The increasing demand for augmented reality and robotics is driving the need for articulated object reconstruction with high scalability. However, existing settings for reconstructing from discrete articulation states or casual monocular videos require non-trivial axis alignment or suffer from insufficient coverage, limiting their applicability. In this paper, we introduce FreeArtGS, a novel method for reconstructing articulated objects under free-moving scenario, a new setting with a simple setup and high scalability. FreeArtGS combines free-moving part segmentation with joint estimation and end-to-end optimization, taking only a monocular RGB-D video as input. By optimizing with the priors from off-the-shelf point-tracking and feature models, the free-moving part segmentation module identifies rigid parts from relative motion under unconstrained capture. The joint estimation module calibrates the unified object-to-camera poses and recovers joint type and axis robustly from part segmentation. Finally, 3DGS-based end-to-end optimization is implemented to jointly reconstruct visual textures, geometry, and joint angles of the articulated object. We conduct experiments on two benchmarks and real-world free-moving articulated objects. Experimental results demonstrate that FreeArtGS consistently excels in reconstructing free-moving articulated objects and remains highly competitive in previous reconstruction settings, proving itself a practical and effective solution for realistic asset generation. The project page is available at: https://freeartgs.github.io/
Figures & tables
Figure 1: FreeArtGS reconstructs articulated object under the free-moving scenario, in which both joint and object pose move in an unconstrained manner.
Figure 2: Overview of FreeArtGS. It consists of three modules: (1) Part Segmentation from free-moving video; (2) Joint Estimation with estimated part transforms; (3) End-to-end Optimization for articulated Gaussian Splatting.
Joint Type
Method
Axis (deg) ↓
Position (cm) ↓
State (deg/cm) ↓
CD-w (cm) ↓
CD-m (cm) ↓
CD-s (cm) ↓
PSNR (dB) ↑
Revolute
ArticulateAnything [ 16 ]
42.00 ± 46.48
59.38 ± 62.19
-
-
-
-
-
Video2Articulation [ 24 ]
20.00 ± 28.81
16.31 ± 28.78
27.37 ± 19.06
2.29 ± 3.40
10.74 ± 16.35
1.87 ± 1.59
-
Ours w/o Smooth Loss
28.01 ± 29.23
17.73 ± 30.79
18.74 ± 20.96
5.72 ± 7.39
14.37 ± 15.89
5.64 ± 9.17
10.60 ± 4.61
Ours w/o Init Loss
9.35 ± 13.50
19.58 ± 39.17
14.64 ± 19.90
0.75 ± 0.90
1.90 ± 3.11
1.14 ± 2.60
13.07 ± 7.26
Ours w/o Noise Resistance
4.75 ± 7.83
2.22 ± 6.89
1.30 ± 1.09
0.17 ± 0.15
0.48 ± 0.79
1.10 ± 2.72
22.65 ± 3.13
Ours w/o Blended Rendering
1.72 ± 2.38
1.88 ± 6.23
1.88 ± 2.54
0.12 ± 0.07
0.34 ± 0.52
1.05 ± 2.61
22.23 ± 2.64
Table 1: Results and Ablation Studies on FreeArt-21. Metrics are reported as mean ± std over all test videos. Lower ( ↓ ) is better for the joint and geometry metrics, higher ( ↑ ) is better for the rendering metric. The best results are highlighted in bold .
Figure 3: Qualitative Results on FreeArt-21. We visualize both the articulation and rendering results of our method. The red part and the blue part are the identified parts by our method.
Joint Type
Method
Axis (deg) ↓
Position (cm) ↓
State (deg/cm) ↓
Revolute
Articulate-Anything [ 16 ]
46.98 ± 45.27
81.00 ± 40.00
N/A
RSRD [ 15 ]
67.06 ± 29.22
203.00 ± 748.00
59.02 ± 34.38
Video2Articulation [ 24 ]
18.34 ± 32.09
13.00 ± 25.00
14.32 ± 26.35
Ours
1.77 ± 2.87
1.31 ± 1.81
3.69 ± 6.60
Prismatic
Articulate-Anything [ 16 ]
52.71 ± 44.69
-
N/A
RSRD [ 15 ]
69.91 ± 24.07
-
70.00 ± 48.00
Table 2: Results on Video2Articulation-S Dataset. We report joint estimation results (top) and geometry reconstruction results (bottom). The best results are in bold .
Figure 4: Qualitative Results on Real-world Objects. Our method successfully reconstructs all the objects with correct joints, geometries, and textures.
Objects
Axis (deg) ↓
Position (cm) ↓
CD (cm) ↓
PSNR (dB) ↑
Drawer
1.06
-
1.91
19.82
Bin
4.68
0.20
3.46
23.75
Case
0.10
0.10
1.89
22.77
Lid
8.92
0.40
3.36
22.39
Fan
1.53
1.30
1.78
21.95
Cabinet
0.10
3.30
2.53
23.71
Table 3: Quantitative Results on Real-world Objects. The rotation and translation errors are clipped to 0.1∘ and 0.1 cm, respectively, which correspond to the smallest annotation units.
Figure 5: Illustration of Part Solver Module.
Joint Type
Method
Axis(deg) ↓
Position(cm) ↓
State(deg/cm) ↓
CD-w(cm) ↓
CD-m(cm) ↓
CD-s(cm) ↓
PSNR(dB) ↑
Revolute
2% Depth Noise
1.41 ± 1.49
0.51 ± 0.58
1.22 ± 1.27
0.23 ± 0.30
0.30 ± 0.27
1.05 ± 2.47
23.52 ± 4.65
AllTracker → CoTracker3
0.88 ± 0.84
0.46 ± 0.86
1.68 ± 1.30
0.79 ± 2.19
0.80 ± 1.08
1.47 ± 2.43
23.87 ± 4.94
DINOv3 → DINOv2
1.64 ± 1.22
0.32 ± 0.47
1.21 ± 0.77
0.11 ± 0.11
0.28 ± 0.27
1.04 ± 2.52
23.74 ± 4.41
Ours
1.04 ± 1.03
0.29 ± 0.36
1.43 ± 1.20
0.14 ± 0.13
0.28 ± 0.36
0.97 ± 2.69
24.02 ± 3.15
Prismatic
2% Depth Noise
1.86 ± 2.51
-
0.92 ± 0.66
0.34 ± 0.13
1.07 ± 1.18
1.78 ± 3.03
21.75 ± 2.99
AllTracker → CoTracker3
1.98 ± 3.09
-
1.12 ± 0.68
0.79 ± 1.08
0.98 ± 1.22
0.94 ± 1.18
21.85 ± 3.64
Table 4: Results of Robustness Analysis.
Method
Rot. Error (deg) ↓
Pos. Error (cm) ↓
Video2Articulation* [ 24 ]
4.621
9.246
Ours
2.483
1.526
Table 5: Results of Camera Pose Estimation on the Video2Articulation-S Dataset. * indicates that the results are taken from the original paper.
Figure 6: Visualization of camera pose trajectories in FreeArt-21. The black line shows the ground truth camera trajectory, and the yellow line shows our estimated camera trajectory.
Method / mIoU (%) ↑
FreeArt-21
Video2Articulation-S
Video2Articulation [ 24 ]
34.61
59.65*
Ours
90.88
84.92
Table 6: Results of Part Segmentation. * indicates that the results are taken from the original paper.
Figure 7: Visualization of all objects in FreeArt-21.
Figure 8: Visualization of our reconstruction results on FreeArt-21.