40150.271
Self-Adaptive [17]20164.034.71532.21411.022101.76618.61426.53431.23240.01575.29470.684
Bayesian Matting [2]200111.298.131295.951038.5411170.761159.511280.721267.6411122.0811135.5911179.2511
Robust Matting [12]20078.367.711049.02719.876109.79644.101062.18950.54881.499118.099115.969
Refine Edge [15]20136.950.31551.81815.853125.25840.73945.38746.43752.225118.8810103.307
Closed Form [7]20085.645.04441.69424.198159.471030.09636.85639.20246.973104.075110.268
Learning Based [13]20094.344.97343.75521.907143.54926.50433.89440.44445.93290.35381.222
Nonlocal mattings [5]201110.956.768113.901233.9710241.311350.741177.141168.371276.438159.0812181.1412
Shared Matting [4]20107.455.84779.09919.785107.45532.94760.78851.479122.3312107.34697.226
Comprehensive Samplings [10]20084.956.79937.36316.96487.64224.04334.32543.69564.637110.50793.354
KNN Matting [1]20129.895.8911112.701166.1213113.10737.18871.261060.031098.4210110.878138.6610
Spectral Mattings [6]201212.5116.8713157.371326.129218.941276.221388.741391.6913148.7013169.3313193.4613
Sparse Samplings [14]20162.843.32234.66214.78289.79319.23229.92244.59651.94485.44281.773
Deep Matting [16]20162.131.29123.16153.021244.93118.20124.36132.04137.60150.48154.761
Self-Adaptive [17]20164.351.29646.56614.351106.71427.55531.98339.35354.80695.90495.735
Bayesian Matting [2]200111.035.931165.24109.288104.681227.611035.041346.411060.341288.4612121.9112
Robust Matting [12]20076.022.32732.7327.26347.271016.04618.72834.47524.96653.42842.405
Refine Edge [15]20135.214.41144.0876.30240.69618.36816.53434.93617.83253.43943.607
Closed Form [7]20084.116.37241.2957.67535.14514.59316.40332.43317.37151.40547.719
Learning Based [13]20094.017.29442.9967.88734.03314.44217.07634.37418.37346.02239.463
Nonlocal mattings [5]201111.629.0710110.761114.1012120.011329.821232.691159.911236.2211108.431396.8211
Shared Matting [4]20107.322.72859.5997.72645.92920.34922.35938.02929.56947.45338.192
Comprehensive Samplings [10]20086.622.78939.97413.831133.11215.61417.51737.09725.72753.33745.048
KNN Matting [1]201210.736.1612115.081225.581344.09727.801131.611056.081134.801087.531175.3610
Spectral Mattings [6]201211.265.5013131.74134.781103.611139.031334.391263.211364.401387.4410122.5313
Sparse Samplings [14]20166.721.78644.8589.86934.07416.94716.55537.40825.84851.77642.986
Deep Matting [16]20162.816.79325.93113.121026.74115.64514.58131.85119.25445.03137.271
Self-Adaptive [17]20163.818.76538.8937.37445.75813.36115.44232.26220.12549.15440.484
Bayesian Matting [2]200111.546.961277.271011.328111.601233.851239.541356.861075.5013102.7012151.6013
Robust Matting [12]20077.126.36837.7338.35552.071019.18721.45837.08628.55857.78947.957
Refine Edge [15]20134.515.24147.3586.58240.54619.40816.77335.74418.24254.11743.434
Closed Form [7]20084.717.25342.8067.11335.79516.70617.10533.94318.08152.82648.949
Learning Based [13]20094.118.97445.1477.45434.62216.32518.09636.77519.50448.00239.162
Nonlocal mattings [5]201111.229.6710114.941114.8410121.991331.031133.661161.341237.0410117.021393.1811
Shared Matting [4]20107.624.21768.2599.00749.95921.88924.25942.74937.141151.50339.513
Comprehensive Samplings [10]20086.526.80935.36215.591134.80315.73218.29739.17827.81755.93848.738
KNN Matting [1]201210.438.8511120.081226.541344.01728.251031.831057.581135.78985.371175.3410
Spectral Mattings [6]201211.072.4513155.21135.031104.951144.501335.841264.961366.661285.2310130.3212
Sparse Samplings [14]20165.123.66639.99411.54935.17416.17416.48238.93727.46651.93444.555
Deep Matting [16]20162.417.21224.74121.031227.68114.33114.98131.41119.05345.10136.961
Self-Adaptive [17]20164.920.61540.2058.93646.73815.80317.03432.95221.69552.15544.906
Bayesian Matting [2]200112.368.3212110.781029.7013119.541247.661252.241367.6013118.011398.3412148.1713
Robust Matting [12]20077.632.72953.1269.85556.831025.40927.41842.73636.49862.55952.276
Refine Edge [15]20133.915.82157.5786.58240.25620.67718.04237.30419.11155.24444.184
Closed Form [7]20084.018.75348.3047.76336.78419.57518.21435.50219.15257.13653.957
Learning Based [13]20093.821.35450.9157.84435.59218.92319.37639.75521.31451.66241.993
Nonlocal mattings [5]201111.031.258129.131117.9610123.491331.741136.131262.321138.8210127.061391.3711
Shared Matting [4]20107.827.95793.58910.60755.32925.04827.72948.28965.061158.63741.342
Comprehensive Samplings [10]20086.734.621042.66316.53935.69319.23421.26744.33833.28760.01855.618
KNN Matting [1]201210.142.7111130.141226.501243.95729.191033.061059.221038.04982.471076.1610
Spectral Mattings [6]201210.981.1413183.76135.491108.671154.311335.811167.311270.451282.7411128.4112
Sparse Samplings [14]20164.727.39641.55212.15836.95517.04218.19343.94732.67654.81349.265
Deep Matting [16]20162.318.72228.24123.651128.18115.36116.66131.80120.98347.83136.951
Self-Adaptive [17]20165.925.66556.94710.23649.29819.97618.71537.00325.31555.89557.119
Bayesian Matting [2]200111.01.85113.58100.12913.33110.74112.31131.47104.51128.011116.2312
Robust Matting [12]20077.20.8990.9420.0734.82100.2570.8880.7761.5893.8993.929
Refine Edge [15]20135.90.5541.4880.0623.9970.3480.5470.7050.8143.8083.166
Closed Form [7]20083.50.4911.3560.0962.8820.1930.3540.6110.6122.7533.387
Learning Based [13]20093.60.5031.4270.0972.8830.1820.3430.6840.6432.3722.432
Nonlocal mattings [5]201111.91.56108.17120.701220.97131.11121.88122.51132.351111.171313.3711
Shared Matting [4]20107.60.7982.7390.0954.5090.3591.0090.9191.4382.9452.945
Comprehensive Samplings [10]20086.60.7471.0630.19103.1550.2250.3960.8681.2473.6573.408
KNN Matting [1]201210.32.28126.40111.22133.4760.61101.02101.73111.94106.42107.7310
Spectral Mattings [6]201211.67.031313.25130.08415.36122.09131.84112.45126.59138.711219.9713
Sparse Samplings [14]20165.70.6661.3050.1183.0940.2560.3850.8671.0462.9462.764
Deep Matting [16]20162.60.5020.4810.32111.1510.2240.2510.6330.5711.8411.841
Self-Adaptive [17]20163.50.5851.1540.0614.4380.1610.3320.6220.8752.9142.663
Bayesian Matting [2]200111.83.12125.07100.22916.65121.34123.03132.501111.601318.031325.6313
Robust Matting [12]20078.21.3491.2640.1566.19100.5091.4290.9982.3895.2195.449
Refine Edge [15]20135.10.6631.6280.0813.9570.4270.6370.7640.9244.0573.163
Closed Form [7]20084.00.6121.4660.1133.2430.2760.4550.6930.8023.2933.937
Learning Based [13]20093.50.6641.5970.1143.2220.2640.4220.7850.8432.9622.872
Nonlocal mattings [5]201111.31.62108.93120.781121.62131.22112.06112.77122.501013.521212.3011
Shared Matting [4]20108.21.0373.5190.1775.7790.4881.51101.6292.57114.0463.786
Comprehensive Samplings [10]20086.61.0980.9520.26103.6360.2750.4860.9861.7174.6584.428
KNN Matting [1]20129.53.05116.83111.32133.4940.70101.1181.96102.2886.30108.5510
Spectral Mattings [6]201211.29.361320.54130.11216.55113.15132.36122.91137.89128.871122.4412
Sparse Samplings [14]20165.00.7961.1830.1883.5250.2630.4440.9871.2963.3943.364
Deep Matting [16]20162.10.5610.4711.05121.1810.1910.2710.6710.5711.9011.991
Self-Adaptive [17]20164.50.7651.3450.1254.9480.2420.4330.6921.1253.6653.625
Bayesian Matting [2]200112.56.711211.09113.971319.88123.00125.18134.081337.451316.501325.0813
Robust Matting [12]20079.22.40102.5480.2367.65101.18102.70111.5784.16107.26107.159
Refine Edge [15]20133.80.8122.1860.1213.9150.5460.8470.9031.1424.5143.392
Closed Form [7]20084.10.8831.7040.2473.6440.4640.7250.8521.2034.3635.216
Learning Based [13]20093.51.0341.8850.2143.6230.4330.6121.0051.3244.2723.743
Nonlocal mattings [5]201110.41.76811.85121.101022.25131.29112.4693.00112.88716.361212.2011
Shared Matting [4]20108.41.7476.0290.2787.4690.8582.70102.2697.65116.3585.055
Comprehensive Samplings [10]20086.92.2891.5530.3293.9760.5450.7361.3563.0186.5996.118
KNN Matting [1]20128.74.24117.84101.32113.5220.8891.3882.34103.0496.1779.8710
Spectral Mattings [6]201211.113.171331.38130.18218.16115.33132.78123.741210.24129.191122.9212
Sparse Samplings [14]20164.61.3751.3220.2354.0270.3520.6531.4372.1964.6754.664
Deep Matting [16]20162.10.6410.6011.34121.2210.2210.3710.7510.7812.2712.211
Self-Adaptive [17]20165.71.4262.4970.1935.4880.5570.6640.9941.9455.2066.047
  1. city
  2. rain
  3. concert
  4. flowers
  5. snow
  6. Slava
  7. Vitaliy
  8. Artem
  9. juneau
  10. woods
  1. Source
  2. Trimap
  3. BM [2]
  4. RM [12]
  5. RE [15]
  6. CF [7]
  7. LB [13]
  8. NlM [5]
  9. ShM [4]
  10. CS [10]
  11. KNN [1]
  12. SpM [6]
  13. SpSM [14]
  14. DM [16]
  15. SAM [17]
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Integral Plots

Subjective comparison

We carried out subjective comparison of 13 matting methods using Subjectify.us platform. We applied matting methods to videos from our dataset and then uploaded videos containing extracted foreground objects and ground-truth sequences to Subjectify.us. The platform hired study participants and showed them these videos in pairwise fashion. For each pair, participants were asked to choose the video with better visual quality or indicate that they are approximately equal. Each study participant compared 30 pairs including 4 hidden quality-control comparisons between ground truth and a low-quality method; answers of 23 participants were rejected, since they failed at least one quality-control question. In total 10556 answers from 406 participants were collected. Bradley-Terry [18] and Crowd Bradley-Terry [19] models were used to convert pairwise comparisons to subjective ranks. The study report generated by the platform is shown below.

Multidimensional Analysis


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[14]Lev­ent Kara­can, Aykut Er­dem, Erkut Er­dem. Al­pha Mat­ting with KL-Di­ver­gence Based Sparse Sam­pling, IEEE Trans­ac­tions on Im­age Pro­cess­ing, 2017.
[15]http://​www.adobe.com/​en/​prod­ucts/​af­ter­ef­fects.html, Re­fine Edge tool in Adobe Af­ter Ef­fects CC.
[16]Ning Xu, Brian Price, Scott Co­hen, and Thomas Huang. Deep Mat­ting. In Com­puter Vi­sion and Pat­tern Recog­ni­tion (CVPR), 2017
[17]Self-Adap­tive Mat­ting. Anony­mous sub­mis­sion.
[18]Ralph Al­lan Bradley and Mil­ton E Terry. Rank analy­sis of in­com­plete block de­signs: I. the method of paired com­par­isons. Bio­metrika, 39(3/​4):324–345, 1952.
[19]Chen, Xi, et al. Pair­wise rank­ing ag­gre­ga­tion in a crowd­sourced set­ting. Pro­ceed­ings of the sixth ACM in­ter­na­tional con­fer­ence on Web search and data min­ing. ACM, 2013.