Our new X account is live! Follow @wizwand_team for updates

3D Human Pose Estimation on HumanEva-I (test)

8.7Walking S1 Error (mm)

GLA-GCN

Updated 3d ago

Evaluation Results

Method
GLA-GCN 2023.07	8.7	6.8	11.5	10.1	8.2	9.9	9.2	-	-	-	-	-	-	-	-	-	-
Li et al. 2023.07	9.7	7.6	15.8	12.3	9.4	11.2	11.1	-	-	-	-	-	-	-	-	-	-
GLA-GCN 2023.07	12.5	9.1	26.9	18.5	12.7	12.8	15.4	-	-	-	-	-	-	-	-	-	-
Zhang et al. 2023.07	12.7	10.9	17.6	22.6	15.8	17	16.1	-	-	-	-	-	-	-	-	-	-
Liu et al. 2023.07	13.1	9.8	26.8	16.9	12.8	13.3	15.5	-	-	-	-	-	-	-	-	-	-
VideoPose3D (MA) 2018.11	13.9	10.2	46.6	20.9	13.1	13.8	-	23.8	33.7	32	-	-	-	-	-	-	-
Pavllo et al. 2023.07	13.9	10.2	46.6	20.9	13.1	13.8	19.8	-	-	-	-	-	-	-	-	-	-
Li et al. 2023.07	14	10	32.8	19.5	13.6	14.2	17.4	-	-	-	-	-	-	-	-	-	-
Zheng et al. 2023.07	14.4	10.2	46.6	22.7	13.4	13.4	20.1	-	-	-	-	-	-	-	-	-	-
VideoPose3D (SA) 2018.11	14.5	10.5	47.3	21.9	13.4	13.9	-	24.3	34.9	32.1	-	-	-	-	-	-	-
Lee et al. 2018.11	18.6	19.9	30.5	25.7	16.8	17.7	-	42.8	48.1	53.4	-	-	-	-	-	-	-
Lee et al. 2023.07	18.6	19.9	30.5	25.7	16.8	17.7	21.5	-	-	-	-	-	-	-	-	-	-
Ordinal Depth Supervision 2018.05	18.8	12.7	29.2	23.5	15.4	14.5	18.3	-	-	-	-	-	-	-	-	-	-
ordinal depth supervision 2018.05	18.8	12.7	29.2	23.5	15.4	14.5	18.3	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. (+) 2018.11	18.8	12.7	29.2	23.5	15.4	14.5	-	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2023.07	18.8	12.7	29.2	23.5	15.4	14.5	19	-	-	-	-	-	-	-	-	-	-
sequence-to-sequence network 2017.11	19.1	13.6	43.9	23.2	16.9	15.5	22	-	-	-	-	-	-	-	-	-	-
Pose Grammar 2017.10	19.4	16.8	37.4	30.4	17.6	16.3	22.9	-	-	-	-	-	-	-	-	-	-
Fang et al. 2017.11	19.4	16.8	37.4	30.4	17.6	16.3	22.9	-	-	-	-	-	-	-	-	-	-
Fang et al. 2023.07	19.4	16.8	37.4	30.4	17.6	16.3	23	-	-	-	-	-	-	-	-	-	-
Moreno-Noguer 2018.05	19.7	13	24.9	39.7	20	21	26.9	-	-	-	-	-	-	-	-	-	-
Martinez et al. 2018.05	19.7	17.4	46.8	26.9	18.2	18.6	24.6	-	-	-	-	-	-	-	-	-	-
Moreno-Noguer 2018.05	19.7	13	24.9	39.7	20	21	26.9	-	-	-	-	-	-	-	-	-	-
Martinez et al. 2018.05	19.7	17.4	46.8	26.9	18.2	18.6	24.6	-	-	-	-	-	-	-	-	-	-
Moreno-Noguer 2017.05	19.7	13	24.9	39.7	20	21	26.9	-	-	-	-	-	-	-	-	-	-
3d-pose-baseline 2017.05	19.7	17.4	46.8	26.9	18.2	18.6	24.6	-	-	-	-	-	-	-	-	-	-
Moreno-Noguer 2017.05	19.7	13.5	26.5	34.6	17.9	20.1	22	-	-	-	-	-	-	-	-	-	-
Moreno-Noguer 2017.10	19.7	13	24.9	39.7	20	21	26.9	-	-	-	-	-	-	-	-	-	-
Martinez et al. 2017.10	19.7	17.4	46.8	26.9	18.2	18.6	24.6	-	-	-	-	-	-	-	-	-	-
Moreno-Noguer 2017.11	19.7	13	24.9	39.7	20	21	26.9	-	-	-	-	-	-	-	-	-	-
Martinez et al. 2017.11	19.7	17.4	46.8	26.9	18.2	18.6	24.6	-	-	-	-	-	-	-	-	-	-
Martinez et al. 2018.11	19.7	17.4	46.8	26.9	18.2	18.6	-	-	-	-	-	-	-	-	-	-	-
Martinez et al. 2023.07	19.7	17.4	46.8	26.9	18.2	18.6	24.6	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2018.05	22.1	21.9	29	29.8	23.6	26	25.5	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2018.05	22.1	21.9	29	29.8	23.6	26	25.5	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2017.05	22.1	21.9	29	29.8	23.6	26	25.5	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2017.11	22.1	21.9	29	29.8	23.6	26	25.5	-	-	-	-	-	-	-	-	-	-
Coarse-to-Fine Volumetric Prediction 2016.11	22.3	19.5	29.7	28.9	21.9	23.8	24.3	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2017.10	22.3	19.5	29.7	28.9	21.9	23.8	24.3	-	-	-	-	-	-	-	-	-	-
Pavlakos et al. 2018.11	22.3	19.5	29.7	28.9	21.9	23.8	-	-	-	-	-	-	-	-	-	-	-
Lin et al. 2017.11	26.5	20.7	38	41	29.7	29.1	30.8	-	-	-	-	-	-	-	-	-	-
Popa et al. 2017.05	27.1	18.4	39.5	37.6	28.9	27.6	29.9	-	-	-	-	-	-	-	-	-	-
Dual-Source Approach 2017.05	27.4	28.6	32.5	39.9	29.4	31.4	31.5	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2016.11	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2018.05	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2018.05	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2017.05	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2017.05	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2017.10	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2017.11	35.8	32.4	41.6	46.6	41.4	35.4	38.9	-	-	-	-	-	-	-	-	-	-
Bo et al. 2017.05	38.2	32.8	40.2	42	34.7	46.4	39.1	-	-	-	-	-	-	-	-	-	-
Dual-Source Approach 2017.05	39.5	47.3	61.4	55.5	48.3	53.1	50.5	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2016.11	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2018.05	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2018.05	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2017.05	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2017.05	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2017.10	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Kostrikov et al. 2017.11	44	30.9	41.7	57.2	35	33.3	40.3	-	-	-	-	-	-	-	-	-	-
Bo et al. 2016.11	46.4	30.3	64.9	64.5	48	38.2	48.7	-	-	-	-	-	-	-	-	-	-
Bo et al. 2018.05	46.4	30.3	64.9	64.5	48	38.2	48.7	-	-	-	-	-	-	-	-	-	-
Bo et al. 2018.05	46.4	30.3	64.9	64.5	48	38.2	48.7	-	-	-	-	-	-	-	-	-	-
Bo et al. 2017.05	46.4	30.3	64.9	64.5	48	38.2	48.7	-	-	-	-	-	-	-	-	-	-
Bo et al. 2017.11	46.4	30.3	64.9	64.5	48	38.2	48.7	-	-	-	-	-	-	-	-	-	-
Yasin et al. 2017.05	52.2	51	62.8	74.5	72.4	56.8	61.6	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2016.11	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2018.05	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2018.05	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2017.05	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2017.05	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2017.10	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2017.11	65.1	48.6	73.5	74.2	46.6	32.2	56.7	-	-	-	-	-	-	-	-	-	-
Wang et al. 2016.11	71.9	75.7	85.3	62.6	77.7	54.4	71.3	-	-	-	-	-	-	-	-	-	-
Wang et al. 2018.05	71.9	75.7	85.3	62.6	77.7	54.4	71.3	-	-	-	-	-	-	-	-	-	-
Wang et al. 2018.05	71.9	75.7	85.3	62.6	77.7	54.4	71.3	-	-	-	-	-	-	-	-	-	-
Wang et al. 2017.05	71.9	75.7	85.3	62.6	77.7	54.4	71.3	-	-	-	-	-	-	-	-	-	-
Wang et al. 2017.05	71.9	75.7	85.3	62.6	77.7	54.4	71.3	-	-	-	-	-	-	-	-	-	-
Wang et al. 2017.11	71.9	75.7	85.3	62.6	77.7	54.4	71.3	-	-	-	-	-	-	-	-	-	-
Radwan et al. 2016.11	75.1	99.8	93.8	79.2	89.8	99.4	89.5	-	-	-	-	-	-	-	-	-	-
Radwan et al. 2018.05	75.1	99.8	93.8	79.2	89.8	99.4	89.5	-	-	-	-	-	-	-	-	-	-
Radwan et al. 2018.05	75.1	99.8	93.8	79.2	89.8	99.4	89.5	-	-	-	-	-	-	-	-	-	-
Radwan et al. 2017.05	75.1	99.8	93.8	79.2	89.8	99.4	89.5	-	-	-	-	-	-	-	-	-	-
Radwan et al. 2017.05	75.1	99.8	93.8	79.2	89.8	99.4	89.5	-	-	-	-	-	-	-	-	-	-
Radwan et al. 2017.11	75.1	99.8	93.8	79.2	89.8	99.4	89.5	-	-	-	-	-	-	-	-	-	-
Simo-Serra et al. 2017.05	99.6	108.3	127.4	109.2	93.1	115.8	108.9	-	-	-	-	-	-	-	-	-	-
Rempe et al. 2022.05	-	-	-	-	-	-	408	-	-	-	-	-	-	-	-	-	-
Rempe et al. 2022.05	-	-	-	-	-	-	422	-	-	-	-	-	-	-	-	-	-
HUND+SO+GT 2022.05	-	-	-	-	-	-	208	-	-	-	90	76	14	0.51	40	-	-
HUND+SO+GT + Dynamics 2022.05	-	-	-	-	-	-	196	-	-	-	91	74	14	0.27	4	-	-
Videopose 2022.03	-	-	-	-	-	-	-	-	-	-	86.2	-	-	-	-	1,387.4	1,387.1
PoseFormer 2022.03	-	-	-	-	-	-	-	-	-	-	104.7	-	-	-	-	1,470.4	1,452
PoseLifter 2022.03	-	-	-	-	-	-	-	-	-	-	240.2	-	-	-	-	1,263	1,129.3
RIE 2022.03	-	-	-	-	-	-	-	-	-	-	92	-	-	-	-	1,375.8	1,369.5
Ray3D 2022.03	-	-	-	-	-	-	-	-	-	-	83.9	-	-	-	-	477.8	468.6
Videopose 2022.03	-	-	-	-	-	-	-	-	-	-	85.1	-	-	-	-	284.6	283.1
PoseFormer 2022.03	-	-	-	-	-	-	-	-	-	-	79.4	-	-	-	-	260.7	253.9
PoseLifter 2022.03	-	-	-	-	-	-	-	-	-	-	3,690.2	-	-	-	-	15,170.6	16,082.6
RIE 2022.03	-	-	-	-	-	-	-	-	-	-	82	-	-	-	-	272.9	285.1
Ray3D 2022.03	-	-	-	-	-	-	-	-	-	-	81.5	-	-	-	-	121.5	99.3