Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting
Authors: Yu Liu
Baoxiong Jia
Ruijie Lu
Junfeng Ni
Song-Chun Zhu
Siyuan Huang
Venue: ICLR 2025 Poster (2025)
Keywords: articulated object modeling
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Building interactable replicas of articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the accuracy of part-mesh reconstruction and part dynamics modeling, particularly for complex multi-part articulated objects. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation to address these issues. Our method incorporates canonical Gaussians with coarse-to-fine initialization and updates for aligning articulated part information across different object states, and employs a skinning-inspired part dynamics modeling module to improve both part-mesh reconstruction and articulation learning. Extensive experiments on both synthetic and real-world datasets, including a new benchmark for complex multi-part objects, demonstrate that ArtGS achieves state-of-the-art performance in joint parameter estimation and part mesh reconstruction. Our approach significantly improves reconstruction quality and efficiency, especially for multi-part articulated objects. Additionally, we provide comprehensive analyses of our design choices, validating the effectiveness of each component to highlight potential areas for future improvement.

Published as a conference paper at ICLR 2025
BUILDING INTERACTABLE REPLICAS OF COMPLEX ARTICULATED OBJECTS VIA GAUSSIAN SPLATTING
Yu Liu 1,, Baoxiong Jia?.*, Ruijie L Junfeng 2 Song-Chun Zhu .2.3, 22.3, Siyuan Huang?
Tsinghua University "State Key Laboratory of General Artificial Intelligence, BIGAI "Peking University
ABSTRACT
Building interactable replicas of articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the accuracy of part-mesh reconstruction and part dynamics modeling, particularly for complex multi-part articulated objects. We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation to address these issues. Our method incorporates canonical Gaussians with coarse-to-fine initialization and updates for aligning articulated part information across different object states, and employs süinning-inspired part dynamics modeling module to improve both part-mesh reconstruction and articulation learning. Extensive experiments on both synthetic and real-world datasets, including new benchmark for complex multi-par objects, demonstrate that ArtGS achieves state-of-ther the-art performance joint parameter estimation and part mesh reconstruction. Our approach significantly improves reconstruction quality and efficiency, especially for multi-part articulated objects. Additionally, we provide comprchensive analyses of our design choices, validating the effectiveness each component to highlight potential areas for future improvement. Our work is made publicly available at: https: /aarticuaateeaaeee g thub. 1o.
INTRODUCTION
Articulated objects, central everyday nunaan-enviromment interactions, have become key focus in computer vision research (Yang et al., 2023a; Weng et al., 2024; Luo et al., 2025; Liu et al., 2024;
Deng et al., 2024). Accurately reconstructing real-world scenes (Chen et al., 2024b; Ni et al., 2024; Lu et al., 2024b) and creating interactable digital replicas of these objects are essential for various applications, including scene understanding (Jia et al., 2024; Huang et al., 2024b; Zhu et al., 2024;
Linghu et al., 2024) and robotics learning (Liu et al., 2022a; Geng et al., 2023b;;; Gong et al., 2023; Yang et al., 2024b; Zhao et al., 2024; Lu et al., 2024a). By building high-fidelity digital twins of articulated objects, we bridge the gap between synthetic and real-works scenarios, thus facilitating the sim-to-real transfer of robotic systems (Torne et al., 2024; Kerr et al., 2024). As we advance towards more sophisticated robotic systems and immersive virtual environments, there is a growing need for improved and efficient modeling techniques for the reconstruction of articulated objects.
The problem of reconstructing articulated objects has been extensively studied (Liu al., 2023a;b; Weng et al., 2024; Deng et al., 2024; Yang et al., 2023a), with a key challenge being the learning of object geometry when only partial views of the object are available at any given state. To accurately reconstruct object parts (e.g., a closed drawer), it is essential to integrate observations from multiple object states during interactions (e.g., the opening process of the drawer). This necessitates the simultaneous learning and alignment of fine-grained object parts across different states, which must be achieved jointly during the reconstruction of object geometries. Such a requirement presents significant challenges in object modeling, especially for complex everyday articulated objects that often consist of multiple interactable parts. Additionally, uncertainties in object geometry reconstruction introduce further challenges in modeling articulation, as errors in geometry modeling can result in inaccurate learning of articulation parameters. These challenges highlight the need for improved models that handle the complexities of multi-pan malti-part articulated objects.
Equal contribution. *Work done intern BIGAT

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Recent approaches attempt to address these challenges using part priors from pre-trained models. These models provide either part segmentation masks via models like SAM (Kirillov et al., 2023), or 2D pixel correspondences for aligning pixels across states (Sun et al., 2021). However, these methods rely heavily on priors from pre-trained models, often using single-state inputs and neglecting critical motion information (Mandi et al., 2024) and struggling with the complexity of multi-part objects when accurately matching pixels across states becomes difficult (Weng et al., 2024). These limitations result in unstable and inconsistent learning of object parts, posing significant challenges to the joint learning of part motion and geometry.
To address these challenges, we propose ARGSS, which introduces several key innovations for handling complex multi-part articulated objects. Specifically, we adopt the commonly used two-state setting for learning articulated objects, as established in prior works (L.iu et al., 2023a; Weng et al., 2024). Central to our approach is the use of 3D Gaussians (Kerbl et al., 2023) as the foundational representation, chosen for their ability to explicitly maintain spatial information while offering efficiency and high reconstruction quality. To effectively model object dynamics and integrate information across multiple object states, we employ canonical Gaussians with a carefully designed coarse-to-fine initialization and update scheme. These Gaussians act bridge between different input object states, enabling accurate deformation modeling that improves both mesh reconstruction and articulation learning. Building on the canonical Gaussians, draw inspiration from Gaussian skinning (Song al., 2024) and introduce a center-based clustering module for part and dynamics learning. This approach leverages motion priors of Gaussians, which are summarized during the learning process, serving as a guide to better align object parts between states and improve articulation learning. These designs allow our method to achieve state-of-the- the-art performance in joint parameter estimation and part mesh reconstruction, excelling on both existing benchmarks and our newly curated complex multi-part articulated object reconstruction benchmark. Our approach outperforms existing methods in both synthetic and real-world scenarios, with significant improvements in axis modeling and overall efficiency. Through extensive experiments, we demonstrate the effectiveness of our model in efficiently delivering high-quality reconstruction of complex multi-pat part articulated objects. We also provide comprehensive analyses of our design choices, highlighting the critical role of these modules and identifying areas for future improvement.
Contributions Our main contributions of this work can be summarized as follows:
We propose ArGSS, a novel and efficient method for articulated object reconstruction that achieves state-of-the-ar performance, particularly for complex multi-part objects.
We introduce coarse-to-fi fine canonical Gaussian initialization and Shinning-inspieee part dynamics modeling with self- self-guided guided motion priors to improve object part and articulation learning, effectively addressing the limitations of existing methods in using object motion information.
We conduct extensive experiments on both synthetic and real-world articulated objects, demon- strating the effectiveness, efficiency, scalability, and robustness of our approach. We also provide comprehensive ablation studies to validate our designs and highlight areas for future improvement.
2 RELATED WORK
Dynamic Gaussian Modeling Recent advancements have shown the potential of Gaussian Splat- ting (Kerbl et al., 2023) for 4D reconstruction (Jung et al., 2023; Katsumata et al., 2023; Wu et al., 2024; Luiten et al., 2024; Li et al., 2024; Lu et al., 2024c; Lei et al., 2024; Guo et al., 2024; Qian et al., 2024; Bae et al., 2024; Wan et al., 2024b). A central focus of these efforts is the deformation modeling of 3D Gaussians. While effective for dynamics capturing, most approaches learn transformations implicitly, limiting their capability for controllable dynamics modeling. To address this issue. recent studies use superpoints (Huang et al., 2024c; Wan et al., 2024a) for improved dynamics modeling and control. However, as superpoint learning based primarily on rendering without considering object physics, these methods fail to reliably capture accurate physical parameters (e.g., joints and axes). Another line of works (Xie et al., 2024; Jiang et al., 2024) introduce controllable Gaussians by integrating physics-based modeling for graphics simulations. These models require intricate priors of objects (e.g., material properties), making them impractical for reconstructing everyday articulated objects. To overcome these challenges, our work combines the explicit 3D Gaussian modeling with articulation modeling, enabling efficient and high- reconstruction with precise articulation parameter estimation for more practical digital-twin construction of articulated objects.
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Articulation Parameter Estimation Estimating joint articulation parameters for articulated ob- jects has been extensively studied, with approaches broadly categorized into two main categories. First, cnddictiio-bbsee methods estimate joint parameters from sensory imputs of different object configurations (Huang et al., 2014; Katz et al., 2013) or use end-to-end models (Hu et al., 2017; Yi al., 2018; Li et al., 2020; Wang al., 2019; Sun al., 2023; Liu et al., 2022b; Weng et al., 2021; Sturm et al., 2011; Chu et al., 2023; Martín-Martin et al., 2016; Liu et al., 2023e; Gadre et al., 2021; Mo et al., 2021; Jain et al., 2021; Yan et al., 2020; Lei et al., 2023) to predict part segmentation, kinematic structure, as well as joint parameters. Second, reconstruction- based methods optimize articulation parameters by reconstructing multi-view images or videos (Wei al., 2022; Tseng et al., 2022; Mu et al., 2021; Lewis et al., 2022; Liu et al., 2023a; Lei et al., 2024; Deng et al., 2024; Swaminathan et al., 2024; Noguchi al., 2022; Zhang et al., 2021; Pillai et al., 2015; Liu et al., 2023b). Most of these methods treat articulation parameter estimation as a separate task, without generating high-quality, interactable part-mesh reconstructions. ArtGS aims to address this gap by integrating part-mesh reconstruction and articulation parameter estimation, enabling the creation of high-quality, interactable replicas.
Articulated Object Reconstruction Articulated object reconstruction, differing from human and animal motion modeling (Joo et al., 2018; Loper et al., 2023; Mihajlovic et al., 2021; Noguchi et al., 2021; Yang et al., 2021 b;;; Romero al., 2022; Zuffi et al., 2017; Yang et al., 2024a; Xu et al., 2020; Tan et al., 2023; Yang et al., 2022; 2023b; Song et al., 2023a; Yang et al., 2023a; Song et al., 2023b), focus on the piece-wise rigidity of each part, requiring both part-level geometry reconstruction and joint articulation parameter estimation. While end-to-end models predict joint parameters and segment object parts from single-stage (Heppert et al., 2023; Wei al., 2022; Kawana et al., 2021) or interaction observations(Jiang et al., 2022: Ma et al., 2023; Nie et al., 2022; Hsu et al., 2023), they struggle to generalize to unseen objects. Per-object optimization approaches (Liu et al., 2023a;b; Weng et al., 2024; Deng et al., 2024; Swaninathan et al., 2024), using multi-state observations for articulation modeling, offer better adaptability to unknown objects but face scaling issues of multiple joints. Methods like DTA (Weng et al., 2024) attempt to handle multi-pan -art objects but still struggle with those having more than three movable parts. We address the reliability, flexibility, and scalability issues of previous works with canonical Gaussian design and akinning-inspired part dynamics modeling, achieving higher accuracy, robustness, and efficiency for articulated object reconstruction.
3 PRELIMINARIES
3D Gaussian Splatting 3D Gaussian Splatting (3DGS) represents a static 3D scene using 3D Gaussians (Kerbl et al., 2023). Each Gaussian G, is associated with a center μas covariance matrix E, opacity o; and spherical harmonics coefficients h;. The final opacity of a 3D Gaussian at a spatial point æ can be calculated as:
(x( a; exp (- (4))
(m=)))
(x: E.
(1))) mi)): where Σ; R.S.S. R.
(1)
As the physical meaning of covariance matrix only valid if it is positive semi-definite, we decompose the covariance matrix E, following Eq. (1) into a scaling diagonal matrix S, and a rotation matrix R. parameterized by a quaternion r:- A scene is then described with a collection of such Gaussians G = {G; p.; ri, 8;, o. N We render an image I and optionally its depth image D from the 3D scene G by projecting each Gaussian onto the 2D image plane and aggregating them using a-blending:
N Σ Tho SS U.), D
N
H Ti Pd, where T:
I
Σ
[[ j=1
)D)
(2)
2DD is 2D version of Eq. (1), with μ4i E, replaced by the projected n2?0 EPD and the pixel coordinate u. SH((-) is the spherical harmonic function, v, is the view direction from the camera to pt, de is the depth of the i-th Gaussian. Given N. input view images {I, N,, 3DGS learns Gaussians with:
(3) where C, = I Il is the Ll-loss, LD.SSIM M is the D-SSIM loss (Kerbl et al., 2023), ASsIM is the weight of D-SSIM loss, and LD log (1 D D111) is the optional depth supervision.
Lrender (1 1--AsssMM Lr + ASSIM + Cp,
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Mubi-siow & RGB-D
Obiain Crase Canonical Geuesiare
Stage 1:
cintly O plinice C Canonical G eussians and Artiuu etion Model
Slage 2:
Gaas Recenstrucion
Saate
Gingle
Maxching Cheter
Anticaaatoon Model
67
Render
State
Sande
Distance Len
Rendering
Eyender C. + Cespa Earets
Figure 1: The overview of ArtGS. Our method is divided into stages: (i) obtaining coarse canonical Gaussians Fin by matching the Gaussians Sangle and Paigle trained with each single-state individually and initializing the part assignment module with clustered centers, (ii) jointly optimizing canonical Gaussians G and articulation model (including the articulation parameters V/ and the part assignment module in Sec. 4.2).
Mesh Extraction from Gaussians To extract meshes from Gaussian splats G, we render depth maps and utilize Truncated Signed Distance Function (TSDF) to fuse the reconstructed depth maps, and extract the object mesh M with marching cubes (Huang et al., 2024a). This process can be done with Open3D (Zhou et al., 2018) with proper choice of voxel size and truncated threshold.
4 METHOD
Given N, RGB-D images of an unknown articulated object (T: D at two joint states E (0, 1}. we aim to reconstruct its part-level meshes M and joint articulation parameters y. We define of learnable canonical Gaussians 9 which can be transformed into joint state Gaussians G via per-Gaussian SE(3) transformation T parameterized by V. Formally,
go = T Ge and g = (T ge for E (0.
(4)
We impose the continuity of motion between the joint states by setting the canonical Gaussians 9 at the mid-state (c: t = 0.5), enforcing that 7'-- = (T"=1) This simplifies the articulation learning and connects the two input joint states through the canonical Gaussians 9 solving potential issues of occlusion and misinformation when reconstructing object meshes separately on the two joint states.
Using this motion model, we leverage multi-view RGB-D images from the two input states to learn both the canonical Gaussian G", the transformation r or equivalently the joint parameters V and extract object meshes M for different joint states following See. An overview of ArtGS presented in Fig. 1, with details on key designs provided in the following sections.
4.1 COARSE-TO-FINE CANONICAL GAUSSIAN INITIALIZATION WITH MOTION ANALYSIS
The initialization of the canonical Gaussians 9 is crucial for articulation learning. A good initial- ization leverages the consistency between input joint states, improving mesh reconstruction and articulation modeling. In contrast, a random initialization leads to undesirable local minima, adversely affecting the learning process (see in Fig. 4). To tackle this issue, we propose a coarse-to-fin strategy for the canonical Gaussian initialization, incorporating preliminary motion information from the two input joint states to enhance subsequent articulation modeling.
Coarse Initialization by Matching Single-state Gaussians In this phase, we first separately train two sets of single-state Gaussians Gingle with input multi-view images following Fq.. (3). We then apply Hungarian Matching to obtain matched Gaussian pairs between Gingle and G1 single based on the distance between Gaussian centers. We take the mean of each pair of matched Gaussians as the coarse canonical Gaussian initialization Go To reduce the significant computation time associated
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with matching large number of Gaussians, Farthest Point Sampling (FPS) to downsample the learned single-state Gaussians to a set of 5K Gaussians prior to matching.
Initialization Refinement with Motion Analysis To support geometry reconstruction and articula- tion modeling, relying solely on 5K matched coarse Gaussians alone is insufficient. Therefore, we refine the coarse initialization Peaare guided by the motion information of object parts. Intuitively, single-state Gaussians, Snngte and singte should exhibit consistency for static object parts discrepan- cies for movable parts, ie., the static parts of these Gaussians are well-learned. Based on this insight, refine the set of coarse canonical Gaussians Gsarss by including Gaussians corresponding static parts, allowing more focused learning of movable parts during articulation modeling. In practice, we classify each Gaussian G; in joint state f static or dynamic by calculating its minimum Chamfer Distance to all Gaussians in the opposite state E:
CD min || 15ll2,
G E Gaingles Gj Single and CD = Mean (CD)-"). (5)
If the distance CD exceeds a threshold Gantie. G3 is classified dynamic; otherwise it is static. To determine which state, t or i, contains more motion information, we compare the mean distance CD' of all Gaussians in state following Eq. (5) and classify the higher state as the more motion informative state. For instance, a cabinet with open drawers provides clearer identification of movable parts than one with closed drawers. With this information, we add the static Gaussians from the more motion informative state to refine Gooass into the final initialization of the canonical Gaussian Gini
4.2 PART DISCOVERY FOR ARTICULATION MODELING
Following Eq. (4), use part-based formulation for articulation modeling. Specifically, given the number of parts K, we aim to decompose the Gaussians into parts and learn the articulation 11k paramerters V {TT-1 1--1 In contrast to existing works that leverage prior information for part discovery (Mandi et al., 2024; Weng et al., 2024), we discover parts in an unsupervised manner during learning.
Center-based Part Modeling and Assignment Given input canonical Gaussians 9 = (G
our objective is to compute part-level masks M R that assign each Gaussian G3 to a specific part. A common approach to generating these assignment masks is through unsupervised segmentation modules using MLPs or slot-attention (Locatello et al., 2020; Jia et al., 2023; Liu et al., 2025). However, these models implicitly segment parts and fail to leverage the explicit spatial and dynamic information present in 3D Gaussians. We observe that such methods struggle with parts that exhibit similar motion patterns, leading to incorrect assignments. To address this issue, we adopt center- based part modeling approach that explicitly utilizes spatial information, inspired by sparse control points from SC-GS (Huang et al., 2024c) and quasi-rigid blend skinning in REACTO (Song et al., 2024). Specifically, we define K learnable centers C₁ (Pk. Vt. A&) with center location Pa E R3, rotation matrix V E |Rax3 and scale vector 2 E IR3. For given Gaussian G, E g we compute the Mahalanobis distance Dis between G; and center Cş as:
X [V& p A
D
WA T
D (X.)² X and M GumbelSoftmax
(6)
where D. is the distance matrix for part assignment. One challenge of using the distance matrix for part assignment is identifying sharp boundaries when two parts overlap spatially (e.g.. in the case of a closed drawer). To improve boundary identification, we introduce a residual term WA- = MLP(μ, X, D), predicted by a shallow MI.P that concatenates the absolute position of each Gaussian and the distance matrix D as input. This residual is added to the original distance matrix D to refine the part assignment mask following Eq. (6). Notably, we use Gumbel Softmax to ensure that each Gaussian is assigned to only one part, which simplifies the optimization of joint parameters. Detailed implementation can be found in Appendix A.
Center Initialization by Clustering Coarse Gaussians We empirically find that the initialization of centers p& and scale X have great impacts the correctness of part discovery in later learning
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process (see Fig. 4). Therefore, similar to the canonical Gaussian initialization described in Sec. 4.1, we utilize the motion type of each joint as additional information for providing good initializations of part centers. Specifically, static and dynamic parts. For static parts, we take the mean of the Gaussians as the part center. For movable parts, do spectral clustering on the positions of movable Gaussians (K clusters) and take the mean of each cluster for part center initialization. We use the distance from the farthest point the center of each cluster as the initial scale.
select the imput joint state with more motion information to identify
4.3 SELF-GUIDED ARTICULATION TYPE AND PARAMETER LEARNING
After obtaining object part representations, we define the per-part articulation parameters via dual-quaternions. Formally, the joints articulation parameters V (TX JR-1 {q (qs.r, (e..r, (= where Qg,r and quid are the real and dual part of the dual-quaternion that determine the rotation and translation of the joint transformation respectively. For notational simplicity, we use qf for of in the following texts. With the mid-state assumption Sec. 4, we have q (q is the inverse of qual-quaternion q. Given object masks M obtained in Sec. 4.2, the per-gaussian qual-quaternion qi for Gaussian G, go is given by:
di
Σ Mik L. Σ Ma K.a).
where (gh)-1 is the inverse of dual-quaternion q and Mis denotes the probability of Gaussian i belongs to part k. With the per-gaussian transformation given q. we transform the canonical Gaussian G to get the two joint state Gaussians G with:
m R pf + t,
(8)
where R and t is the per- garssaan rotation matrix and translation vector derived from q' and @ denotes quaternion multiplication operation. We assume that the scale and opacity d, of the Gaussian G, remains consistent under transformation.
To enhance the learning of articulation parameters, we adopt a warm-up strategy for predicting the joint type of each part. During the warm-up stage, we optimize the articulation parameters L = k without any constraints. Next, we develop heuristic for joint type prediction based on the learned rotation qk.r- Specifically, we classify the joint as revolute if the rotation degree of Qk.r exceeds a threshold Geval: and otherwise prismatic. With predicted joint types, we constrain the joint transformation for each part. Specifically, we manually set the rotation quaternion qu.r of prismatic joints as identity quaternion. This operation allows the model focus on optimizing the translation term de of the prismatic joint, thereby obtaining a more accurate estimate of the joint parameters.
4.4 OPTIMIZATION
We train our model using the rendering loss with depth supervision Geoder described in Sec. on the reconstructed G' for the two joint states as discussed in Sec. 4.3. To reduce the chances of learning artifacts during update, we use the single-state reconstructed Gaussians Gliggee as an additional supervision:
N N Σ
CCD
min | 15ll2 G, G,e and Gj E Gaingle
where we calculate the single single-direction Chamfer Distance between the deformed Gaussians g' and single-state reconstructed Gaussians Gingle the loss signal. As these single-state Gaussians are only rough estimate, only introduce this loss in the first IK to 5K steps. Additionally, to regularize the learning of part centers pt, we add another regularization loss as:
Creg Σ
1E10 |Pkk Pxll2. where Ps
Mik
44:
(10)
Ei=1 M
which enforces that the centers P& should be close to the average spatial position of Gaussians in canonical Gaussians Ge that belong to part k. Above all, our supervision could be summarized
L= Crender + >cp LCD + Areg
(11)
We provide more implementation and model training details in Appendix A.

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5 EXPERIMENTS
Datasets We evaluate our method on three datasets: (1) PARIS, a two-part dataset proposed by Liu al. (2023a), which features articulated objects consisting of one static and one movable part. includes 10 synthetic objects from the PartNet-Mobillty dataset (Xiang et al., 2020) and 2 world objects captured using the MultiScan (Mao et al., 2022) toolset. (2) DTA-Multi, a dataset proposed by Weng et al. (2024), containing 2 synthetic multi-part articulated objects from PartNet-Mobility each with one static part and two movable parts. (3) ArtGS-Mule our newly curated dataset, featuring 5 complex articulated objects from Partiit-Mobility with 3 to 6 movable parts.
Metrics Following the evaluation protocols of PARIS (Liu et al., 2023a) and DTA (Weng et al., 2024), we assess the performance of all methods using both mesh reconstruction and articulation estimation metrics. For mesh reconstruction, we compute the bi-directional Chamfer Distance between the reconstructed mesh and the ground truth mesh with 10K uniformly sampled points from each mesh. We report the Chamfer Distance for the whole object (CD-w), the static parts (CD-s), and the movable parts (CD-m). For articulation estimation, we evaluate the predicted articulation using the angular enror (Axis Ang.) and the distance (Axis Pos., revolute joint only) between the predicted and ground- joint axes. We also report the part motion error (Part Motion) which measures the rotation geodesic distance error (in degrees) for revolute joints and Euclidean distance error (in meters) for prismatic joints.
5.1 RESULTS ON SIMPLE ARTICULATED OBJECTS
Experimental Setup We use the PARIS dataset as the benchmark and select Ditto (Hsu et al., 2023), PARIS (Liu et al., 2023a), CSG-reg (Weng et al., 2024), 3Dseg-reg (Weng et al., 2024), and DTA (Weng et al., 2024) as baselines for quantitative evaluation. Following the evaluation setting from DTA (Weng et al., 2024), report all metrics with mean + std over 10 trials calculated the high-visibility joint state. We re-train DTA on the same device (NVIDIA RTX 3090) for training time comparison. Additional results on all joint states are provided in Tab. A.I.
As shown in Tab. 1, our method significantly outperforms existing approaches across all metrics, especially for joint articulation parameter estimation, where ArGS achieves substantially lower errors. This improvement stems from motion model with Gaussian Splatting, which explicitly deforms Gaussians for more precise part transformation modeling, leading to more precise joint parameter estimation. For mesh reconstruction, ArtGS excels reconstructing movable parts, yielding lower CD-m values, especially for real-world objects. While DTA performs well on CD- and CD-s due its state-by-state reconstruction, we show in Fig. 2 that it struggles with the low-visibility state. In contrast, ArtGS achieves significantly better results on the low-visibility state while maintaining competitive results on the high-visibility state. This is attributed to the canonical Gaussians modeling that connects the two input joint states for mutually improved mesh reconstruction. Additionally, ArGS shows consistently better results on real-world objects with significantly faster training time, positioning it an efficient solution for building digital twins of real-world articulated objects.
Results
5.2 RESULTS ON COMPLEX ARTICULATED OBJECTS WITH MULTIPLE MOVABLE PARTS
Experimental Setup We use DTA-Multi and ArGGS-Multi as benchmarks for evaluating complex articulated object reconstruction. On DTA-Multi, we compare our model against PARIS and DTA, while on ArGGS-Mul we use DTA as the main baseline given its strong performance. Similar to Sec. 5.1, we report all metrics with mean over 10 trials for DTA-Multi and 3 trials for AnGSS- multi because of the training time required for baselines. For AnGS-multi, we report the average of all movable parts for articulation estimation and mesh reconstruction due to the large number of parts. Considering the potential eттоr prediction with no mesh for one of the parts, we manually set the Chamfer Distance of the empty prediction 1000.
Results As demonstrated in Tab. 2 and Tab. 3, method consistently outperforms existing methods by a large margin in both mesh reconstruction and articulation estimation. Notably, on ArGGS-Multi, the baseline model DTA struggles with movable part identification and axis prediction
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Table 1: Quantitative evaluation PARIS, Metrics reported mean std over 10 trials at the joint state with higher visibility, following (Weng et al., 2024). PARIS* (Liu al., 2023a) is augmented with depth for fair comparison. DTA is re-trained for time efficiency comparison. Lower (L) is better on all metrics and we highlight best and second best results. Objects with f are seen categories trained Ditto. indicates wrong motion type predictions. Axis Pos. is omitted for prismatic joints (Blade, Storage, and Real Storage).
Syntbria Ofjects
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607
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5.30
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166
1.15
6.32
0.76
3.0
1.26
2:32
1.35
4.5
56:51
1159
7.16
270
6:33
12.45
9.M4
DITA
0.60
0 30ue
4.00m
1.55
2.
2.33
4.5u
055
4.0
3,45
2.6
1099.
667
D.M.
1153
1M
1.87
2.55
6.3g
1.54
7 13
2.63
1.41
2.0
3.39
Dito
141.1
0.99
0.19
0.96
20.54
31.21
15888
12.99
195.33
2.30
42.30
50.00
20,35
35
PARSS
17.9am
7.23
0.15
6.54mm
16.5
30 A6
10.77
265.27
117 117.5%
53,34m
52.48.m
7785
43457
276 2lace
CTD-1
3heg
C5Ging
191
21.71
6.22
256 99
1.95
636
29.76
436
26
6:33
1.39
73.36
442.77
521.00 40
451.33
DSA
0.7
8.13va
0,44
10.11aa:
1,33
1.4
0.45
2.5
0.36
1.56-0
10.79-ca
1595-u
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0.54
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0.13va
0 0.59
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0.5241
1.35
0.45
Li2
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0.66442
6.38vz
3.47-z
Din
2.b
0.51
2.51
1.70
2.36
2.0
1.39
43
3.9
14.8
10.29
PARES*
9.20
5.27
32.00
20.50:14
CD-
10sx-122
C.41
0.11
3.00
1.70
1.9
4.48
0.76
4.00
531
14,20
10:00
195A
0.33
4.54
LiKi
8
1.5%
0.8
3.5
5.1
DTTA
DTA
Ours
GT
DTA
Ours
GT
State 0
State
Figure 2: Qualitative visualizations of PARIS objects. We present reconstruction comparisons between DTA and our model on Real Storage (Top) and Synthetic Blade (Bottom). DTA struggles with mesh reconstruction at the low-visibility state, as it processes each state separately. In contrast, our method leverages the connection between states to improve the reconstruction for both low- and high-visibility stales.
as the number of parts increases, whereas our model maintains high performance regardless of part count. We also provide a qualitative comparison in Fig. 3 for better visualization. Moreover, our method maintains the same time efficiency while the training time of existing methods scales with the number of parts. These results underscore the robustness and effectiveness of our method in modeling complex, multi-part articulated objects.
5.3 ABLATIVE STUDIES
Experimental Setup To verify the effectiveness of our model design, we meticulously design four ablations of ArtGS to identify the impact of key components in our method: (i) Randomly initializing
canonical Gaussians (w/o Cano. Init.), (ii) predicting part assignments with MLP (w/ MLP Seg) or Slot-Attention (w/ SA Seg), (iii) randomly initializing part centers Ck (w/o Center Init.), (iv)
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Published as a conference paper at ICLR 2025
Table 2: Quantitative evaluation DTA--uutti We report averaged metrics 10 trials with different
random seeds. Lower ()) better on all metrics. Joint of "Storage-m" prismatic with no Axis Pos.
Object
Mathod
Axis Ang
Asia Ang
Axis Pos
Axis Pox
Purt Mation 0
Pur Mocice
CD4
CT>m
CDDnn
CDsw
Time (min)
Fridge-s
DTA
0.25
0.06
0.1
0.01
0.23
0.08
0.83
0.44
0.53
0.88
Ous
PARIS
43.26
26.18
1042
70.84
0.54
8.55
128.62
266.71
8.66
Ouss
8.01
0.02
0.01
0.01
0.00
0.78
0.19
0.27
0,93
Table 3: Quantitative evaluation on ArtGS-Multi. Metrics are averaged over 3 trials. Due to the large number of parts, report the average metric for all movable parts. Lower (L) better all metrics. "Table-31249" 3 prismatic joints with no Axis Pos.
Object
Method
Axis Ang
Axis Pos
Part Motion
CD-s
CD-m
CD-w
Time (min)
Table
DTA
24.35
0.12
0.59
104.38
0.55
34
25493 (4 parts)
Ours
1.16
0.00
0.74
3.53
0.74
8
Table
DTA
20.62
4.2
30.8
1.39
230.38
1.00
37
31249 (5 parts)
Ours
0.04
0.00
0.01
1,22
3.09
1.16
8
Storage
DTA
51.18
2.44
43.77
5.74
246.63
0.88
35
45503 (1 parts)
Ours
0.02
0.00
0.03
0.75
0.13
0.88
8
Storage
DTA
19.07
0.31
10.67
0.82
476.91
0.71
45
47468 (7 parts)
Ours
0.14
0.02
0.62
0.67
3.70
0.70
8
Oven
DTA
17.83
6,51
31.80
1.17
359.16
1.01
35
101908 (4 parts)
Ours
0.04
0.01
0.23
1.08
0.25
1.03
8
DTA
Ours
GT
DTA
Ours
State 0
State 1
Figure 3: Qualitative results on multi-part objects. We present reconstruction comparisons between DTA and our model on Storage-47648 (Left) and Table-31249 (Bottom). On ArGSS-Mlti, DTA struggles with movable part identification and axis prediction the number of parts increases, whereas our model maintains high performance regardless of part count, achieving high-quality reconstruction of part mesh and joint articulation.
clustering all Gaussians instead of clustering movable Gaussians for part center initialization (w/o Motion Prior), and (v) learning articulation parameters without the joint prediction warmup stage (w/o Joint Pred.). We select two representative objects: "Storage-47648" with 4 revolute and 2 prismatic joints and "Oven- DDven-101908' 101908" with revolute joints for ablative analysis. Similar to Sec. 5.2, we report the average of all parts over 10 trials for all metrics.
Results and Discussions
As shown in Tab. 4 and Fig. 4, we make the following observations:
Canonical Gaussians Initialization. Omitting this initialization strategy significantly degrades the model performance across all metrics, particularly for movable parts. As illustrated in Fig.

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Published as a conference paper at ICLR 2025
Table 4: Ablative experiments. Lower (L) better all metrics.
Storage 47648 parts)
Oven 101908 parts)
Method
Axis Ang
Axis
Part Motion
CDss
CD-m
CD-w
Axis Ang
Axis Pos
Motion
CDss
CD-m
CDow
Full
0.62
0.67
0.70
0.04
0.23
0.25
wis Cano. init.
24.15 15
0.73
20.61
0.88
495.07
1.25
57.87
2.95
54.45
1.73
1030.19
2.36
Center Init.
52.78
0.83
33.04
1.09
364,19
1.69
28.94
2.36
22.46
1.41
3.36
2.13
Motion Prior
26.74
0.22
21.16
258.23
599.46
115
40.08
0.98
41.06
1,75
503.44
2.35
wob Joint Prod.
0.16
0.02
0.72
0.67
3.90
0.71
0.01
0.01
0.23
1.08
0.25
1.03
MLP Sag
21.84
3.46
31,43
664.25
1:28
12.08
3.33
27.28
7.78
126.95
2.19
w/ SA
25.43
23.22
459.89
1.16
58.04
4.53
51.28
1,26
496.64
2.35
Indiilized Coorse Canonicel Coussiare Can
Optimised Canonical Grussiens G
Poll
Mocion Prior
Pull
(4) wro Motion Prior
Cano. Init.
Figure 4: Abaltion Studies. visualize initialized and optimized canonical Gaussians with their part assignment and centers for the full model, w/o Motion Prior and w/o Cano. Init. We highlight center error, part assignment error, and canonical Gaussian error with red, green, and blue bounding boxes separately.
(5), the absence of our initialization strategy leads to malformed canonical Gaussians, making the model converge to suboptimal local minima during optimization.
Center-based Part Modeling and Assignment. Replacing our center-based part assignment module with MLP or Slot-Attention ("w/ MLP Seg" and "w/ SA Seg") leads to substantial performance drops, especially in joint parameter estimation and movable part reconstruction. This demonstrates the superiority of our center-based approach in accurately segmenting articulated parts.
Center Initialization. Random center initialization performs well for static parts but poorly for movable parts. Clustering all Gaussians fails to reconstruct both static and movable parts due to incorrect center initialization. As illustrated in Fig. 4 (1), clustering on movable Gaussians still produces an incorrect center but provides good starting point for optimization. Our ArtGS will refine the centers in the optimization process as shown in Fig. 4 (3). contrast, clustering on all Gaussians results in entirely wrong center initialization (Fig. 4 (2)), which is difficult to correct (Fig. 4 (4)), leading even worse performance than random initialization. This highlights the importance of our center initialization strategy in achieving accurate part articulation modeling. Joint Prediction Warmup. This technique primarily affects prismatic joints, as we not constrain the transformation of revolute joints. As shown in Tab. 4, predicting the joint type and then refining joint parameters with type constraints slightly improves the articulation reconstruction.
In summary, these ablation studies confirm that each component contributes significantly to its overall performance, playing crucial roles in achieving accurate joint parameter estimation and nigh-quality part mesh reconstruction. We provide further discussions in Appendix B and Appendix C.
CONCLUSION
In conclusion, propose ArtGS, a novel approach for reconstructing articulated objects from two states of multi-view images. By leveraging 3D Gaussians and introducing novel techniques for state aligmment and part dynamics modeling, our approach overcomes key limitations of existing methods. The performance improvements in joint parameter estimation and part mesh reconstruction. particularly for complex multi-part objects, demonstrate the effectiveness of our innovations. Our comprehensive experiments across synthetic and real-world datasets validate the robustness and efficiency ArtGS, while also revealing promising directions for future research. As the demand for accurate digital replicas of articulated objects continues to grow in fields such as robotics and augmented reality, ArGS provides a solid foundation for bridging the gap between physical and virtual environments. Moving forward, anticipate that the principles introduced in this work will inspire further advancements in the field, ultimately enabling more sophisticated and realistic simulations for wide range of applications.
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Published as a conference paper at ICLR 2025
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A IMPLEMENTATION AND TRAINING DETAILS
Canonical Gaussian Initialzation We train single-state Gaussians go and G for 10K steps with loss L = (1- LL +AsSIMLD SSIM + Ao where ASSIM 0.2, 0.01 is used in experiments and Lo is an opacity entropy loss calculated as:
N To (1 ()) log(1 o;)],
o, 1f0,>0.55 L.=-
which encourages Gaussian opacities to approach either 0 or 1, controlling Gaussian count and accelerating training. We then obtain coarse canonical Gaussians by matching Go and g) as described in Sec. 4.1. This stage takes about 2 minutes per object.
Part Discovery for Articulation Modeling As described in Sec. 4.2, given canonical Gaussians 9 = [G.) and K learnable part centers C (px, RR, Aε), we calculate part-level masks M using Eq. (6). We use a learnable hash grid H to encode Gaussian positions and predict the residual term in Eq. (6) as:
[R Pk)] A
Du = (X+)T. X
D DAW
(Du) Wass MLP(PE, (P)), (
M GumbelSSfftaxx
Since the part assignment and articulation parameters are far from optimal at the beginning of training, using hard assignment for Gumbel-Softtne hinders the joint optimization of the part assignment and articulation parameters. To address this problem, we anneal the temperature T from to 0.1 over 10K steps, using soft assignment that similar Softmax when 0.1 and hard assignment otherwise for training stability. This approach allows for more flexible assignments during the early stages of training, facilitating better joint optimization, and gradually transitioning to decisive part assignment as the model converges.
Optimization To enhance the learning of articulation parameters, we adopt a warm-up strategy to predict the joint type of cach part. This process requires 3K-5K steps that take 30 to 50 seconds. Then we train ArGS with joint type constraint for 20K steps, taking 5-7 minutes per object. For hyper-parameters, we set the threshold Ganse to identify static/movable Gaussians Guaii 0.02
max, CD for two-part objects and Estatic 0.05 max, CDD- for multi-part objects. We use Erevol 10 for predicting joint types following PARIS (Liu et al., 2023a). Aed and Areg are set as 100 and 0.1 separately. addition, the CD loss in Eq. (9) aims decrease the distance between deformed Gaussian G and its nearest Gaussians G in Gingle- Since the deformed Gaussians and canonical Gaussians for prismatic joint have large overlap, the nearest Gaussian may be in the opposite direction of the ideal one, making it ineffective for prismatic joints. Thus the CD loss is only used for regularizing the objects that only have revolute joints. Moreover, the densification strategy of Gaussians is cloning or splitting one Gaussian when the gradient of its center μa is greater than a threshold Cdensify- This is effective for static scenes but meets challenges for dynamic scenes. In the early stage of training, the large gradient is often due to deformation error. To prevent excessive increase of Gaussian quantity due to deformation error, we raised this threshold Edensify from 0.0002 used in previous works (Kerbl et al., 2023; Huang al., 2024c) to 0.001.
B ADDITIONAL DISCUSSIONS
We present a comprehensive analysis of ArtSS and DTA through additional quantitative and qualita- tive results.
Visibility Problem Our results uncover intriguing inconsistency in DTA's performance across different states of the same object. As illustrated in Tab. 1. DTA demonstrates good reconstruction quality in the high-visibilits state but shows markedly poor performance in the low-visil www-viiiillity state. This limitation is particularly pronounced for objects with prismatic joints, such as real storage and blade. In these cases, DTA struggles accurately capture the geometry and articulation of partially
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Table A.I: Quantitative evaluation of each state on PARIS data, We report the average of metrics over 10 trials of each state. "metric-O/1" represents the metric evaluated state 0/1 and "metric-m" is the average of two states. We highlight best results on average of two states. Axis Pos. is omitted for prismatie joints (Blade, Storage, and Real Storage).
Synthetic Objects
Real Objects
Metric
Method
FoldChair
Fridge
Laptop
Oven
Scissor
Stupler
USB
Washer
Blade
Storage
All
Fridge
Storage
All
DTA-D
0.03
0.09
0.07
0.22
0.10
0.06
0.11
0.36
0.20
0.07
0.13
2.08
13.64
7.86
Oun-0
0.01
0.03
0.01
0.01
0.05
0.01
0.04
0.02
0.03
0.01
0.02
2.09
3.47
2.78
Axis Ang
DTA-1
0.44
0.10
0.07
0.23
0.10
0.07
0.11
0.36
0.26
0.09
0.14
2.07
8.08
5.08
Ous-1
0.01
0.03
0.01
0.01
0.05
0.01
0.01
0.02
0.03
0.01
0.02
2.09
3.47
2.78
DTA-m
0.04
0.10
0.07
0.22
0.10
0.06
0.11
0.36
0.23
0.08
0.14
2.088
10.86
6.47
Our--m
0.01
0.03
0.01
0.05
0.01
0.02
0.03
0.01
0.02
3.47
2.78
DTA-0
0.01
0.01
0.01
0.01
0.03
0.02
0.00
0.04
0.02
0.59
0.59
Oun-U
0.00
0.00
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.47
0:47
Axis
DTA-I
0.01
0.01
0.01
0.02
0.02
0.05
0.02
0.59
Ousi1 I
0.00
0.00
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.47
0.47
DTA-m
0.01
0.01
0.01
0.01
0.03
0.02
0.00
0.01
0.02
0.59
0.59
Ous-m
0.00
0.00
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.47
0.47
DTA-0
0.10
0.12
0.11
0.12
0.38
0.08
0.15
0.28
0.00
0.00
0.13
1.85
0.14
1.00
Oun-0
0.03
0.04
0.02
0.02
0.04
0.01
0.03
0.03
0.00
0.00
0.02
1.94
0.01
0.99
Part
0.37
0.28
0.97
Motion
Oun-1 I
0.03
0.01
0.02
0.02
0.01
0.01
0.03
0.03
0.00
0.00
0.02
1.91
0.01
0.99
DTA-m
0.09
8.12
0.11
0.12
0.38
0.088
0.15
0.28
8:00
0.00
0.13
1.85
0.12
0.99
Our--m
0.03
0.04
0.02
0.02
0.04
0.01
0.03
0.03
0.00
0.00
0.02
0.04
0.99
DIA-0
0.18
0.62
0.32
4.60
3.30
2.68
2.52
4.77
0.55
4.71
2.41
2.36
10.98
6.67
Oun-U
0.26
0.52
0.59
3.88
0.62
3.85
2.25
6.41
0.54
7.47
2.64
1.64
2.93
2.29
DTT--1
0.19
0.63
0:30
4.58
3.55
2.91
2.90
4.56
0.45
2.50
2.59
9.60
6.10
CD&s
Ousi I
0.26
0.48
0.63
4.00
0.61
3.83
2.56
6.43
0.54
7.31
2.67
2.01
4.02
3.02
DTA-m
0.19
0.62
0.31
4.59
3.43
2.79
2.61
4.66
0.50
4.80
2.46
2,48
10.29
6.39
Ous-m
0.26
0.50
0.61
3,94
0.61
3.84
2.41
6.42
0.54
7.39
2.65
1.82
3.48
2.65
DTA-0
0.15
0.27
0.16
0.44
17.38
2.34
1.47
0.37
2.05
0.36
2.50
1.12
30.78
15.95
Oun-0
0.54
0.21
0.14
0.89
0.65
0.88
1.22
1.51
1.12
1.03
0.82
0.56
6.28
3:17
CD-m
0.30
10:11
0.45
365,
183,80
Oun-1
0.12
0.21
0.13
0.76
0.64
0.52
1.43
0.15
1.01
1.02
0.63
1.31
87.81
41.56
DMTA-m
0.15
0.44
13.75
1.73
0.44
31,72
5.05
1.48
198.26
99.88
Ours-m
0.33
0.21
0.14
0.82
0.65
0.70
1,33
1,00
1,06
0.73
0.99
47,05
24.02
DTA-0
0.77
0.70
0.35
4.21
0:42
2.13
1.17
4.59
0.36
1.09
1.83
2.08
8.98
5.53
OunsU
0.43
0.58
0.47
3.58
0.69
3.13
1.28
6.12
0.61
5.13
2.20
1.29
3.23
2.26
DTA--
0.26
0.70
0.32
4.27
0.41
1.92
1.52
4.48
0.38
3.99
1.83
2.19
9.03
5.61
CD-w
Ouss1
0.30
0.59
0.50
3.71
0.67
2,63
1.87
5.99
0.65
5.21
2.21
1.45
2.45
1.95
DTA-m
0.26
0.70
0.34
4.25
0.41
2,02
1.34
4.53
0.37
4.04
1.83
2.13
9:01
5.57
Ours-m
0.36
0.59
0.48
3.64
0.68
6.05
2.84
2.11
occluded parts. The observed inconsistency and state-dependent performance fluctuations underscore the necessity for more robust approach that effectively connects and leverages information from mul- tiple states. This precisely where ArtGS's strengths become evident. By establishing connections between different articulation states, ArtSS achieves more consistent and high-quality reconstructions across varying object configurations. Jointly optimizing over multiple states allows ArtGS to: I) Leverage complementary information from different articulation states, 2) Maintain consistency in part assignment and geometry across states, 3) Better handle occlusions and low-visibility scenarios by inferring occluded geometries from other states. These capabilities enable ArtGS produce more accurate and reliable reconstructions, particularly in challenging scenarios. The superior perfor- mance of ArtSS demonstrates its potential for robust articulated object reconstruction in real-world applications.
Significance of Part Assignment Through analysis of both qualitative (Fig. A.3) and quantitative (Tab. 3) results, we have identified that the model's ultimate performance primarily determined by the accuracy of part assignment. When the model fails to correctly divide an object into parts, it becomes impossible to obtain reasonable joint parameter estimation. Conversely, even when joint parameter estimation is inaccurate, the model may still correctly separate the object's parts. This insight reveals that accurate part assignment is a crucial prerequisite for high-quality articulated object reconstruction. Our findings emphasize that to enhance the reconstruction of articulated objects, the ability to reasonably separate parts is of paramount importance. ArtGS addresses this challenge through the center-based segmentation and improved initialization by clustering. These techniques work in synergy to significantly improve the part segmentation capabilities of ArtGS. By enhancing the model's ability to correctly identify and separate object parts, we lay a solid foundation for
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Table A.2: Quantitative evaluation of Axis Pos metric on PARIS, Metrics reported as 1 std 10 trials average of 2 states. We report the value timed by 1000 and highlight the hest results.
Metric
Method
FoldChair
Fridge
Laptop
Oven
Scissor
Stapler
USB
Washer
All
Axis Pos
DTA
0.53gaa
0.62ga3
1.10ga7
1.49210
2.48
2.21
0.35
4.53g21
1.66-12
Ours
0.48
0.44202
0.39
0.55
0.16291
0.93.44
0.08
0.33
0.42_a3
subsequent stages the reconstruction process, including joint parameter estimation and final mesh reconstruction.
C LIMITATIONS
Stability of Randommess. ArtS exhibits enhanced robustness and stability across different random seeds, primarily due to our innovative initialization strategy for canonical Gaussians and our part assignment module. We observe that stability issues often stem from the initialization of three key components: canonical Gaussians 9o part centers C in the part assignment module, and joint articulation parameters V. As demonstrated in Sec. 5.3, faulty initialization of g and can lead to significant performance degradation, particularly for complex objects with multiple movable parts. While our current initialization strategy has greatly improved stability, severe initialization errors in center C' may still result in part mis-segmentation. We can integrate prior models such as SAM (Kirillov et al., 2023) to enhance the ability to correct center initialization errors. Although ArtGS works with randomly initialized V, we have observed that improved initialization of y brings enhanced performance. Future work could explore the integration of heuristic algorithms or feed-forward articulation estimation models to provide better initial estimation for V.
Limited States Our current approach is limited to modeling articulated objects using only two states, which may not fully capture the complexity of real-world multi-part objects. Moreover, as the number of parts increases, distinguishing parts with similar joint axes and motion patterns (such as parallel drawers) becomes increasingly challenging, complicating the segmentation process. To address this, main avenues could be explored for future research: 1) Multi-state Extension: Develop a methodology to extend ArtS to handle multiple states that interact with different parts, potentially by identifying movable parts with sequential state update mechanism. This would involve iteratively updating the model as new state information becomes available, allowing for more comprehensive representation of the object's articulation space. 2) Continuous Temporal Reconstruction: Adapt ArtGS to reconstruct articulated objects from monocular video sequences. This approach would leverage temporal information infer a continuous range of articulation states, providing more nuanced understanding of the object's movement capabilities.
Mesh Reconstruction Fidelity Our current implementation utilizes the original Gaussian Splatting technique, which, while effective, has limitations in terms of mesh reconstruction quality compared with NeRF-based methods( (Wang et al., 2021; Yariv et al., 2021; Wen et al., 2023). Integrating recent advancements in reconstruction with Gaussian Splatting (Huang et al., 2024a; Chen al., 2024a) may help to improve the reconstruction fidelity of ArtGS.
D ADDITIONAL EXPERIMENTS
D.1 ADDITIONAL QUANTITATIVE COMPARISONS
We provide additional comparisons with previous methods in this section.
Scaled Axis Pos Metric. Following DTA and PARIS, we multiply the 'Axis Pos' metric by 10 in Tab. and Tab. A.I. While this metric shows minimal variation among current methods for synthetic objects, we also report the Axis Pos metrie multiplied by 1000. As shown in Tab. A.2. ArtGS demonstrates superior performance compared to DTA.
Perception-basee Metrics. To evaluate rendering quality, we assess perception- metrics including LPPPS Zhang et al. (2018), SSIM Wang et al. (2004), and PSNR, with results shown
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Table A.3: Quantitative evaluation for perception-basee based metrics PARIS data. We report the results on
average of two states. We highlight best
results.
Synihetic Objects
Real Objects
Metric
Mathed
FoldChair
Fridge
Lapeop
Oven
Scissor
Stupler
USB
Wasber
Blade
Storage
Fridge
Storage
All
31.50
37.87
38.37
38.49
40.08
36.18
26.21
Ouns
34,46
37.11
34.09
37.06
38.29
39.15
39.64
38.50
41.16
37.24
37.67
27.05
25.38
26.23
SSIM
PARIS
0.83
0.994
0.991
0,980
0.996
0.995
0.992
0.991
0.995
0.993
0.991
0.898
0.953
0.926
0.997
0.983
0.988
0.995
0.998
0,999
0,998
0.995
0,999
0.992
0.995
0,939
0,930
0.935
LPIPS
PARIS
0.045
0,032
0.020
0,045
0.015
0.019
0.029
0.029
0.017
0.096
0.035
0.188
0.139
0.164
0.097
Table A.4: Quantitative comparison for whole mesh reconstruction on PARIS data. We report the average of CD-w over 10 trials. We bold best results average of two states.
Metric
Methed
Synthetic Objects
Real Objects
FoldChiir
Fridge
Laptop
Oven
Scisaur
Supper
USD
Washer
Blade
Storage
All
Fridge
Storage
All
DTA
0.26
0.70
0.34
4,25
2002
1.34
4.53
0.37
4,84
188
2.13
9.01
5.57
CD-w
TSDF with ga deydi
0.30
0.56
0.47
3.60
0.49
2.78
1,60
5.73
0.54
5.33
2.32
3.15
151.86
67.51
Ours
0.36
0.59
0.48
3,64
288
1.58
6.05
0.63
5.17
221
1.37
2/34
2.11
in Tab. A.3. While our primary focus aligns with previous methods mesh reconstruction and articulation estimation, AnGS achieves comparable or superior performance relative to PARIS.
L Limited Improvement for CD-w on Simple Synthetic Objects. Our method's performance on simple synthetic objects, particularly in terms of CD-w metric, is constrained by our use of TSDF for mesh extraction from Gaussian Splatting-rendere depths. To analyze this limitation, we compare against meshes reconstructed using ground-trute depth with TSDF As shown in Tab. A.4, even with ground-truth depth input, TSDF-based reconstruction cannot surpass algorithms using marching cubes with NeRF, primarily due to the fundamental differences between TSDF and marching cubes algorithms on simple geometries. However, for complex or real-world objects where articulation re- construction becomes more critical, the advantages of our model become evident. Additionally, TSDF with ground truth depth on real-world objects may produce poor-quality meshes (e.g., real_ due to depth sensor noise, while our ArtGS achieves high-quality reconstruction. Importantly, pri- mary objective is to create digital twins of real-world articulated objects, where ArGSS demonstrates significant performance improvements, particularly for complex and real-world scenarios.
D.2 FAILURE CASES
Incorrect Initialization of Part Centers. For real-world objects with multiple parts, clustering- derived part centers may be inaccurate (Fig. A.1 (a)) due to sensor noise, occlusion, and varying illumination conditions. These incorrectly initialized centers often persist through optimization, degrading performance for parts with misaligned centers (Fig. A.1 (c)). Manual correction of erroneous part centers prior to training (Fig. A.1 (b)) yields improved results (Fig. A.I (d)). As discussed in Appendix C, incorporating prior models like SAM for automatic, accurate part center initialization remains promising direction for future work.
Similar Motions. Our method exhibits limitations when handling parts with identical motion across states, demonstrated in case 2 of Fig. A.I where two drawers are pulled with the same distance. In such scenarios, the model tends to learn a single joint to fit both parts, failing to distinguish between the independently movable parts. As discussed in Appendix C, expanding ArtGS to incorporate additional states would provide richer motion information, potentially enabling better part separation.
D.3 EVOLUTION OF CANONICAL GAUSSIANS
We visualize the evolution of canonical Gaussians in Fig. A.2, showing both their part assignments and centers. Our initialization strategy begins with dense static Gaussians and sparse dynamic Gaussians. As training progresses, the Gaussians undergo densification while simultaneously refining their part centers and assignments. These visualization results demonstrate the effectiveness of ArtGS.
D.4 ADDITIONAL QUALITATIVE COMPARISONS
We provide additional qualitative comparisons different datasets in the following pages.
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Case
Input Image
(3) Init. Cano.
(b) M Init. Cano.
(c) Opt. Cano.
(d) M- Opt. Cano.
Inpul Image
(c) Inil. Cano.
(1 Opt. Cano.
Figure A.1: Failure cases. We illustrate failure cases of our ArtGS. 'Init/Opt. Cano.' represents initialized and optimized Canonical Gaussians, while the prefix 'M' indicates manual correction of erroneous part centers.
Int.
Cama
Oye. Cato
Figure A.2: Evolution of canonical Gaussians. We visualize the evolution of canonical Gaussians, showing both their part assignments and centers. initialization strategy begins with dense static Gaussians sparse dynamic Gaussians. As training progresses, Gaussians undergo densification while simultancously refining their part centers and assignments. These visualization results demonstrate the effectiveness of ArtGS.
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DTA
Ours
GT
Table-25493
State
State
Storage-45503
State
State
Oven-101918
State
State
Figure A.3: Additional qualitative results on ArtGS-Multi.
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=
T 0.25
T= 0.5
T= 0.75
T=1 1
PARIS
Ours
PARIS
Ours
PARIS
Ours
PARIS
Figure A.4: Interpolation results on PARIS data.
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Reviews:
[{"review_id": "gy74Fr7vIZ", "reviewer": "Reviewer_JGzH", "summary": "This paper introduces a part-aware articulated object reconstruction method with 3D Gaussian splatting. The method reconstructs the shape and articulation from two states of multi-view RGB-D images with a coarse-to-fine Gaussian initialization. A skinning-related center-based part modeling is designed to automatically assign Gaussians to statics and movable parts. The articulation type is learned through self-guided parameter learning.", "questions": "1. Have you tried to present the results when different movable parts are in different states?  Since this method models multiple movable parts with distinct part centers and dual quaternions, I believe it is possible to do that. It would be interesting to see those results.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-organized and the overall writing is good.\n\n2. The optimization speed is improved by using 3D Gaussian splitting.\n\n3. Center-based part modeling and assignment allow the model to assign Gaussian to different movable and static parts. The use of part centers allows multi-movable parts compared to PARIS and does not require prior part segmentation knowledge compared to DTA. \n\n4. The self-guided motion priors with part dual-quaternions improve articulation learning with a further heuristic joint type prediction.\n\n5. The experiments and ablation studies are extensive.", "weaknesses": "1. Only state 0 and state 1 results are presented, the authors should present more intermediate states like Figure 8 in PARIS where they illustrated results on t=0.25, t=0.5,  and t=0.75.", "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": "UkglC7M59K", "reviewer": "Reviewer_HLUC", "summary": "This paper presents ArtGS, an innovative method for reconstructing multi-part interactive objects. ArtGS utilizes 3D Gaussian Splatting combined with a coarse-to-fine standard Gaussian initialization strategy to achieve high-precision reconstruction of dynamic scenes.", "questions": "Please refer to the weaknesses section for further questions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The key highlight of the paper is the innovative introduction of 3D Gaussian Splatting (GS) and superpoints, along with a coarse-to-fine Gaussian initialization strategy. Additionally, the method achieves state-of-the-art results on existing datasets, particularly excelling on a newly introduced dataset.", "weaknesses": "The final visual renderings demonstrated in the paper primarily focus on cabinet-like objects, lacking validation on other categories. To better assess the generalizability of the method, I suggest adding renderings for a broader range of objects, such as chairs, scissors, and staplers, which have distinct motion components and structural differences. This would provide a more comprehensive evaluation of the method's applicability and accuracy across diverse objects.\n\nMoreover, I have some doubts regarding the main contributions and novelty of this work. The success of this method may largely derive from elements such as the coarse-to-fine training strategy, joint optimization of static shape and articulation, the use of Gaussians instead of NeRF, and super-part rigging—all of which have been explored in previous research but are integrated here for this specific task. I would appreciate if the authors could further clarify their unique contributions.\n\nLastly, there is a lack of video examples to visually demonstrate the interaction of dynamic parts. Including video results would significantly help in understanding the method's advantages in complex dynamic scenarios.", "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": "QTd9aHsh41", "reviewer": "Reviewer_AX3x", "summary": "- This paper works on the task of articulated object reconstruction from multi-view RGB-D images.\n- This work proposes to leverage 3D Gaussian as the underline representation to reconstruct articulated objects with multiple parts in a self-supervised way.\n- Along with the representation, several strategies are proposed to improve the reconstruction accuracy, such as a coarse-to-fine initialization for part geometry and skinning-inspired dynamics modeling for articulation estimation.\n- Experiments demonstrate the effectiveness and robustness of the proposed method.", "questions": "The below are some details of the approach or implementation that would benefit from clarification.\n\n-\tIn Section 4.1:\n\t-\tOnce $ \\mathcal{G}_{init}^c$ is initialized, are the Gaussians labeled as static/dynamics in binary or part ids?\n\t-\tSince the refinement of $ \\mathcal{G}_{coarse}^c$ only involves the static part, would the region of the movable part be very sparse? Does this sparsity remain for the optimization step later? Would it affect the reconstruction quality in the end?\n\t-\tHow many Gaussians are initialized in $ \\mathcal{G}_{init}^c$?\n-\tIn Section 4.2:\n\t-\tWhat are the learnable centers $C_k$ useful for by intuition? What is $\\mathbf{R}_k$ in $C_k$? Is $\\mathbf{R}_k$ related to the articulation for the specific part?\n\t-\tIn equation 6, the variables $X_i^k$,  $\\mu_i^c$, and $\\mathbf{W}_\\Delta$ are not defined.\n\t-\tIn lines 256-257, it is unclear how the distance matrix is related to part assignment. It is also unclear why identifying the sharp boundaries is the unique challenge in this case.\n\t-\tIn lines 258-260, why introducing $\\mathbf{W}_\\Delta$ is helpful in tackle the challenge?\n-\tIn Section 4.3:\n\t-\tIn equation 7, $M_{ik}$ is undefined.\n\t-\tIn equation 8, $\\mathbf{r}_i^c$ is undefined.\n\nThe below are some missing references for citation.\n- [1] Yan, Zihao, et al. \"RPM-Net: recurrent prediction of motion and parts from point cloud.\" arXiv preprint arXiv:2006.14865 (2020).\n- [2] Jain, Ajinkya, et al. \"Screwnet: Category-independent articulation model estimation from depth images using screw theory.\" 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.\n- [3] Lei, Jiahui, et al. \"Nap: Neural 3d articulated object prior.\" Advances in Neural Information Processing Systems 36 (2023): 31878-31894.\n- [4] Liu, Jiayi, Manolis Savva, and Ali Mahdavi-Amiri. \"Survey on Modeling of Human-made Articulated Objects.\" arXiv preprint arXiv:2403.14937 (2024).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "-\tThis work is contributing to an increasingly important research problem of building digital twins for arbitrary articulated objects with complex structure.\n-\tThe experiments are showing promising results overall.\n-\tThe observation and analysis of the experiment results are well discussed.\n-\tThis work is well contextualized in the first two sections.", "weaknesses": "-\t**The writing of the Method section should be improved**. The quality of the writing overall is great, but the method section requires further improvement to articulate the details of the approach, especially for the logic of the presentation and usage of mathematical symbols. The current form hinders the understanding of the proposed method. Please see more details for clarification in the `Question` section.\n-\t**The experiment evaluation needs a bit more care**. \n\t-\tThere are at least 17 highlighting of numbers in `Table 1` is misplaced, e.g., PARIS*[Axis Ang, Real Objects-Fridge] in the second row should be the second best.\n\t-\tThe “Axis Pos” reported in `Table 5` for synthetic objects is almost no difference given the limited significant figures. It would be more meaningful to show the metrics with more significant figures or report the numbers by timing 10 or even 100. \n-\t**More qualitative results**. The presentation of the experiment would benefit from showing more qualitative results (in different complexity) that compares with the baseline methods, which can visually demonstrate the gap revealed in the metric evaluation.\n-\t**More discussion of the failure cases and limitations**. For the completeness of the paper, the failure cases and discussion of the limitation accordingly should be incorporated. There are assumptions that are not explicitly mentioned in the paper, e.g., the number of parts is given, the two states of the observation are pre-aligned in the world/object coordinate system. Maybe there are other assumptions that can be discussed, e.g., whether the articulated parts are required to be opened to expose visibility as much as possible.", "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": "LLWKXEt9ty", "reviewer": "Reviewer_x9y2", "summary": "This paper presents a novel method for interactable articulated object modeling. The method leverages the skinning-like motion priors between object parts and the correspondences between two-state objects to model part-aware articulated objects. Results show that the results outperform existing works in both well-used benchmarks and datasets created by the author containing complex multi-part objects.", "questions": "- What is the comparison baseline for w/o Cano. init? Does it work if we just take one-state GS as initialization?  I think it should be ok as two-state is only w.r.t. some partial rigid transformations. \n- Can you explain the gap between obvious improvement in articulation parameters estimation and minor improvement in overall, geometry modeling?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "+ The writing of the paper is easy to follow. \n+ The motivation of the paper is reasonable. By combining explicit 3D Gaussian modeling with proper articulation modeling, the method enables better articulation parameter estimation and object reconstruction.\n+ The design for initialization and part segmentation based on different states and object parts is reasonable. \n+ Extensive experiments are conducted to evaluate the effectiveness of the method presented.", "weaknesses": "- The method is designed for two-state objects. It is uncertain how this can be applied to object captures with more states. Moreover, the number of parts is set manually as hyperparameters making it difficult to scale to different objects.\n- It seems the method combines existing designs to achieve 3DGS-based articulated modeling. Though it is not super interesting, the overall pipeline makes sense. However, I am not sure what is the argument for using 3DGS compared to NeRF and MVS point clouds.  \n- The authors only compare the method with DTA for visualization and with DTA and PARIS on DETA-Multi and ArtGS-Multi. Is it possible to compare with more methods? For example, some NeRF-based methods. Some related suggestions: \n [1] Real2Code: Reconstruct Articulated Objects via Code Generation; [2] Articulated Object Neural Radiance Field; \n- More visualization results are essential for understanding the method to complement Fig. 4. For example, how well does the matching process and refinement work? I think these visualizations are vital for multi-stage methods. \n- I am not sure why perception-based metrics are not reported for evaluations, e.g., PNSR and LPIPS for 3DGS evaluation. \n- Though the paper is motivated by interactive articulation objects, I did not see related evaluation and applications for it. \n- I did not see discuss about failure cases and limitations.", "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)