Articulation in Motion: Prior-free Part Mobility Analysis for Articulated Objects By Dynamic-Static Disentanglement
Authors: Hao Ai
Wenjie Chang
Jianbo Jiao
Ales Leonardis
Eyal Ofek
Venue: ICLR 2026 Poster (2026)
Keywords: Articulated object understanding
dual-Gaussian representation
Prior-free Motion-based part segmentation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Articulated objects are ubiquitous in daily life. Our goal is to achieve a high-quality reconstruction, segmentation of independent moving parts, and analysis of articulation. Recent methods analyse two different articulation states and perform per-point part segmentation, optimising per-part articulation using cross-state correspondences, given a priori knowledge of the number of parts. Such assumptions greatly limit their applications and performance. Their robustness is reduced when objects cannot be clearly visible in both states. To address these issues, in this paper, we present a new framework, *Articulation in Motion (AiM)*. We infer part-level decomposition, articulation kinematics, and reconstruct an interactive 3D digital replica from a user–object interaction video and a start-state scan. We propose a dual-Gaussian scene representation that is learned from an initial 3DGS scan of the object and a video that shows the movement of separate parts. It uses motion cues to segment the object into parts and assign articulation joints. Subsequently, a robust, sequential RANSAC is employed to achieve part mobility analysis \textit{without any part-level structural priors}, which clusters moving primitives into rigid parts and estimates kinematics while automatically determining the number of parts. The proposed approach separates the object into parts, each represented as a 3D Gaussian set, enabling high-quality rendering. Our approach yields higher quality part segmentation than previous methods, without prior knowledge.
Extensive experimental analysis on both simple and complex objects validates the effectiveness and strong generalisation ability of our approach. Project page: https://haoai-1997.github.io/AiM/.

Published as a conference paper at ICLR 2026
Articulation in Motion: PRIOR-FREE PART MOBIL- ITY ANALYSIS FOR ARTICULATED OBJECTS BY DYNAMIC-STATIC DISENTANGLEMENT
Hao Ai Wenjie Chang" " .ianbo Jiao" Ales Leonardis": Eyal Ofek fr.1
School of Computer Science, University of Birmingham, Birmingham, UK 2 University of Science and Technology of China
ABSTRACT
Articulated objects are ubiquitous in daily life. Our goal is to achieve a high- quality reconstruction, segmentation of independent moving parts, and analysis of articulation. Recent methods analyse two different articulation states and perform per-point part segmentation, optimising per-part articulation using cross-state cor- respondences, given priori knowledge of the number of parts. Such assump- tions greatly limit their applications and performance. Their robustness is reduced when objects cannot be clearly visible in both states. To address these issues, in this paper, we present a new framework, Articulation in Motion (AIM). We infer part-level decomposition, articulation kinematics, and reconstruct interactive 3D digital replica from user-object interaction video and a start-state scan. We propose dual-Gausse
scene representation that is learned from initial 3DGS scan of the object and a video that shows the movement of separate parts. It uses motion cues segment the object into parts and assign articulation joints. Subse- quently, robust, sequential RANSAC is employed to achieve part mobility analy- sis without any part-level structural priors, which clusters moving primitives into rigid parts and estimates kinematics while automatically determining the number parts. The proposed approach separates the object into parts, each represented as a 3D Gaussian set, enabling high-quality rendering. Our approach yields higher quality part segmentation than previous methods, without prior knowledge. Ex- tensive experimental analysis on both simple and complex objects validates the effectiveness and strong generalisation ability of our approach. Project page: https://haoai- 1997. githhubii//AMMI.
"Motion is the cause of all life."
Leonardo da Vinci
INTRODUCTION
Everyday environments are abounded with articulated objects", composed of multiple rigid parts linked by joints (Mueller, 2019) (e.g. doors with revolute joints and drawers with prismatic joints). Modelling of such objects is valuable for practical applications across scene understanding (Jia et al,, 2024; Huang et al., 2024b), robotics (Kerr et al., 2024; Wu et al., 2025b), mixed reality (MR) (Taylor et al., 2020; Jiang et al., 2024), and embodied AI applications (Puig et al., 2023; Zhou et al., 2025). Advances in neural 3D representations (Mildenhall al,, 2021; Kerbl et al., 2023; MacSwayne et al., 2025) enable high-fidelity object-level 3D reconstruction: however, reconstructing part-level structure, articulation dynamics, and functionality of articulated objects remains challenging.
Substantial efforts have been devoted to building 3D physics-consistent and interaction-ready asser- tions of articulated objects from RGB RGB-D observations (Wei al., 2022; Song al., 2024;
* "quual contribution. *Conespondeece: e w.f.fee@eee keeeke..nn.... uk. "Equal advice.
In this work, only discuss the human-made articulated objects with rigid parts.

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Start
End
DTA
ArtGS
GT
Ours
Figure 1: Left: Prior two-state methods often degrade on the sequences from closed-start to open-end. Right:
Results of the proposed AIM, compared to ground truth (GT) geometry.
Wu et al., 2025a). Some approaches (Mu et al., 2021; Jiang et al., 2022h; Heppert et al., 2023) rely on the parameters of known joints in learning articulated shape representations. However, col- lecting such data at scale can be time-consuming and often has limited generalisation to previously unseen objects. To mitigate these issues, some unsupervised, part-level reconstruction methods have been proposed (Liu et al., 2023a; Deng et al., 2024; Weng et al., 2024; Liu et al., 2025). Most of them assume multi-view observations of the objects in two distinet articulation states, denoted as start and end states, Liu et al. (2023a); Deng et al. (2024) recovered a deformation field between the NeRF-based start-state and end-state geometries. However, optimisation is unstable and highly sensitive to initialisation. Similarly, the 3D Gaussian splatting (3DGS) based method (Wu et al., 2025a) learns a per-Gaussian start-to-end deformation field, enabling static-dynamic segmentation of up to one moving part per step. As deformation is defined over all Gaussians, threshold based separation is prone to noise. Lately, given known number of articulated parts, DTA (Weng al,, 2024) simultaneously reconstructs a start and end point cloud, predicts per- per-part segmentation prob- abilities, and estimates articulation parameters via linear blend skinning (Kavan et al., 2007) to align the parts across the two states. Meanwhile, ArtGS (Liu et al., 2025) constructs start and end Gaus- sian sets and uses their geometric correspondence to initialise a canonical mid-state Gaussian set. It then learns part-centre locations, predicts Gaussian to- to-part assignments (Huang et al., 2024c), and optimises per-part articulation following the blend skinning (Song et al., 2024). Although effective, these two- state methods degrade substantially when the number of articulated parts is unknown (as shown in Fig. 2), and their stability is limited by the reliance on geometric correspondence between the two states. Specifically, the commonly used two-state input setting has inherent limitations: Many articulated objects cannot be well represented by only start and an end state. When the end state reveals regions absent in the start state, breaking cross-state correspondence (e.g. the interior of refrigerator or oven, as shown in Fig. 2), these methods are prone to degraded segmentation.
In this paper, we introduce Articulation in Motion ( (AIM), a new framework that reconstructs the geometry, segmentation, and kinematics of articulated objects by analysing a video of their articu- lated motion, which is simple, practical, and better aligned with the way humans learn articulation through continuous interaction (Fig. 3) rather than using isolated start and end states. Furthermore, continuous motion cues avoid cross-state correspondence failures when the end state reveals newly seen regions (see Fig. and Fig. 2). We do not assume known number of articulated parts, any prior knowledge of their joint types or motion parameters, or visibility along the entire motion. We recover the articulation parameters stably for interactive manipulation. It comprises three stages (see Fig. 3 and Fig. 5): 1) 3DGS is used to reconstruct the initial geometry and appearance. 11) We in- troduce dual-Gaussian scene representation, which contains the pre-built start-state Gaussian and a deformable 3DGS, which tracks motion on the interaction video. A built start-state Gaussian gradually pruned static base, achieving dynamic-static disentanglement based on the motion cues. III) Depending on the trajectories of only-moving Gaussians, an optimisation-fre sequen- tial Random Sample Consensus (RANSAC) clusters them into rigid parts and estimates per-part articulation parameters without any part prior. Our contributions are summarised as follows:
We present Articulation in Motion (AIM), which reconstructs part-level articulated objects, with extracted joints, based on a video that shows the objects' degrees of freedom. It opens a way to use interactive natural videos for reconstruction.
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We propose dual-Gautee Gaussiann representation disentangle the statics and dynamics, and track the moving primitives. Additionally, we introduce a static-during-motie motion detection module to handle newly revealed but static regions during interaction.
Our method achieves robust part segmentation and articulation estimation using Sequential RANSAC, without any structural prior.
Extensive experiments demonstrate that our method can independently segment stably and accu- rately moving parts of the object, reconstruct the geometry and articulation parameters of each part, and its appearance, under challenging scenarios.
2 RELATED WORKS
Articulated object reconstruction from videos or images. This work focuses on articulated ob- jects, composed of rigid parts and connected by joints. For such objects, research has focused primarily improving piecewise rigidity, identifying part-level mobility, and enabling control- lable 3D model generation. REACTO (Song et al., 2024) reconstructs the canonical object state, represented by NeRF, and learns deformation field with enhanced part rigidity from captured video. However, REACTO reconstructs articulated objects as a single unified surface, without part-level geometry, which limits physically realistic interaction. Jiang al. (2022b); Liu et al. (2023a); Deng et al. (2024); Weng et al. (2024); Liu et al. (2025) proposed to reconstruct articu- lated objects at the part level and estimate joint parameters from multi-view RGB/RGB-D obser- vations of two different articulation states, i.e. the state before interaction and the end state after interaction. PARIS (Liu et al., 2023a) learns a deformable field that applies two inverse mo- tion parameters to hypothetical intermediate-state NeRF, Similarly, REArGGS (Wu et al., 2025a) builds on 3DGS to learn a statie-to-dynamic deformation field for the intermediate state and iden- tify the dynamic part. Both are limited to objects with moving sub-part. To support multi- ple movable parts, Weng et al. (2024); Deng et al. (2024); Liu et al. (2025) directly predict part- wise segmentation probabilities for each point and learn the motion parameters per part to con- struct the cross-state correspondence fields, similar to linear blend skinning (Kavan et al., 2007)
Part mobility analysis. Part mobility analysis typically involves part segmenta- tion and articulation estimation, e.g. joint type, axis and state. For supervised learning, Jiang et al. (2022a); Qian et al. (2022); Sun et al. (2024); Wang et al. (2024) leverage advanced network archi- tectures to predict part mobility directly from a single RGB image or motion video, while Weng et al. (2021); Liu et (2022; 2023b) jointly predict part- level 3D segmentation and per-part mo- tion properties from a single point cloud, To reduce the dependence on annotated datasets, recent work has explored unsu- pervised solutions. A representative two- baste pipeline (Zhong et al., 2023; Liu et al., 2023a; Weng al., 2024; Wu et al., 2025a) predict the part segmentation probabilities for each point and the articulation param- eters per part, supervised by the point correspondence field between 3D shapes of two given input states. However, these methods depend on the input part number, lacking sufficient generalisation to real-world scenarios with unknown structural details, e.g. for objects with unknown structural details, optimisation becomes unstable. It often fails to converge to the correct number of parts (see Fig. 2). Additionally, as shown in Fig. 2, capturing multi-view observations for two distinct states can easily introduce ambiguities when cross-state correspondences are undefined for regions that appear only in the end state, instead, inspired by (Shi et al., 2021; Yan et al., 2019), which segment point clouds using trajectories from registered sequences, our framework infers part-level structure and articulation information from the motion trajectories in a single video. RSRD (Kerr et al., 2024), POD (Wu et al., 2025c) and Video2Articulation Peng al. (2025), similar video-based input,
DTA
ArtGs
DTA
Ours
ArtGS
Ours
Ours
Figure 2: Left: DTA and ArtS fail to recover from an in- correct input number of parts (4 here) and result in over- segmentations; Right: Visual results of DTA and ArGSS with closed-start and open-end states. The static part is gray and the moving part is green. In contrast, Ours requires no geometric priors and recovers accurate part-level segmentation from the continuous -losed-startt--ope--ndd interaction process.
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Multi-view Scan
3DGS modeling
Stage I: Start Static Modeling
Start GS (g*)
Br
(G*)
(
Moving GS (g*))
Time
Rasterization
Lios
SDMO
Adaplive Densify
Lg t==²)
R M.
Only
Control
Dift.
Densification & Prune
R
(g8)
Camera
R
Interaction video
Stage II: Dual-Gaussian Joint Optimisation
Figure 3: Overview of the first two stages: I) 3DGS start-state { reconstruction from multi-view RGB scan. II) A deformable 3DGS {g"h .) tracks motion video, while joint optimisation prunes moving compo- nents from { Pruned static Gaussian set 19 } encodes the static base. An SDMD module handles newly revealed but static Gaussians. Together, these yield two separated Gaussian sets ((9)) } and { .t}) for the articulation analysis (Fig. 5).
but both focus on per-part pose tracking and require segmentation masks from pre-trained mod- els. They cannot perform part segmentation autonomously, and the performance is fundamentally bounded by the pre-trained segmentation models (see Fig. A7).
Dynamic Gaussian splatting. 3DGS (Kerbl et al., 2023) provides an explicit point-based represen- tation, enabling real-time, differentiable splatting- blase rendering. As a result, there is increasing interest in extending 3DGS to dynamic scene modelling (Duisterhof et al., 2023; Luiten al., 2024; Yang et al., 2024; Wu et al., 2024). Luiten et al. (2024) tracks attribute changes of each Gaussian primitive while Yang al. (2024) learns MLP-based deformation field from time and primi- tive positions to represent scene flow. Additionally, several methods (Wu et al., 2024; Duisterhof al., 2023) introduce more efficient representations encode temporal and structural information, and employ motion clustering strategies for compactness. Tracking all Gaussians is expensive, and motion can reduce motion-basee segmentation (see Fig. A14 in Appendix). Our dual Gaussian de- tects static parts of the geometry, represented by 3DGS, while moving geometry is tracked using deformable 3DGS; clear dynamic-statee disentanglement enables stable segmentation.
3
OUR METHOD
The proposed Articulation in Motion includes three stages. Stage I: We reconstruct a 3DGS model (preliminaries e.g. 3DGS and Deformable 3DGS please see Appendix A.) of the object static state {GS}. Stage II: Given a video which parts of the object are moved, we propose a dual-Gaussian representation (Sec. 3.1) that jointly optimises { that models the static part of the object and a deformable GS {GM, t} that captures the moving parts of the object. Moreover, an additional statie-during-motee revealed during the video and adds them to the static part of the object. After obtaining {GM, 1} with the dimeddepnneet deformation, we infer the trajectories of each moving primitive and introduce the sequential RANSAC to group the moving primitives in Stage III. achieve motion- based part segmentation and articulation estimation (Sec. 3.2). The entire training is supervised solely by RGB observations: the start-state scan and the monocular video frames. Below we describe AIM in detail:
initial
detection (SDMD) module handles the newly static parts that
3.1
FOR DYNAMIC-STATIC DISENTANGLEMENT
To achieve motion-based part segmentation and articulation analysis, it is essential to accurately describe the trajectories Gaussian primitives based on the given motion video. Although D- 3DGS (Yang et al., 2024) learn time-dependent deformation fields from motion in the video, assigns a displacement to all Gaussians, including static ones. This introduces noise that may harm segmentation and articulation estimation, especially with multiple moving parts, where
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aall-Gauea Gaussian trajectories confuse the part-level structure (see Fig. A14 in Appendix). To address this issue, we propose a dual- Gaussian representation that comprises two sets of Gaussians to separately model the static base body and the moving components in the given video. The methodology is visually summarised in Fig. 3. Firstly, we train a vanilla 3DGS based on multiple views of the object in start state, namely (GS), following Eq. 8. Subsequently, given the motion video, we follow D-3DGS to initialise a moving Gaussian set {G .) and train it with Eq. 9, to make these Gaussians capture the moving parts in the video and predict the spatiotemporal deformation field. We jointly render and optimize both sets, {G'} U {G directing { to model motion cues and removing these moving elements from { to obtain the static base (g,)) ), achieving clean dynamic-static disentanglement for subsequent part mobility analysis. Newly emerging regions, initially captured by the moving Gaussian set, are identified by a parie-during-motionn detection (SDMD) module, which locates locally rigid components, estimates their local rigid motions, and reassigns them to the static set according to the predicted transformations.
Dual-Gaussian joint optimisation. Using the multi-view scan of the start state, we model the object's geometry and appearance via the original 3DGS pipeline. Following the standard train- ing settings, we obtain set of initial Gaussians G (u, St, T'a, d4, i=1 where N, denotes the total number of Gaussians, including both static and dynamic components. Thereafter, we ini- tialise a sparse point cloud and prepare to learn time-indexed deformable Gaussian set, denoted as M (my 85. sy)rr) Nm Then, we employ MLP-based deformation network Fe alongside the moving Gaussian set to capture the motion trajectory. Specifically, {GM (1js 8j, f)),, represents the geometric priors in the canomical space, while the changes (ōμ, år) in the position and rotations are learned by the deformation network as:
N'
(ōp4, = Fo(r(sg((1)))
(1)
where t is the input time index, sg represents stop-gradient operation, and y indicates the position encoding (Vaswani et al., 2017). We employ the same network architecture as D- 3DGS Yang et al. (2024). To constrain the moving Gaussian set to encode only continu- ously moving content in the video, while the start-state Gaussian set (GS) remains static- focused, we jointly optimise these two Gaussian sets. As shown in Fig. 3, during the ini- tial 10k iterations of the optimisation, we freeze all attributes of {GS} except opacity a, while {GM, and the deformation network Fo trained with the normal adaptive density control. In this process, we progressively prune the moving elements of (G*} as their opacity decreases over time obtain the static Gaussian set, namely ). while {g 1) fits the moving components in the video (see Fig. A4). In the following itera- tions, we unfreeze the static Gaussian set, and jointly perform densification and pruming on both sets. Through the differen- tiable rendering on the combination of {G25 and (GM,t='' t=t"),
we supervise the total training process with the video frame at the corresponding timestep t t't'. Since previously unseen ar- eas in the start state, e.g. the interior structures of refrigerators, washing machines, and cabinets, will be captured by the mov- ing Gaussian set, an SDMD detection module is introduced to audit the moving Gaussian set and prevent static leakage.
Figure 4: Renderings of the start (left), and end (middle) states. Without SDMD detection, some newly revealed static parts are wrongly associated with the moving Gaussian set (right).
Static-during
detection. During the first 10k itera- tions, we freeze all position-related attributes of (gS), allow- ing (gM, .) to thoroughly learn the moving parts while also adapting to newly revealed static content (Fig. 4). Although such content becomes stationary once revealed, it is often already occupied by moving Gaussians, which hampers the static set from learn- ing this geometry. To address this, we introduce a static-during-man motion detection (SDMD) scheme. During joint densification and pruning, we perform trajectory inference for the moving Gaussians (GM, ,1] every 2,000 iterations at E (0, 0.5,, 1). We then apply sequential RANSAC with the Kab- sch algorithm (Magri & Fusiello, 2016) and a fixed inlier threshold of 0.05 to the resulting trajectory sequence to extract locally rigid motion patterns (details in Sec. 3.2). Groups whose motion magni- tude falls below the preset threshold (defined in Sec. 3.2) are identified as static, and their Gaussian primitives are reassigned from as 1} to {G.)). Compared with a simple motion-distance filter, our SDMD avoids misassignment near the joint axis (where motion trajectories are near zero).
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1 0): Statio:
Sequential RANSAC
(e - 1): Prismatic
Minimal sel sampling
Support sof
[9.00.99..02 0.35).
1 155 1
Kebech salver
Mation hypothesle
(s] oj: Static;
i = Prismatic joint (d - 1-0002.00000000 e 0.11;
( - 2]: Revolute joint. (4 [1.00 0.00,0.00 1.54).
(9".--0)
(5 0.5)
(g"Me-11
Figure 5: Stage III: Motion-based part segmentation and articulation analysis. As the clean ( (} pro- vides time-varying trajectories, Sequential RANSAC groups trajectories into rigid parts (multi-par parlti-prttte supported) without priors optimisation, and directly outputs per-part articulation parameters. The green (top) and purple (bottom) points are our predicted moving Gaussians.
3.2 MOTION-BASE BASED PART MOBILITY ANALYSIS
Existing methods typically assume known number of object parts to infer part mobility. In practice, they input the ground-truth part count to establish cross-state correspondences, which then serve as priors for clustering-be based part segmentation. In contrast, our core idea is to understand the part mobility based on the motion cues in the interaction videos. Once the dual-Gaussian representation decouples the statie base and dynamic components, recover accurate, time-parame tine-parameterised tra- jectories for the moving Gaussian primitives over arbitrary time horizons with selectively sampled timesteps. This enables motion- motion-based part segmentation by clustering moving Gaussians with the same motion patterns into rigid parts. Therefore, as shown in Fig. 5, based on the clean inferred mo- tion trajectories, introduce simple, robust, purely analysis-based sequential RANSAC (Magri & Fusiello, 2016) to achieve the part segmentation and estimate articulation parameters. Built on sequential RANSAC with Kabsch solver (Magri Fusiello, 2016). AIM automatically recovers the part number and its kinematic parameters, i.e. joint type, axis direction, and motion magnitude.
Part segmentation. From the time- timedeepndent moving Gaussian set (gM, M, t} with learned deforma- tion field Fo, we can infer the centers positions of moving Guassian at timestep Pe = (uA)) i=1- Furthermore, we can easily obtain the one-to-one corresponding trajectory between timestep Ł and t', recorded as {Pt-ee) To extract rigid parts, we employ a sequential RANSAC with a Kabsch solver (Kabsch, 1976). Unlike conventional start-end matching methods (t = v.s. t= 1) or pre- trained eegmentation-drivee approaches, which require structural priors from manual input or pre- trained models, our method aggregates evidence across trajectories spanning multiple time windows to capture diverse motions and improve robustness. For one trajectory of time window (Pa-sb). ). the optimal rigid transform is estimated by Kabsch solver,
RR bp) arg min
Σ
| (R +t)ll
(2)
R.t
#E.min
where Smi is a randomly sampled minimal set. The residual error of each moving Gaussian (g.M) defined as:
err; = 11 RR =b)
Gaussian G.M accepted as an inlier if err; Cin- After Nampling iterations, the largest consen- set is selected as the support set. The motion parameters (R, () are subsequently re-estimated from all inliers using the Kabsch solver to obtain one motion hypothesis. The identified inliers are removed, and the process is repeated on the remaining Gaussians, The procedure terminates when no valid support set is found, when the maximum itcration budget Nmax iter is reached, or when the largest inlier set is small. This sequential RANSAC yields collection of support sets, each cor- responding to one rigid part. In this work, to balance accuracy and efficiency, we simultaneously employ the trajectories of time windows, Po-10.5 and Po..1. and compute the mean residual error across them to determine inliers, as:
eIr, = Al - (Ri-oss + to-
+ [ 1R + 1ll
M
(4)

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Published as a conference paper at ICLR 2026
Per-part articulation parameters estimation. With the selected support sets, employ the Kab- sch algorithm to estimate the rigid transformation {(Rs, {+)]= K is the number of support sets, ie. the number of parts. Furthermore, extract the underlying articulation parameters to characterise the motion. We follow existing works (Liu et al., 2023a; 2025; Weng et al,, 2024) and focus on the following joint articulation parameters: the joint axis position p. joint axis direction u, translation distance 4 rotation angle O and joint type (prismatic or revolute). According to Rodrigues' rotation formula (Rodrigues, 1840), the rotation matrix R& can be expressed as:
R cos 0 + sin Oμ [ux]x + (1 cos @ @ uz),
(5)
where direction uy is a unit vector, I is the identity matrix, Flx is the cross product and @ is the outer product. From Eq. 5, we can obtain the uy and Ø₁ respectively, as:
[2, 1
R (1,11] 2)
(r (r((Rr)) -1)
U1 2 sin θ
R 0.2
R& 2.0 0
O₂ = arccos
(6)
R (1.0 R. (0,
2
For the translation distance & and the position p (start point) of joint axis, we can calculate them based on the rotation matrix R₃ and translation component ty as:
Us: tk |
Ps (Ra- 1)_ (
(x).
For the joint type, inspired by (Shi et al., 2021; Liu et al., 2025), we classify it as revolute when the rotation degree O exceeds threshold Gyemal 10 (about 0.17 radians) and prismatic by contrast. Based on this, in static-during-mote -otiinn detection, a region in a moving Gaussian set is considered static if the rotation angle θ S 0.1 radians and translation magnitude & S 0.05 units.
4 EXPERIMENT
4.1 DATASET, METRICS, AND IMPLEMENTATION
Dataset. We select various articulated objects from PoriNet-Mobillty (Mo et al., 2019). We rendered video of articulated motion using camera trajectory around the object. To verify the effectiveness of our prior-free part segmentation, we render objects with multiple parts moving simultancously in a variety of motions. For two-state baselines, we render 100 random upper-hemisphere views of the start and end states, respectively. Our benchmarks include challenging 8 two-part objects, 2 three-part objects, and multi-part objects. Most interior parts are gradually revealed over time, reflecting real-world applications. (Additional examples are provided in the Appendix C.)
Metrics. We conduct comparisons from three perspectives: 1) Part segmentation performance. To consider the points inside the surface, we voxelize the meshes and evaluate part-level segmentation with 3D Intersection-over- Uver-Unionnn Union (IoU) (Nie et al., 2021); 2) Reconstruction quality. Following prior works, compute the bi-directional Chamfer Distance (mm) to measure the reconstruction quality; 3) Articulation estimation accuracy. Following prior works, We report the Axis Ang Err (²), Axis Pos Err (0.Im), and Part Motion ( m). (More details please refer to Appendix B).
Implementation details. We evaluate against recent state-of-th of-thh--ttae art methods, PARIS DTA, and ArtGS, that use RGB- -CB-th inputs. Our approach requires an RGB video with 200 frames for two- and three-part objects and 500 frames for more complex objects. We present a baseline, Ours-h, which replaces the proposed dual-Gaassian representation with single deformable 3D Gaussian shape (3DGS). Following ArGS,, we use a truncated signed distance function (TSDF) volume for mesh reconstruction, render depth maps, and marching cubes (Huang al,, 2024a).
4.2 QUALITATIVE AND QUANTITATIVE EVALUATION
Part segmentation. As shown in Tab. 1, our method attains the best 3D IoU on almost all objects in both two-part and multi-part Palti--prttta settings. On complex objects, the gains are large. e.g. on Storage (6 moving parts). our mean dynamic-par loU exceeds the prior SoTA by +27. 12%.11% Standard devi- ations are consistently lower, indicating greater stability than two-state inference (e.g. DTA/ArGS on Fridge, Oven). Compared with Ours-b, the Gual-Gaussian dynamic-stalee static separation further im- proves accuracy by suppressing static interference.
PARRS is augmented with depth supervision
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Table 1: Part segmentation performance articulated objects. (a) Two-part; (b) Three-part: (c) Complex objects. For two-part objects, 3D (o) is reported as meanisst over 10 trials, while for three-part and complex objects, we report mean 3D (%) over 10 trials. Dang represents the average over all movable parts. F denotes failure. Higher is better, with the best highlighted in bold.; Gray means two-state methods.
(a) Two-purt objects
ID IoU (96)
Method
Two-part objects
Fridge
Oven
Scissoe
USR
Washer
Rlade
Storage
PARIS
85.2 25.23
92.19
P
87.84_2.00
86. M..7%
Static
DTA
86.27
91.11as0
66.01=3.98
86. 1...0
88, 1104
83 23.22
88. 57.
AmoS
17.69
94.55-377
44.77
94.51
90.91 =5.sT
88 28.68
Ours-b
23.70
94.46-281
81,27=2.84
83 21.41
+4.07
Ours
88.01 88.01
97.72.002
92.42.1.06
92.93
96 96.98g2.2
84 84.33311.6
91.41 22.78
PARIS
55.97 25.97
45. 25..2
a7.81
F
a2.75
14,42=1008
42 22.88
Dynamic Part
52.06x22.22
41.23-222.0
53.58
5 5.97
27.92-11.42
39 =22.24
AnGS
25.04
M5.62430.43 F
30
Durs-b
$7.09
72.50.20.0 ds
77.74
55.36.14..2
35, 35,54 41.7662.34
Ours
:5.19
89.61
92.21
91.95.1.18
68.52.13.80
43,92
69.01 27.42
PARIS
70.60212.40
68 2022..
75.7.20.1
F
65.64411434
61. 63.13
64,88 =20.0s
DTTA
16
66. 17.12.75
59.79
23.05
$5.57.2.20
63.94 24
Meon
AnGS
73. 23.24
80. 12:14.3
79.87=6.19
87.73
27.73
65.06 F
50 59.03
76.9% es
Ours-b
70. 70.40
83.48-5.0
79.51
55, 25.26.114.31
62 62.68-2.47
62 22.066118.7
Ours
110.4
93.66-2006
92.31=1.12
92.44.3.24
82.75
64.12-1.9
80.21 en.37
(b) Three-part objects
(c) Complex objects
3D IoU
PARIS-m
DTA
AnGS
Ours
3D IoU (S)
DTA
AAGGS
Ours
89.77
88.53
92.23
94,20
5
87.95
93.32
97.01
Storage
Do
55.32
51.78
94.95
Storage.rsw
Dang Mean
26.38
52.23
79.34
17024
D
60.72
35.18
58.14
81.87
8
89.55
81.05
94,80
95.42
89.81
90.44
91.75
Fridge
Do
25.73
55,28
88.53
89.95
Tables124
Dany
37.29
38.07
43.92
11304
D1
45.31
61.19
80.48
91.42
Meun
47,80
48.55
53.44
Table 2: Mesh reconstruction comparison. (a) Two-part objects; (b) Three-part objects: (c) Complex objects. For two-part objects, we report CD distance (mm) as mean across 10 trials. For three-part and complex objects, we only report the mean value, while we report average CD for movable parts. Lower (L) is better.
(a) Two-part objects
Metric
Method
Two part objects
Fridge
Oven
Scissor
USB
Washer
Blade
Storage
DTA
3.19_0s0
9.10
9.412100 00
2.04ga.12
5.03ga.44
0.33
4,94.0
CD-$
AnGS
1.58
2.39
0,80-0.0
11.01.0.45
6.63a0.27 F
1,23
7.5020..5
Ouns-b
4.73-0sa
10.08:1.45
2.22-1.0:
34.61
1 1.90
7,27
Ours
3.4
10.
10.36an.73
0.14_0.0
1.54
20.5
1,76
2.09
4.08
27.61
1.90
481 06.00....4
19. 19.30
67.33
CD-m
ArGS
43.51 1o
14.34
53.71 Los se
50.00 19.77
29.79 155.65 F
473.72
6.92 10.51
Ouns-b
18.27
5.17
73, 73.13
19.46.5.0
110 110.66000.00
88.85
1.
1.
0.27_010
0.89 10
21.03
2.36
18.95
(b) Three- part objects
(c) Complex objects
PARIS m
DTA
AAGSS
Ours
+
DTA
AAGGS
Ours
CD s
8.05
3,20
3:58
10.37
Straage 47024
CD D,
CD-D,
F
287.70
275.87
253.69
1.21
27.35
0.81
Storage
CD-Magv
CD-s
200.15
2.08
71.17
2,96
8.36
2.63
Fridge
CD-D,
CD4
298.29
691
29.95
4.00
12.83
2.93
8.36
3,85
Table
CD-s
2.56
3.65
3.08
139.85
323.06
12.17
CD-Mavo
152.93
51.40
4.99
Mesh reconstruction. We report mesh-recoonsrruction results in Tab. 2. Despite using RGB-only inputs, our CD on static parts is competitive with PARIS and AnGS, and our errors on dynamic parts are much lower e.g. Storagea764s: 8.36 vs. 71.17 (ArtGS); Table: 4.99 vs. 51.40.
Articulation estimation. Our framework attains highly accurate joint predictions (see Tab. 3). For two-part objects, our anis- errors are consistently minimal (e.g. Oven: 0.27 vs. 5.39 of DTA). For complex objects, the improvements are striking: on Storage, we reduce axis-angle error from 12.78 (ArtGS) to 0.58 and part motion errors to nearly zero (0.02 for prismatic joints). This evidences the advantage of dual-Gaussian representation and motion-based fitting.
Analysis of two-state limitations. From qualitative and quantitative results, it can be observed that two-state methods strongly rely on geometric correspondence between the start and end states. Once this correspondence is broken, such as the open-end state reveals interior regions absent from the close-start, these methods are forced to match dissimilar geometry, leading to degraded part segmen- tation and unstable articulation estimation. Most notably, two-part objects such the fridge and oven in Fig. 6, the newly revealed interior structures cause the canonical mid-state Gaussian initial- isation in ArtGS to fail. This disturbed canonical initialisation propagates to articulation estimation
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Published as a conference paper at ICLR 2026
Table 3: Quantitative evaluation of articulation estimation. (a) Two-part: (b) Three-pan part: (c) Complex objects. For complex objects, we report the average all moving parts. Due to the different magnitudes of part motion for revolute and prismatic joints, we report both of them. F' denotes failure. W'' denotes that more than 6 out of 10 trials result in an incorrect joint-type prediction. indicates prismatic joints w/o rotation axis.
(a) Two-part objects
Two-part objects
Metric
Method
Fridge
Oven
Scissoe
USB
Washer
Blade
Storage
Axis
DTA
1.86
5.30
g101
0.22
17.34_o5.a
1.65.0as
8.18
AnGS
WT
WT
2 2.39
23.86usss.00
WT
1.31
g.00.0.01
Ouns-b
6,76g3.4
3,36
5. 2.12
6.65
F
0.252220
1.72
Ours
2.70
0.27
g.00
0.59
163
0.18
1.52
Axis
DTA
1.75
4,98
4
0.01
26.50
WT
1,78
6.01
WT F
Ouns-b
1.22
1.24 s0
0.86
0.84-0000
Ours
1.86
1,13
0.75
1.45
1.12
PARIS
114.60
111.50
122.05 1-2.50
11
10os
0.10
Part Motion
DTA
171.77
142.10 142.10
150.50
0.32, 0.15
76.43
2.02
0.07
AnGS
WT
WT
99.00 99.09
120.05
WT
2.
0.0020:00
Ours-b
10.6225.0
2.64
5:20g0.00
16.94-.44
0.25
1.7222.57
Ours
6.76 4.
5.12g0
6.65 4.20
6.90
0.01 20.0
0.02 200
(b) Three-part objects
(c) Complex objects
Mithob
2/
Pat Motion
D,
Per
Angwoy
Posrway
Morion
MationG
DIA
39.03
78.39
16.36
0.39
0.81
Storage
AnGS
12.78
3.36
61.93
0.38
Storag
20.33
3.33
1:35
4004
Ous
0.58
1.31
10.56
0,82
Table 2710
AmGS
2.43
0:43
Rille
DIA
13,13
399.0
175.90
9.88
6/21 066
36.36
Ours
1.19
0.11
1.10
0.81
0.68
Start
End
Start
End
End
GT
GT
DTA
DTA
ArtGS
ArtGS
ArtGS
Ours
Ours
Ours
Figure 6: Qualitative results part segmentation and articulation estimation on two two-part objects (fridge, left; oven, middle) and a complex multi-part object (Storage-47643 right). For complex object, each predicted joint axis is visualised using the same colour as its corresponding part segmentation mask. Across the two-part objects, DTA and ArtSS often struggle with mis-segmentation and inaccurate joint-axis/type predictions. In contrast, our method produces clean part segmentation and consistent joint-axis estimation across all objects.
and results in joint-type errors, often predicting prismatic joint instead of the correct revolute joint. By contrast, DTA is relatively more stable due to its symmetric optimisation of both the start-to-end and end-to-start transformations, yet it still misclassifies newly revealed structures, e.g. predicting the oven interior as dynamic or assigning large portions of the moving fridge door to the static part.
Summary. Overall, compared to two-state motion inference, our method demonstrates stronger and more stable performance under a challenging doss-start/open-end setting. In the two- and three-part datasets, Articulation in Motion achieves the best results on the vast majority of objects. On complex objects, our approach shows clear advantage, significantly surpassing prior methods (more visualisations in Appendix D and Fig. A4). For mesh reconstruction, while our approach still has limitations in overall mesh fidelity compared with NeRF-based methods, the strength of our part mobility analysis enables consistently superior reconstruction of dynamic parts over prior hate-oo-the-aa methods.

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Published as a conference paper at ICLR 2026
Table 4: Ablation studies complex objects. We report the average metrics of dynamic parts. And calculate the mean across three trials.
Angang
Postoug
Motion
Motion
CD-Maug
3D JoU
AnGS
12.78
3.34
81.93
0.18
71.17
52.25
ss/o start state scan
37.60
Storage 47648
w/o SDMD
2.62
1.41
14.72
0.06
91.52
77 45
dual--GS
2.95
1.27
15.36
0.08
17.43
66
wy/o RANSAC
78.54
Full
0.58
1.31
10:56
0.02
8.36
79.34
AnGS
33.19
2,42
82.29
0.43
51.40
38,07
o state scan
Tuble
w SDMD
11.62
1.49
23.74
0.21
18.05
37.10
31209
oo dual-GGS
1.49
0.94
1.47
0.57
6.26
41.74
wy/o RANSAC
40.65
Full
1.19
0.81
1.10
0.01
4.99
43.92
4.3 ABLATION STUDY
Effectiveness of start state scan. As shown in Tab. 4, while directly training the dual-Gaussian representation with set of random Gaussians as the static, 3D IoU ang drops from 79.34% to 37.60%. On Table, the pipeline cannot detect the moving Gaussians. These results indicate that the start state can anchor the shape and appearance of objects and is essential for capturing motion cues.
Effectiveness of the static-uuring-motion detection (SDMD). Disabling SDMD consistently harms dynamic geometry and motion recovery. In particular, the sharp increase in CD-m aeg in both storage and table shows that the filtering of static noise during motion capture is critical to part mobility analysis. (More visual results please see Fig. A13 in Appendix.)
Effectiveness of the dual-GS representation. We assess the dual-Gaussian representation by replacing it with the original deformable-3DCC Across metrics, articulation accuracy and part- segmentation IoU degrade markedly without our dual-Gaussian representation. This confirms that explicit dynamic-stoe static disentanglement is cornerstone for prior-free part mobility analysis. (More visual results please see Fig. A14 in Appendix.)
Effectiveness of sequential RANSAC. We first attempted prior-free density clustering with DB- SCAN (Ester al., 1996), which failed to produce valid partitions across objects. We then applied K-means (Pham et al., 2004) with the provided part count, yielding reasonable groups but infe- rior articulation and segmentation. In contrast, sequential RANSAC delivers the best prior- free performance while remaining robust to motion variability. Given our accurate motion trajectories, K K-means outperforms ArGSS, underscoring the quality of our motion cue extraction.
5 CONCLUSION AND LIMITATION
Conclusion. In this work, we presented a compact pipeline, Articulation in Motion (AIM), to achieve a prior-free and stable part-mobility analysis. Compared to previous works based on two- state shape correspondence, our method utilises more natural motion and human-object interaction videos input. It introduced video. With the dual-Gaussian dynamic-sttre stanic separation, we obtained clean motion trajectories; coupled with the robustness of sequential RANSAC, this enables prior-free part segmentation and articulation on unseen objects. Comprehensive experimental evaluations validated the effectiveness and stability of the proposed AIM in diverse challenging scenarios.
Gaul-Gaassian scene representation to analyse motion cues in the
Limitations and future work. Our AIM generates higher quality segmentation of articulated ob- jects and recovery of their degrees of freedom (DoF) compared to prior work. We do not require the use of depth sensing, as done by most prior art techniques; however, we do utilise a video that captures the DoFs of the object's parts. Such a video contains more information than a three- dimensional reconstruction of the object at the start and end states. Yet, in many common cases, this easier to capture compared the former type of data: some objects contain many DoFs, and some are dependent on each other, making it hard to capture all of them with only two static states. The capture of a video a more natural way for person to introduce an object, where they can expose each DoF at time. Capturing the whole geometry of internal parts, such as drawers, blade of a knife, or the blades of a pair of scissors, requires the full disassembly of the articulated object. The generated geometry is limited to the visible geometry and, as such, may be limited in its application for developing interactive models. Future work may utilise a data-driven approach to complete such parts of the whole geometry, given dataset of these parts.
extended
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This project is supported by the Amazon Research Award "PCo3D: Physically Plausible Control- lable 3D Generative Models". The project is also supported by an unrestricted charitable donation from Meta (Aria Project Partnership) the University of Birmingham. The computations in this research were supported by the Baskerville Tier 2 HPC service. Baskerville was funded by the EP- SRC and UKRI through the World Class Labs scheme (EP) TO22221 (1) and the Digital Research Infrastructure programme (EP) W032244 1) and is operated by Advanced Research Computing the University of Birmingham.
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Junyi Zhang, Charles Hermmann Junhwa Hur, Varun Jampani, Trevor Darrell, Forrester Cole, De- qing Sun, and Ming-Hsuan Yang. Monst3r: A simple approach for estimating geometry in the presence of motion. arXiv preprint arXiv:2410.0388 2024.
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Zrporz. Autoscg-sam2. 2024. URL https: /ttph/gttttttt///// com/ www.///ooo//tueettcooooooooaooee /AtuttutttegsAAae Au- tomated image segmentation tool based on Segment Anything Model (SAM).
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APPENDIX
This is the appendix for the main paper. Here is a general roadmap describing the contents of each part of this document supporting the main paper:
In Section A, we first review the core 3DGS (Kerbl al., 2023) and Deformable 3DGS (Yang et al., 2024) formulations to unify notation and provide the foundational baseline for our dual- Gaussian representation in the main paper.
Section B, we detail three evaluation aspects: part segmentation, reconstruction quality, and articulation estimation accuracy.
In Section C, provide additional details to rendered datasets, which include the detailed splits/statisties and representative multi-view examples.
Section D provides more experimental results, including more visual comparisons among DTA (Weng et al., 2024), ArtGS (Liu et al., 2025) and ours (Section D.1), more quantitative and qualitative results compared with pre-trained segmentation driven method ArtcuuAAtiiulatioon (Peng et al., 2025) (Section D.2), more results compared with DTA and ArtGS under the open- ppertstart and open-end setting (Section D.3). Sec. D.4 details our real-world data ac- quisition and preprocessing pipeline, and reports visual results.
Section provides additional qualitative results for the ablation study.
A PRELIMINARY
3D Gausssian splatting 3DGS represents the scene as an explicit point-based 3D structure, en- abling orders of magnitude faster reconstruction and rendering. In this work, we build on 3DGS to reconstruct the articulated objects with the part-level structures. In details, each 3D Gaussian G; is defined by a center position 14; E R3, opacity a; IR, covariance matrix Σ; parameterized by 3D scale 8; E R and rotation ri E R4, as well as spherical harmonics (SH) coefficients h; for view dew-deeendent color modeling (Kerbl et al., 2023). Given image captures the scene, we optimise collection of Gaussians {9} {G. J via blending- based differentiable rendering:
N I= Σ TT, a2 = 0,exp(-
1 1--1"""''----0 (u- (4 T. = (1 a?D), (8)
where pi and D.D denote the 2D projections of the 3D center μ1; and covariance matrix E,, respec- tively; u represents the pixel coordinate; and c, is the colour of G,, determined by the SH coefficients h; and the view direction. T; represents the transmittance from the start of rendering to G..
Deformable 3D Gaussian splatting. To model the varying changes of geometry and appear- ance in a dynamic scene, Deformable-3DGS. (Yang et al., 2024) introduces learnable deformation field model temporal transformations in the centre positions 14, rotations r, and scales s of 3D Gaussians. This deformation field is parameterised by multi-layer perceptron (MLP), which pre- diets offsets based on time and the canonical Gaussian G.. Specifically, at time step I, the deformed Gaussian Ga is defined as:
Ga Ti, o,, h4) Go(u + 84, 3, + 8s, ; 8r, or, o., h4),
where the offsets 8p, ds, dr are given by (t() (24)). with Fe representing the deformation field and y denoting the positional encoding function. With differentiable rendering, both the Gaussian parameters and the deformation network parameters are jointly optimised. Here, we only consider the vime-ngrying transformation of position 5n and rotation 5r.
B METRICS AND EVALUATION DETAILS
We evaluate from three perspectives consistent with the main text: (1) part segmentation via 3D IoU on voxelized meshes, (2) reconstruction quality via bi-directional Chamfer Distance (mm), and (3) articulation estimation accuracy via axis and motion errors.
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1. PART SEGMENTATION PERFORMANCE (3D IoU)
For each predicted part and its ground-truth (GT) counterpart, we voxelize both meshes onto a shared binary occupancy grid (identical bounds and voxel size). Let D and 19 be the sets of occupied voxels. The part-level segmentation score is
D n V loU
(10)
D V
as in prior work (Nie et al., 2021).
2. RECONSTRUCTION QUALITY (CHAMFER DISTANCE, MM)
We uniformly sample points on the surfaces of the predicted and GT meshes and compute the sym- metric (bi-directional) Chamfer Distance in millimetres. For point sets X and Y.
min ulla + CD(X,Y) = Σ
Σ yEY
minlly
y
1||2
(11)
X
yEY ØEX
Y
zEX
We report CD for the whole object (CD-w), the static parts (CD-s), and the movable parts (CD-m).
3. ARTICULATION ESTIMATION ACCURACY
For each dynamic joint, we report three metrics:
Axis Ang Err (*), Let à", 2 E IR3 be the unit axis directions (predicted and GT). The angular error (orientation-invariant) is
0 min arccos(à"--r ag), 180 arccos(à"-Oe -a))
(12)
Axis Pos Err (0.1m). Let the axes be lines (s) O + s â" and (t) 0 +t The shortest distance d between two 3D lines is used, and we report it in units of 0.1m by
d ( ( (o" oo)l
AxisPosErr 10 d,
(for non-parallel axes),
(13)
ár x as
and [1 - 0²) x @ for (nearly) parallel axes. This metric reported for revolute joints only.
Part Motion ( or m). Between the start state t=0 and end state t=1, measure the state emror:
(i) revolute: geodesic angle on SO(3) between the predicted and GT relative rotations AR (R() and AR (R()
arccos
tr(AA"
180"
(14)
2
(ii) prismatic: Euclidean difference between relative translations APP = 14 - and A/9 = 14 - 19
8 = APP A/9 (meters).
(15)
C DATASETS
C.I SYNTHETIC DATASET
We build a dataset to evaluate motion segmentation under increasing difficulty. Detailed splits and statistics are reported in Table A1, Table A2, and Table A3. Each scene contains start (static) state, a continuous motion segment, and an end (static) state. We also provide visual examples from the dataset. Fig. Al shows two-object the motion segment. Fig. A2 shows a three-object scene with the same counts: 100/200. Fig. A3 is complex object scene with longer motion segment: 100/500.
with 100 multi-vies view images for the start state, 200 for
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Start State
Motion
End State
Figure A1: Examples from the Two objects dataset
Start State
Motion
End State
Figure A2: Examples from the Three objects dataset
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Table A1: Motion type and motion records across ten scenes the Two-part objects dataset
Blade 103706
Fridge 10905
Oven 101917
Scissor
Stapler 103111
Storage 45135
USB 100109
Washer
Scene
11100
103776
Motion type
Translate
Rotate
Rotate
Rotate
Rotate
Translate
Rotate
Rotate
Motion
110
90"
45
600
Table A2: Motion types ranges extracted from the two scenes of the Three-part objects dataset.
Scene
Motion Type
Range
Motion Type
Range
Fridge
Rotate
-180
Rotate
0º + -900
11304
Storage
47024
0
Rotate
0 + 90
Translate
0 - 01.
Table A3: Motion types and ranges extracted from two additional scenes of Complex objects dataset.
Scene
ID
Motion Type
Range
Part ID
Motion Type
Range
0
Rotate
- 120
Rotate
0 - -1200
Storage
2
Rotate
0 - -600
Rotate
0 -+ 60
47648
4
Translate
- 0.1
Translate
0 -+ 0.16
Table
Translate
0.38
Translate Rotate
0.35 -
31249
Rotate
-900
- 90
Start State
Motion
End State
Figure A3; Examples the Complex objects dataset
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Table A4: Comparison results with Video2Articulationn (Peng et al., 2025). We report four metrics, i.e. Axis Ang ()), Axis Pose (0.lm). CD-m (mm) and CD-s (mm). Especially, for two-part objects, we report metries as ald across 10 trials. For three-part object, we report the mean value on different moving parts. Fail represents that Video2Articulatioo fails to detect the correct part segmentation masks, and (%) shows the failure rate. Bold means better performance.
Two-part
Three-part
Method
Fridge
Storage
USB
Washer
Fridge (JoiniU)
Fridge (Jaint1)
Axis
VideoZArtieulationn
3,8020000
6.33
1.8920.00
Fuil (100%)
2.17
1.35
Ang (*)
2.70
1.52
2.59
1.63
Axis
Video2Articlation
0.95
0.12
Fail (100%)
0.71
1.92
Pose (0.lm)
Ours
0.86
1.45
1.12
0.68
3.58
CD-m
Viteo2Articultion
8.06
:12.92
24.68
Fail (100%)
41.38
(mm)
Ours
2.21 20
18.95
0.89
21.03
2.12
3.85
CD-s
Video2Articulation
7.21 20.12
8.6620.47
101.42.0.77
Fail (100%)
44.45
44.45
(mm)
Ours
3,45.0.09
+..01
1.54
9.25
8.16
8.16
D ADDITIONAL RESULTS
D.1 ADDITIONAL, RESULTS ON OUR DATASET
As shown in Fig. A4, we provide more rendering results of our A1M. Additionally, we provide more visual comparisons among DTA, ArtGS, and ours. As shown in Fig. A5 and Fig. A6, compare the rendering quality with ArtGS, and compare the part segmentation performance with DTA and ArtGS. Especially, all the results with the start state and generated with the estimated motion parameters. From the results, we can see that our method achieves more stable and accurate part mobility analysis. Furthermore, the point clouds in Fig. A6 are presented to show the geometry recovery.
1021
(o1911
Washer103776
Foldehair
Storage47024
Storage47648
Figure A4: Rendering of dual-Gaussian representation (left: static result (R$) }. right: moving result
{R M, }). All objects (category..mame) are from PartNet- dataset (Mo et al., 2019).
D.2 ADDITIONAL COMPARISON WITH PRE-TRAINED SEGMENTATION DRIVEN METHOD
As introduced in Sec. 2, our work primarily focuses on self-contained methods, namely approaches that can independently perform part-level mobility analysis without relying on any externally pre- trained segmentation models or segmentation-mask pools. Therefore, we select PARIS, DTA, and ArtGS as our baselines. Furthermore, motivated by the inherent limitations shared by these two- state-based methods, we propose AIM, which leverages motion cues from common close-to-open interaction videos to achieve part prior-free mobility analysis without any structural priors.
To further demonstrate the effectiveness of our method beyond the self-contained setting, we additionally report both quantitative and qualitative comparisons against the recent pre-trained segmentation-ddrivnn approach, Video2Articulation Peng et al. (2025). Since VideoZArticulation requires preprocessing through Monst3r Zhang et al. (2024) and AutoSeg-SAN Zrporz (2024), we directly use the overlapping subset of objects provided in their released dataset. Specifically, we evaluate four two-part objects (Fridge- (Fridge-10905, 10905, Storage-45135.
USB-100109 Washer- 103776) and one three-part object (Fridge- 1304) and reproduce the official codes using the official settings. These results could be found in Tab. A4 and Fig. A7.
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GT
ArtGS
Ours
GT
AAAD
ArtGS
0090
AAD
000
AADD
Ours
DOLD
Figure A5: Rendering results based articulation estimation parameters for the Top (fridge): From the visualisation, can be observed that the newly seen interior content severely influences the performance of ArtGS's ss articulation estimation, causing the door located inside the body. Bottom (oven): Similarly, during opening the oven, the newly seen content could not be well aligned between the two states, leading wrong axis estimation and joint type estimation. The handle moves into the
of two-part objects:
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(a) GT
(b) DTA
(c) ArGS
(d)Ours
Figure A6: Qualitative comparison hetween DTA, ArGSS and ours, w.r.t. GT.
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100NOE
static
Pre-trained
static
segmentation results
Video2Articulation
Figure A7: Visual results of Video2Articulatton Peng al. (2025) (Left) and ours (Right) (Notably, the expensive pre- processing process of Video2AAticclattinn, we directly use their released dataset to reproduce Video2Articulattio, there is some colour difference). Left: From the visualisations, we observe that the Video2Articulatioon method fails to predict correct moving parts when the underlying pre-trained segmentation models cannot provide reliable masks. Typical failure cases include mis- segmenting the drawer and cabinet, or losing track the inside of refrigerator door once it opens. Right: In contrast, our approach performs part mobility analysis by directly exploiting motion cues from the interac- tion video. After achieving clean static dynamic separation, we apply multi model fitting based on the inferred motion patterns, which leads consistently more robust and accurate results.
Table A5: Quantitative evaluation of articulation estimation under the open-start and open-end condi- tions. (a) Two-part; (b) Three-part; (c) Complex objects. For complex objects, report the average of all moving parts. Due the different magnitudes of part motion for revolute and prismatic joints, report both them. indicates prismatic joints w/o rotation axis.
(a) Two-part objects
Metric
Method
Two-part objocts
Fridge
Oven
Scissor
USB
Washer
Blade
Storage
Axis
DTA
0.08 20.0s
0.10
0.06
0.83
2.05
0.41
0. 04
Ang
AnGS
0.00
0.01 20.00
0.07
0.01 =0.00
0.03+0.02
0.02
0.0020.co
Ours
0.19
0.0620.00
0.20g0.06
0,05
g.03
0.05
Axis
DTA
0.01 20.00
2.06
0.01
2.03
4.05
Pos
AnGSS
0.00.0.00
0.01 20.00
0.00
0.0020.00
0.004:.00
Ours
0.04
0.05
0.00
0.02
0.0240
Part
DTA
0.12
0.202..co
0.04
0 0.6620.58
12.13-11107
0.00
0.00
Motion
AnGS
0.01 $0.00
0.04
0.05
0.0020.00
0.03
0.00
0.00
Ours
0.79
0.46
0.58
1.19
0.10
0.00
0.00
(b) Three-part objects
(c) Complex objects
Do
Dj
Methods
Axis
Axis Pos
Part Motion
Axis
Axis
Part Motion
AnddS
Amp.s
10.18
Pescu,
Mution,,,
Miriong,,
0.13
Ang
Ang
Pos
0.0
DTA
0.09
0.02
0.07
0.32
0.00
Tabic
AndS
0.02
0.00
0.00
Storage 47254
AmtSS
0.04
0.00
0.00
Ouzs
0.36
0.00
0.7
0.00
Ours
0.18
0.05
0.22
0.00
DTA
0.26
0.01
0.19
0.18
0.01
0.26
Fridge
Amts
0.00
0.00
0.04
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Table A6: Mesh reconstruction comparison under the open-start and open-end condition. (a) Two-part objects; (b) Three-part objects; (c) Complex objects. For two-part objects, we report CD distance (mm) as atan across 5 trials. For three-part and complex objects, we only report the mean value, while we report the average CD for movable parts. Lower (L) is better.
(a) Two-par objects
Metric
Method
Two-part part objects
Fridge
Oven
Scissor
USB
Washer
Blade
Storage
DIA
p.62
4.59
0,71 -0.51
3.19
1.69
2.80
2.78
CD-S
ArGS
0.50
0.82-0.20
2.5820.01
2.96
2000
2..5
Ours
0.53
4.59
0.57_0.00
2.95
0.85
0.72.0.00
5.84
DTA
0.30g0.03
0.47
0.46
3.52
1_38
3.28
2.40
CD-m
AnGS
20000
0.79 20.05
2.27
4.27
2.80
1..0
Ours
0.25
0.63
0.49
1.33
0.32
0.75
2.94
(b) Three- objects
Complex objects
DIA
ARGS
Ours
+
ArtGS
Ours
CDss
1.01
0.95
1.58
CD-s
1.52
1.64
Storage
CD-D,
0.49
0.25
Storage 47648
47254
CD-D;
1.11
0.41
0.46
CD-Mavg
3.89
4.36
CDss
2,66
1.97
2.12
Tablen249
CD-s
2.11
2.08
Fridge
CD-D,
3,56
1.26
1.26
CD-Mavg
3.60
4.19
10289
CD-D;
Figure A8: The Meta Project Aria Glasses.
D.3 ADDITIONAL RESULTS UNDER THE OPEN-START AND OPEN-END SETTING
As shown in Tab. A5 and Tab. A6, evaluate all methods under the same input setting used in AnGS and DTA, namely, the open-start and open-end configuration. Meanwhile, we render the sequences using the motion parameters provided by AnGS. Under this setting, both ArtGS and DTA achieve good articulation estimation, particularly because the geometric correspondence between the two states clear and the part number known. This also makes the articulation estimation of ArtGS stable, with almost zero variance in the evaluation of articulation estimation. Mcanwhile, without structural priors as input, our AIM also achieves accurate and stable articulation estimation through
based manamic-staiio disentanglement and robust motion-based Sequential RANSAC. As shown in Tab, A5, Tab. A6, our results are comparable to these optimisation-ont baselines, and in challenging complex-object cases, AIM is more stable. Meanwhile, AIM's part segmentation, driven only by motion cues in interaction videos and without requiring the number of parts, remains par with even surpasses recent tfate-of-thh-att ArtGS.
D.4 ADDITIONAL RESULTS ON REAL--WORLD. DATA
D.4.1 REAL- DATASET ACQUISITION
To better support natural human-object interaction during data capture, we leverage the Meta Project Aria Glasses (as shown in Fig. A8) to record the interaction video. In detail, videos are recorded real time using the device's built-in fisheye cameras while the user observes the target object and manipulates its movable parts. As shown in the video, during the interaction process, the user first walks into the scene and observes both the surrounding environment and the articulated objects in their closed-stan start state. To achieve the automatic pipeline, the user then signals the beginning of interaction by using the hand to touch the target object i.e. the oven). While manipulating the movable part, the user freely moves the head to observe the object from different viewpoints. The
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hand is then removed inspect the object again. The user subsequently touches another object ( (.ee the storage) and repeats the same manipulation aadiiiio-nndooobbratttiooo procedure.
Start
Hand In
Hand out
End
Figure A9: The captured interaction vidcos (The vidco could be found in the Supplementary Material). In particular, we use the hand as indicator to automatically detect the motion start (hand in) and end times (hand out), enabling fully automated data-processing pipeline.
Figure A10: The estimated pose of real-world video via using COLMAP Schonherger & Frahm (2016).
For data processing, we first extract the recordings from the Aria glasses. Since the RGB cameras on Aria glasses are fisheye cameras, we apply the official Project Aria toolkit (Engel et al., 2023) to rectify each frame and re-project it into a pinhole camera mode. This produces set of undistorted pinhole frames along with their corresponding timestamps (see Fig. A9). We then extract keyframes with FFMPEG and manually filter the frames to remove blurred or low-quality images. Finally, as shown Fig. A10, the curated images are fed into COLMAP (Schonberger & Frahm, 2016) to estimate camera poses via conetture-fro-mmotiii struction. Especially, we follow the process, introduced video2atticulation Peng et al. (2025), to obtain the whole articulated object (i.e. oven and storage) via Grounded SAM2 Ravi et al. (2024); Ren et al. (2024) with the two text prompts: "silver microwave" and "wooden storage". The final inputs AIM consist of 100 start-state frames and 87 motion frames for the oven sequence, and
which are used as inputs for subsequent recon-
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77 start-state frames and 58 motion frames for the storage sequence. Although the number of mo- tion frames is much smaller than in our rendered dataset, the following results show that AIM still performs robustly and accurately under this limited motion input.
D.4.2 EXPERIMENTAL RESULTS ON REAL-WORLD DATA
As shown in Fig. All and Fig. A12, we present our results on the real-world captured sequences. AIM performs robust and accurate part mobility analysis purely from RGB inputs, without any struc- tural prior knowledge. Notably, AIM reliably predicts the correct joint-axis direction and achieves low-error articulation estimation, such as the oven door's nearly 85 opening motion, which is pre- dicted about 82%. Moreover, our SDMD module correctly reassigns newly revealed static regions during motion, such as the oven interior. For the storage example, despite significant occlusion from the and the user's hand, AIM still produces correct part-level segmentation and articu- lation estimation. These results further confirm the strong motion analysis ability of representation and the generalisation capability AIM in challenging real-world scenarios.
Limitations:
From the real-world data, we observe that when motion introduces structural ambiguities-purriculute
those caused by specular reflections, such as the glass door of the oven in the video video-our our vanilla 3DGS-based reconstruction in AIM can be affected. In future work, we plan to further address such challenges, for example, by incorporating depth information improve robustness under complex lighting and reflective surfaces. Moreover, as discussed in Sec. 5, we will extend AIM to more diverse and larger real-worke scenes in future work.
Articulation Estimation
1g c 0)
Statio:
& 0e 13: Revolute
(0298 01939, 0.171). 0-1409).
Rendered predicted
Figure A11: Qualitative results our AIM on the real-world data of the oven. Left: Comparison between ground-truth views and rendered views. Besides, we provide the rendered masks based on our dual-Gaussian representation (via directly changing the spherical harmonics of Gaussians). Due to the strong specular re- llections the oven's 's glass door, the appearance of the moving part undergoes frequent and significant changes during interaction. Despite this challenge, our dual-Gaussian representation still achieves clean dynamic-stann static disentanglemer by relying on stable motion cucs. Moreover, the SDMD module reliably reassigns the newly revealed static interior regions back to the static base as the motion unfolds, further improving the quality of disentanglement and reconstruction. Right: Our frior-free part mobility analysis. Based on our Hual-Gaussian representation, easily infer the trajectories of moving Gaussians, and obtain the articulation parameters based on the optimisation-Frs
robust sequential RANSAC without any prior structural knowledge.
E ABLATION STUDY
In Fig. A13, we present qualitative ablations by removing individual modules, highlighting the im- portance of each component. As shown in Fig. A14, we also provide the comparisons between our dual Gaussian representation and the deformable Gaussian. The dynamic-static disentanglement of dual Gaussian representation can better track the moving regions and support more accurate trajectory- part segmentation.
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Articulation Estimation
(8 o):
Static:
(G
(d - 1=-000209,00318 0.318, -0.903 .-1023)
Rendered
Rendered predicted
GT
Figure A12: Qualitative results of our AIM the real-world data of storage. Left: Comparison between the ground-truth views and rendered views. Besides, we provide the rendered masks based our nual-Gaussian representation (via directly changing the spherical harmonics Gaussians). Although large regions age are occluded by the oven and the hand during the interaction video (see Fig. A9), AIM remains robust and accurately performs dynamic-static disentanglement, enabling clean separation of the static base and the mov- ing part purely from motion cues. Right: Our prior-free part mobility analysis. Based representation, we can casily infer trajectories of moving Gaussians, and obtain the articulation parameters based on the optimisation free and robust sequential RANSAC without any prior structural knowledge. (No- tably, for prismatic joint, we do not consider the axis pose).
stor-
Gau-SGa
Full
w/o dual-GS
w/o RANSAC
w/o SDMD
w/o start state scan
Figure A13: Qualitative comparisons for the ablation studies.
Deformable 3DGS
Figure A14: Part segmentation results based on Deformable 3DGS and our dual-Gaun Gual-Gaussian representation. We
cluster Gaussians using the same sequential RANSAC settings; colours denote groups. Static noise in De- formable 3DGS noticeably degrades the segmentation.
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Reviews:
[{"review_id": "oWe6HJAilf", "reviewer": "Reviewer_ch7L", "summary": "Paper addresses the problem of piecewise-rigid reconstruction via combining old-school computer vision (sensible priors in a dual Gaussian formulation and sequential RANSAC) with modern Gaussian-based 3d rendering.\n\nThis is a solid piece of engineering that shows that the use of established CV methods can substantially contribute to the performance of modern systems.\n\n+ Comfortably state-of-the-art \n+ Good ablation studies showing the contribution of each component.\n+ Thoughtful qualitative results/figures making it clear how each component is useful.\n\nIt's worth mentioning that this paper opens the door to more sophisticated forms of model fitting than sequential RANSAC. However, the proposed benchmark is saturated by this approach. More challenging data would be needed before anything better than greedy optimization is needed.", "questions": "-", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "This is an extremely well-motivated approach where the design decisions have a clear and obvious contribution and that substantially improves over multiple recent existing approaches.\n\nThe writing is clear, and the design decisions are well supported by experimental evaluation.\n\n+ Comfortably state-of-the-art \n+ Good ablation studies showing the contribution of each component.\n+ Thoughtful qualitative results/figures making it clear how each component is useful.\n\nThis paper opens the door to more sophisticated forms of model fitting than sequential RANSAC. However, the proposed benchmark is saturated by this approach. More challenging data would be needed before anything better than greedy optimization is needed.", "weaknesses": "The clarity of the approach probably works against this paper (at least at review time, this clarity is a benefit when published), and I suspect that some of the other reviews may complain about lack of novelty as it looks obvious in hindsight. Of course, if it was obvious, the existing approaches would already be doing something similar, and the performance improvement would be less noticeable. \n\nI think describing the dual Gaussian approach as prior free is somewhat misleading. The reason it works well is because it injects a healthy prior that most of the world is static, which makes the actual task much easier.\n\nThe dataset is small by modern ML dataset standards, consisting of few enough sequences that they can be named and discussed individually. However, this is fairly common in 3d reconstruction, where if you want high-quality ground truth, you typically have to pay the cost in terms of a small number of evaluation sequences.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bw0Nxx9goq", "reviewer": "Reviewer_Rvjo", "summary": "The paper introduces a novel framework called ”Articulation in Motion” (AIM), which performs reconstruction, segmentation, and articulation analysis of articulated objects. The authors propose a practical solution that requires only a user-object interaction video and a start-state scan to generate a part-level decomposition, an abstraction of the articulation kinematics, and a 3D digital replica. The framework is developed over three stages: the first consists of a 3D Gaussian splatting reconstruction of the object in an initial static state; in the second step, the static 3D Gaussian splatting is optimised, and jointly another 3D Gaussian splatting representation of the moving parts is generated from the video. At this stage, the novel static parts revealed by the motion of the articulated parts are detected using a static-during-motion (SDMD) module and added to the static representation. In the third stage, with a sequential RANSAC, the trajectories of each moving primitive are grouped to perform motion-based part segmentation. The proposed solution has been tested over a dataset provided by the authors, and it has been compared with state-of-the-art methods by overcoming them, especially on the\nsegmentation of the articulated parts.", "questions": "1. Given the interaction video, is it possible that two parts are in motion at the same time? Or is the interaction constrained to a single specific part motion?\n2.  The number of states is not clear.\n3.  How is the freezing and unfreezing of the static Gaussian managed?\n4. I would appreciate some clarification on the dual optimisation.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "• The internal organisation of the paper is a significant strength, ensuring that each component of the proposed solution is introduced and explained coherently and sequentially.\n• The proposed framework introduces a strong methodological contribution, integrating several state-of-the-art algorithms in a novel manner to solve an important problem, such as the analysis and reconstruction of articulated objects without any prior knowledge of the object's moving parts.\n• The quantitative and qualitative evaluations reported in the experiment section confirm the validity of the proposed solution. In particular, the proposed method outperforms all state-of-the-art methods across metrics related to part segmentation, while achieving above-average results in the other metrics.", "weaknesses": "- The dual Gaussian representation is introduced as novel, but it seems the same as the dense static Gaussians and sparse dynamic Gaussians introduced in ArtGS.\n- Apart from the novelty of the video interaction, it is not quite clear where the novelty here is w.r.t. ArtGS.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IkIgXa6Rnz", "reviewer": "Reviewer_ZaoU", "summary": "The paper tackles the task of reconstructing articulated objects from a user-object interaction video and a start-state scan. Previous methods mainly focus on reconstruction from two different articulation states, which the authors think limits their applications and performance. The authors propose a dual-Gaussian scene representation learned from an initial 3DGS scan. It then applies RANSAC on motion trajectories to segment parts and estimate joint parameters.", "questions": "See above", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper explores a new setting that copes with start-state scans and a human-object interaction video instead of two-state multi-view observations in previous papers. This sounds more reasonable and practical than previous papers.\n2. The proposed method use RANSAC to perform part discovery, eliminating the necessity of knowing part numbers ahead.\n3. The idea of dual-Gaussian representation that differentiate static and moving Gaussians sound reasonable to me.", "weaknesses": "1. One of the claim of this paper is that it copes with a human-object interaction video and a start-state scan instead of two-state multi-view observations. But the dataset used in the paper seems to be rendered from PartNet-Mobility. Some real-world examples should be helpful.\n2. The baseline choices seem weird. The chosen baselines are mainly PARIS, DTA, and ArtGS, which mainly focuses on two-state observations. Some more plausible baseline may be Video2Articulation[1]. \n3. The authors should also elaborate on how they perform these baselines as the setting are very much different.\n\n[1] Generalizable Articulated Object Reconstruction from Casually Captured RGBD Videos\n\nI slightly lean towards borderline reject, and I will change my rating if my concerns are addressed and consider other reviewers' comments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "n5Cj8F0UqQ", "reviewer": "Reviewer_3jJi", "summary": "This paper proposes Articulation in Motion (AIM), which reconstructs articulated objects from motion videos instead of two-state scans. It uses a dual-Gaussian representation (static + deformable) to separate moving/static parts, then applies Sequential RANSAC on motion trajectories to segment parts and estimate joint parameters without knowing part count.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Novel Problem Formulation: The shift from two-state (start-end) analysis to continuous motion video is well-motivated and more practical. The paper correctly identifies that many articulated objects cannot be well-represented by just two discrete states, especially when interior parts are gradually revealed.\n\n2. Technical Contribution - Dual-Gaussian Representation and Static-During-Motion Detection (SDMD): The proposed dual-Gaussian approach ({G^S} for static, {G^M,t} for moving) addresses a real limitation of prior work. The joint optimization strategy with selective pruning is useful.\n\n3. Prior-Free Framework: Eliminating the need to know the number of parts is a significant practical advantage. The sequential RANSAC approach for automatic part discovery is well-suited to this problem.", "weaknesses": "1. Unrealistic Trajectory Assumption (T=2 Sampling): The paper's core technical assumption is critically flawed for real-world videos. The method samples T=2 timesteps: {0 → 0.5, 0 → 1}, which assumes that all the parts of an articulated object should have different state at t=0, 0.5, 1.0. However, in fact, when we capture videos of interacting with objects in real scenes, we usually can only interact with different parts one by one. This means that in a video with hundreds of frames, only a few dozen frames of a certain part may be in motion.  For example:\n- t=0.0: Door closed (0°)\n- t=0.5: Door closed (0°)      ← No motion detected\n- t=0.7: Door opens and then closes\n- t=1.0: Door closed (0°)      ← No motion detected!\n\n2. Questionable Baseline Results: The reported performance of ArtGS and DTA is suspiciously poor and inconsistent with the original paper and other current works [1, 2, 3]. For example, Table 2 shows ArtGS achieving 473.72mm CD-m on Blade and 155.65mm on Washer—these are catastrophically bad results. Similarly, Table 3 reports axis angular errors of 89.91° and 89.92° on Fridge and Oven, meaning the estimated joint axes are essentially perpendicular to ground truth, suggesting complete method failure rather than mere performance degradation. These results are orders of magnitude worse than what ArtGS reports in their original paper and what subsequent works[1, 2, 3] have achieved when reproducing ArtGS. If the method truly failed this catastrophically, the authors should explain why, but no such analysis is provided. Other recent papers successfully reproduce ArtGS with reasonable performance, making these results highly suspicious. Additionally, the performance of ArtGS on complex objects is even better than that on two-part objects. This abnormal phenomenon is also an unreasonable reproduction resultWithout credible baseline implementations, the claimed improvements cannot be validated—this is a fundamental flaw that invalidates the entire experimental section.\n\n3. Lack of results in real-world scenario: despite emphasizing practical advantages over two-state methods, the paper provides zero real-world experiments. The authors never demonstrate that their system works on actual captured videos of people opening doors or manipulating objects. Without real-world validation, it's unclear whether the method's restrictive assumptions (fixed T={0,0.5,1.0} timesteps) actually provide any practical advantage over existing approaches. \n\n[1] Self-Supervised Multi-Part Articulated Objects Modeling via Deformable Gaussian Splatting and Progressive Primitive Segmentation\n[2] Part2GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting\n[3] REArtGS: Reconstructing and Generating Articulated Objects via 3D Gaussian Splatting with Geometric and Motion Constraints", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)