Generalizable Hand-Object Modeling from Monocular RGB Images via 3D Gaussians
Authors: Xingyu Liu
Pengfei Ren
Qi Qi
Haifeng Sun
Zirui Zhuang
Jing Wang
Jianxin Liao
Jingyu Wang
Venue: NeurIPS 2025 poster ()
Keywords: Hand-object reconstruction
3D Gaussian Splatting
Primary area: applications

Abstract: Recent advances in hand-object interaction modeling have employed implicit representations, such as Signed Distance Functions (SDF) and Neural Radiance Fields (NeRF) to reconstruct hands and objects with arbitrary topology and photo-realistic detail. However, these methods often rely on dense 3D surface annotations, or are tailored to short clips constrained in motion trajectories and scene contexts, limiting their generalization to diverse environments and movement patterns. In this work, we present HOGS, an adaptively perceptive 3D Gaussian Splatting (3DGS) framework for generalizable hand-object modeling from unconstrained monocular RGB images. By integrating photometric cues from the visual modality with the physically grounded structure of 3D Gaussians, HOGS disentangles inherent geometry from transient lighting and motion-induced appearance changes. This endows hand-object assets with the ability to generalize to unseen environments and dynamic motion patterns. Experiments on two challenging datasets demonstrate that HOGS outperforms state-of-the-art methods in monocular hand-object reconstruction and photo-realistic rendering.

Generalizable Hand-Object Modeling from Monocular RGB Images via 3D Gaussians
Xingyu Liu", Pengfei Ren", Qi Qi, Haifeng Sun, Zirui Zhuang, Jing Wang, Jianxin Liao, Jingyu Wang"
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (liuxingyu, rpf, qiqi8266, hfsun, zhuangzirui, wangjing, liaojx, wangjingyu] @bupt.edu.cn
Abstract
Recent advances hand-object interaction modeling have employed implicit rep- resentations, such Signed Distance Functions (SDF) and Neural Radiance Fields (NeRF) reconstruct hands and objects with arbitrary topology and photo-realistic detail. However, these methods often rely dense 3D surface annotations, are tailored to short clips constrained motion trajectories and scene contexts, limiting their generalization to diverse environments and movement patterns. In this work, we present HOGS, an adaptively perceptive 3D Gaussian Splatting (3DGS) framework for generalizable hand-object modeling from unconstrained monocular RGB images. By integrating photometric cues from the visual modality with the physically grounded structure of 3D Gaussians, HOGS disentangles inher- ent geometry from transient lighting and motion induced appearance changes. This endows hand-object assets with the ability generalize to unseen environments and dynamic motion patterns. Experiments on two challenging datasets demon- strate that HOGS outperforms state-of-the-art methods in monocular hand-object reconstruction and photo-realistic rendering.
Introduction
Fine aine-grained hand-object modeling is crucial for immersive AR/VR applications. Existing methods largely rely dense 3D annotations pre-scanned object models [16] 35
47 48 [5] [4]. which incur high labeling costs and limit scalability. Leveraging the ubiquity and accessibility of monocular RGB images reconstruct interactions, by contrast, offers more practical avenue for seamless integration into consumer-grade grade AR/VR ecosystems. Recent works [50] 7 achieve hand-object reconstruction from short RGB clips but remain restricted fixed environments and limited motion trajectories, requiring scene-specific optimization and retraining for new conditions. We present cross-scene and cross-motion generalizable paradigm for photo-realistic hand-object modeling from monocular RGB images, reducing novel-scene setup from hours to seconds without post-training adaptation.
Recent studies have extensively explored implicit representations, such as signed distance functions (SDF) and Neural Radiance Fields (NeRF) [29]. to advance fine-grained hand-object modeling.
SDF-based methods (20 27 densely optimize the zero-level set of a spatially continuous signed-distance field, capture high-density hand-object surface with arbitrary topology. Neural implicit approaches
[12
continuously encode scenes as volume density and view-dependent
Equal contribution.
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Poor shading textures
3D annotation free
Cross seeesseee///tiionn
Calegory-agnosik
Now-adaptiie modeling
(a) Previous implicit methods
Method
Appearunce changes
Adaptive perception
Generalized modeling
6DoF-hased
14 15
100
SDF-based 2 3 35
x
Clip-based 7 50 37
HOGS
Gaussians
Table 1: Comparison of representative hand-object modeling methods across key desiderata. Our method is both category- agnostic and 31) surface annotation- while achieving strong generalization across environments and motions.
(b) HOGS
Figure/ 1: HOGS overcomes the transient appearance changes of hand-objects through the perceptual capa- bilities of neural networks, to achieve photo-realistic hand-object modeling with motion and scene adaptation.
radiance, showing the feasibility of recovering photo-realistic hand-objects under temporally and spatially constrained conditions (e.g., from short video clips).
Despite these advances, existing SDF-based methods heavily rely on dense 3D surface supervision. while acquiring such precise annotations is typically impractical and cost-prohibitive in the wild. Additionally. previous neural implicit approaches suffer from two fundamental limitations. First, models trained on single short video sequences capture appearance and motion biases, as the visual conditions and motion patterns within such limited data are highly constrained; second, they neglect explicit perception of physical effects like hand-object contact and transient illumination phenomena. Persistent occlusions, dynamic shadows, and specular reflections inherent to real-world interactions violate the simplified assumptions of these methods, resulting in degraded geometry estimates and rendering artifacts, as illustrated in Fig. [ As a result, they struggle to generalize beyond their constrained training clips when exposed to unseen environments complex motion patterns.
To address these challenges, we explore how generalizable priors on appearance variation and motion dynamics can be learned from large-scale monocular RGB images, promoting hand-object representations that generalize across diverse environments and motion patterns. While implicit radiance fields achieve high photometric fidelity, they model scenes as undifferentiated volumes, lacking physically grounded structure for contact-level reasoning. Motivated by the need for both learnability and physical awareness, we adopt 3D Gaussian Splatting (3DGS) (21), where each Gaussian primitive encodes physically meaningful attributes. We integrate neural perception with this geometric representation to capture the underlying patterns of lighting and surface appearance, and to explicitly reason about hand-object contact and motion. This adaptively perceptive representation learning design endows hand-object assets with robust cross-scenario generalization capabilities.
To this end, we propose Hand-Object Gaussian Splatting (HOGS) for interacting hand-object reconstruction from monocular RGB images, with central focus generalization across diverse environments and motion sequences. Built upon an adaptively perceptive 3DGS-based representation, HOGS models articulated interactions via deformable Gaussian primitives driven by hand poses and object 6-DoF transformations. For generalizable hand-object modeling, we introduce two modules: Vision-driven generalizable Perception Module (V-PM), which disentangles 3D Gaussians into gcometry-invaricn canonical templates and vision-dependent Iearnable components, explicitly decoupling appearance-invariann persistent geometry from transient photometric variations; and Geometry-driven Pose refinement Module (G-PM), which employs a lightweight 3D neural network to extract geometric priors from 3D Gaussian primitives, utilizing the physical awareness to accurately refine hand-object pose and contact. HOGS jointly optimizes hand-object Gaussians and the proposed submodules from monocular images through a unified differentiable rendering pipeline, leveraging 2D photometric supervision without relying on dense 3D surface annotations.
Experimental results on two challenging datasets show that our method outperforms the state-of- the-art (SOTA) methods in monocular hand-object reconstruction and photo-realistic rendering. Furthermore, qualitative results and multimedia supplementary materials highlight its generalization

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across diverse visual conditions and motion patterns. Code is available at httpt::t//giiie com/ rulven/HOGS Our contributions could be summarized as:
We propose hand-object Gaussian splatting framework for interacting hand-object photo-realistee reconstruction from monocular RGB images, without relying on dense 3D surface annotations.
We integrate visual and geometric priors from neural networks into the modeling of hand-object Gaussians, yielding hand-object assets with perceptual generalization across various environments and motions.
Experiments demonstrate that our method significantly improves both reconstruction and rendering performance, showcasing strong generalization capabilities.
2
Related Work
2.1 Interacting Hand-object Reconstruction
Many works have been proposed to understand hand-object interactions [20 2 50
40 48 26
32
with many focus on joint hand-object or hand-held object reconstruction. Early mainstream efforts [14] 15
40 48 assume known object templates and use parametric models (e.g., MANO) with a fixed resolution, reducing mesh reconstruction to pose estimation. To achieve oategory-agnoste reconstruction, Hasson et al. [16] use AtlasNet [10] to deform object vertices from sphere. Karunratanakul et al. [20] introduced SDF-based implicit fields for fine-grained
47
hand-object reconstruction. AlignSDF 3 further integrates the strengths of parametric models and
SDF by encoding pose priors into the implicit field. Chen et al. [2] use kinematic and temporal features to guide SDF-based 3D reconstruction. However, these methods rely accurate and dense 3D annotations. With the development of geometric volume rendering techniques, several 2D photometric supervise hand-object reconstruction methods have been proposed. MOHO [52] leverages occlusion-aware synthetic pre-training to pursue hand-held object reconstruction from a single-view image. Some recent methods 7 37) attempt exploit temporal coherence in monocular videos as a proxy for multi-view supervision. However, natural interaction sequences often exhibit persistent occlusions and invariant lighting patterns between the hand and object. These intrinsic limitations lead to a domain gap between observed motion patterns and unconstrained monocular scenarios. In consequence, prior methods struggle bridge such a gap across scenarios with significant appearance- motion discrepancies, failing to robustly model interactions under varying motion and unseen scenarios.
2.2 Human-centric Photo-realistic Modeling
With the advancements in neural implicit and geometric representations, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), significant achievements have been made in 3D
scene reconstruction and novel view synthesis. Recently, some studies have explored adapting these representations to the photo-realistic rendering of dynamic human bodies and hands. Several works 19 36) leverage pose information derived from parametric models like SMPL (8 and MANO (38 to drive textured representations neural fields for modeling dynamic humans or hands from multi-view inputs. HumanNeRF [44] further relaxes the requirements for multi-view inputs by adopting simpler monocular setting, and proposes decoupling the motion field into skeletal rigid and non-rigid components. Subsequent studies [8) focus on achieving significantly faster training speeds. More recently, some works have introduced 3DGS for animatable human modeling from monocular inputs. To capture fine details such as clothing and hair, HUGS [22) allows 3D Gaussians deviate from the human body model. 3DGS-Avatar [37] proposes pose-dependent rigid and non-rigid deformations to handle highly articulated and out-of-distributio poses of clothed humans. GauHuman [17 refines human pose and Linear Blend Skinning (LBS) weights and employs human priors with KL divergence measure for adaptive density control of 3D Gaussians. iHuman 33 binds 3D Gaussians to the human body surface for explicit normal rasterization, and optimization with normal supervision, to achieve 3D reconstruction and photo-realistic rendering. However, photo-realistic reconstruction of closely interacting and mutually occluding hands and objects from unconstrained monocular images remains challenging and underexplored.
[45 32
12
3

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Vision-driven Generalizable Perception
Image Backbone
M
öp
P
Multimedal Pretrained Fncoder
ds
S
Pixel-aligned Visual Feature
pq
q
Gaussian Rasterizer
F(T(D))
7
Haionddepennent Components
Input Image
Sab
Goometry-invanee Templates
(G) (Grr Fol
Pose Deform
Pose Refine
(G.)
1G.1
(G.
Initialization
Geometry-driven Pose Refinement
Rendering & Reconstruction
Figure 2: Our framework. We initialize deformable 3D Gaussians using hand articulations and object 6-DoF poses. The Vision-driven generalizable Perception Module (V-PM) disentangles Gaussians into geometry-invariant templates and lisoonddependent components via extracted visual features, while the Geometry-drine driven Pose refinement Module (G-PM) extracts primitive-level 3D geometric features refine hand-object poses and contacts. These modules are co-optimized in a differentiable rendering pipeline, jointly reconstructing photo-realistic geometry and appearance under cross-scene motion and illumination variations without per-sequence fine-tuning.
Method
Given diverse collection of monocular RGB hand-object images across varied motions and scene context, HOGS jointly optimizes deformable 3D Gaussian primitives for each hand and each object, while learning generalizable interaction priors to refine hand-object modeling. During inference unseen sequences involving novel interactions and appearances, HOGS can adaptively modulate the trained hand-object assets in purely feed-forward manner, without extra fine-tuning.
Fig. illustrates the overview of our framework. We use pose-deformable 3D Gaussians to model hand-object interaction from monocular images across multiple sequences (Sec. B.1). To enable generalizable hand-object Gaussian modeling, we introduce a Vision-driven generalizable Perception Module (V-PM), to dynamically adapt 3D Gaussians to visual scene variations (Sec. 3.2). well as a Geometry-drive Pose refinement Module (G-PM), extracting Gaussian primitive-level geometric features to enhance robustness against inaccurate hand-object motion estimation (Sec. 3.3) Details of optimization and mesh extraction are provided in the Appendix.
3.1 Hand-Object Interaction Modeling
We represent hand and object in the canonical coordinate space through a set of 3D Gaussian primitives } and {..). and their positions are initialized by random sampling the canonical MANO surface and within the 3D bounding box, respectively. Each 3D Gaussian G is defined by its center p E IR$ covariance matrix Σ E R333 opacity and view-dependent color c. Its geometry is parameterized as
(xx) = exp (-1 (x (X- 'x p))p (x-)))
where the covariance matrix Σ = RSSTRR is represented by a scaling matrix E R333 and rotation matrix R 12333 which are represented by the diagonal vector R3 and quaternion vector q E 4 in practice. During the rendering process, 3D Gaussians are projected onto the camera view. Given viewing transformation W and the Jacobian matrix of the projective transformation J, the covariance matrix in camera coordinates is computed as: E' = JWEWTJJ JT. To account for the mutual occlusion relationships between the hand and the object during rasterization, their Gaussians are jointly accumulated via alpha blending to compute the pixel color C::
= Σ c;a: II
aa))
(2)
N.
j=1
4

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where Nuo is the depth-ordered set of hand and object Gaussians overlapping the pixel.
To adapt the motion of the hand and object,
pose-driven approach to transform the canonical Gaussian 9 to the toxe-transformed Gaussian G. in the observation space. For the hand branch, similar to prior methods [77, we apply the forward Linear Blend Skinning (LBS) to transform the center position p and rotation matrix R of each 3D Gaussian:
Pr w:(p) Bi P.
Σ
(3)
R2
Σ W/ (p) B,
R.
(4)
1:3.1.3
where Pa and R2 are the center and rotation matrix of the transformed 3D Gaussian, {Bt the bone transformations derived from the hand poses 0, and {wy (w))p))==,.... are the learnable skinning weights at position p. Similarly, for the object branch, we apply a 6D rigid transformation based on the object rotation and translation to transform the canonical Gaussian to the posed Gaussian. In practice, we use an off-the-shelf regressor 35 to initialize the hand pose and object 6-DoF pose.
3.2 Vision-driven Generalizable Perception
Across multiple motion sequences, variations in the visual environment result in changes in the appearance of the hand and object, such as alterations in lighting intensity and direction, transient lighting patterns induced by surface materials and occlusions, well as motion-induced blur and tex- ture deformation. To enable the 3D Gaussian model to generalize across various visual environments in unconstrained monocular scenarios, we decouple the parameters of the vanilla Gaussian primitives into gcometry-invarian templates and vision-dependent components.
Take color с as an example, the vanilla 3DGS uses Spherical Harmonics (SH) coefficients for view-relevant appearance modeling, which is insufficient for capturing the aforementioned visual variations across scenes. To address this, we first employ Vision Transformer [6] as the image backbone to extract visual features F from the hand-object region, and obtain pixel-aligned visual features (Px)) using bilinear sampling based on the 2D projection location т (P:) of each pose- transformed 3D Gaussian position Pt. We use an MLP to encode the visual features and Gaussian parameters into a vision-dependent appearance component Z,;
Z ((F(())))
where the 3D coordinates of each Gaussian within G are represented using a multi-resolution hash grid encoding (00. Then, we compute the vision-dependent color component in an adaptive manner. Specifically, we use an MLP network e to obtain a color weight vector a from the vision-dependent appearance component Zu and the precomputed color C.a. derived from the SH coefficients. The weight a quantifies the importance of different visual features in determining the final appearance, which can be formulated as:
a = Sigmoid((Eh (Ze, can)),
The final color e of each 3D Gaussian is obtained by weighting the SH color and the visual component:
c (1 - a) Cah + a Sigmoid(Z.))
(7)
For the remaining 3D Gaussian parameters, we follow similar process, but in simpler manner:
(öp, ös, σq) = (F, F(m(pp)).
(8)
p' p óp.
(9)
s' cxp(os),
(10)
q' q [1, 8q1, 592, ōqs]:
(11)
where 6p, ds, and åq are the vision-dependent components of Gaussian center, scale, and rotation, respectively, and p'. s', and q' are the corresponding updated Gaussian parameters.
5

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3.3 Geometry-driven Pose Refinement
Sequence-specin reconstruction methods independently apply structure- seree-ffrommoiioio (SfM) each video for initial motion estimation, while per-frame cues are used to refine poses. This per-sequence design tightly couples the pipeline to the specific trajectories and scene layouts of each sequence. As result, the learned motion and contact patterns are sequence-specific and do not generalize well to unseen interactions or motions beyond the observed trajectories.
To address monocular images with previously unseen motion patterns, we leverage the rich geometric and physical properties of 3D Gaussians, which encode physically meaningful spatial and volumetric information and provide explicit geometric cues for guiding motion and interaction reasoning. Consequently, we employ lightweight 3D neural network to integrate this information, achieving precise hand-object pose refinement and contact reasoning.
Firstly, capture the geometric structure information of 3D Gaussians, straightforward approach is to treat the Gaussian primitives as a 3D point cloud and extract geometric features using point cloud networks. However, may exhibit lower efficiency when handling non-geometric attributes like color. To address this limitation, we adopt ULIP [6]. a multi-modal pre-trained model, to improve the comprehensive understanding of 3D Gaussians by effectively integrating both geometric and visual information. Specifically, we employ PointNet++ (14), pre-trained by ULIP, as the 3D backbone to encode the pose-transformed Gaussian centers P. and Gaussian parameters into the subsampled Gaussian centers p E V and 3D Gaussian feature matrix Fg E N= This process enables efficient feature extraction and downsampling while maintaining the structural and physical properties of the 3D Gaussian representation. Then, following [14], we employ a Transformer encoder containing self-attention modules [41] and 3-layer MLP to obtain the hand-relative object translation offset ÀT, E TR and object rotation offset A RR, E R333
Additionally, we employ a contact and penetration loss [16] to optimize interactions between the hand and object. We treat the centers of hand and object Gaussians as hand-object point clouds, and following [6). apply distance- penetration and collision losses between them to enforce physically plausible contact and prevent interpenetration. Leveraging these geometric attributes of hand-object 3D Gaussians, our approach relaxes the need for ground-truth object templates or
detailed 3D vertex annotations required by existing contact optimization methods [16 49 20), while optionally utilizing 6D pose annotations. Details of training losses are provided in the Appendix.
Experiment
4.1 Datasets
DexYCB is a hand-object dataset containing 582K RGB-D frames over 1,000 sequences of 10 subjects grasping 20 different objects. We follow the dataset split in [35], filtering samples without interactions, obtaining 147,526 training samples. For reconstruction evaluation, follow [2 27
to downsample the video data to 6 frames per second, resulting in 5,928 testing samples.
HO3D_V v3 1 is an RGB-D hand-object interaction dataset with 10 subjects manipulating 10 objects from the YCB dataset. Following the evaluation protocol of [7], we select 18 sequences for training and evaluate the quality of reconstructed hand-held object meshes.
4.2 Baselines
We conduct comparisons with existing hand-object and hand-held object reconstruction methods, including 3D dense supervised baselines (typically SDF-based) and 2D photometric supervised baselines (e.g.. MOHO [52] and HOLD (7). Additionally, we re-implement 3DGS-Avatar [37] and GOF [5]] which originally utilize 3DGS to model animable human bodies and reconstruct static unbounded scenes, respectively, extending them to a hand-object baseline and a rigid object baseline (marked by i). In particular, we adapt 3DGS-Avatar by replacing SMPL with MANO for the hand branch and substituting pose-dependent deformations (e.g., LBS) with rigid transformations for the object branch.

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Method
CDp
Fy @11
FA F5t
CDof
Fo
@
3D Supervised Methods:
Hasson et al. [16]
0.537
0.115
0.647
1.94
0.383
0,642
Grasping Field 20
0.364
0.154
0.764
2.06
0.392
0,660
A 3
0.358
0.162
0.767
1.83
0.410
0.679
gSDF 2
0.302
0.177
0.801
1.55
0.437
0.709
HORT
0.630
0.850
2D Supervised Methods:
MOHO 12
0.600
0,810
GOFF 51
0.68 0.24
0.610
0.834
Ours
0.481
0.133
0.732
0.785
0.918
Table 2: Quantitative results of hand-object reconstruction on Dex YCB. Video data is downsampled frames per second.
Method
CD,
F, @ 101
CD Doosse
Method
PSNR
SSIM1
LPIPS
iHOI 49
3.8
75.8
41.7
GOFI [51
29.58
0.9686
31.69
DifTHOI [50]
4.3
68.8
43.8
3DGS-Avatar 37
29.71
0.9690
30.52
HOLD 7
0.4
96.5
11.3
Ours
31.12
0.9728
26.83
Ours
0.7
96.2
2.7
Table 4: Quantitative results of hand-object photo- realistic rendering on YCB.
Table 3: Quantitative results of hand-held object reconstruction on HO3D.
4.3 Implementation Details
We initialize the hand Gaussians and object Gaussians by randomly sampling K 5,000 points within the canonical MANO surface and the 3D bounding box, respectively. For optimization, we follow [21] employ cloning or splitting and pruning to adaptively control the density of the 3D Gaussians during optimization. We employ an individual set of Gaussians for each subject or object. For the input of the visual encoder. we crop the hand-object region of the RGB image and resize it to 224x224. We use an Adam W optimizer (24) for training. On Dex YCB, we train the model for total of 360k iterations, which takes approximately 10 hours on NVIDIA RTX 4090 GPU. On HO3D, we train for 200k iterations. After 360k iterations on DexYCB, we fix the parameters of the 3D Gaussians and continue training for an additional 10 epochs, focusing solely on optimizing the pose parameters to prevent underfitting.
4.4 Metrics
Geometric Metries. For geometric evaluation of the object, we follow to report the Chamfer distance (CD.) in cm2 after ICP alignment with rotation, translation, and scaling, Chamfer distance incorporating 6-DoF pose (CDpose). as well as the F-score evaluated at thresholds of 5mm and 10mm (F, @5 and F, @ 10). For hand reconstruction, at mm and 5mm thresholds (F, @1 and F; @5), In addition, to evaluate hand-held object pose performance, report Object Center Error (OCE), Mean Corner Error (MCE), and the standard pose estimation metric average closest point distance (ADD-S) following 335
report the Chamfer distance (CD,) and F-score
Rendering Metrics. For evaluation of hand-object rhato-realistic rendering, we report the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [4], and learned perceptual image patch similarity (LPPIPS) 53 Only the region within the hand-object mask is considered. Note that LPIPS values in all tables are scaled up by 1000.
4.5 Comparisons with State-of-the-arts
Surface Reconstruction. To validate the quality of hand-object modeling, we compare with hand- object and hand-held object reconstruction SOTAs on the Dex dataset. As shown in Table our method significantly improves object reconstruction quality over SOTA methods while achieving
7

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Method
OCE, ADD-SL
Method
CJ PSNR
SSIMT
LPIPS!
AlignSDF
3
27.0
3DGS-Avater
37
x 29.71
0.9690
30,52
Wang et al.
42
27.3
32.6
15.9
/ 29.48 J0.23 0.9689 10.0010
32.02
Lin al.
25
39.8
45.7
31.9
X
29.57
0.9687
31.75
gSDF
12
19.1
Ours (w/o V-PM)
/ 29.32 10:22
10.25
0.9685
10.0002
33.44
HOISDF
35
18.4
27.4
13.3
x
31.12
0.9728
26.83
Ours
18.1
26.1
12.0
Ours
31.00 0..77 27.57
Table 5: Hand-held object pose esti- mation results on DexYCB
Table 6: Ablation study for the impact of color jittering DexYCB. CJ represents Color jitter.
ID
Method
PSNR
SSIM
LPIPS!
OCE!
MCE,
ADD-S1
0
Full model
31.12
0.9728
26.83
17.83
25.95
11.87
1
w/o contact optimization
31.09
0.9727
26.93
18.10
26.13
11.97
2
w/o pre-trained 3D backbone
31.09
0.9726
26.96
18.11
26.40
11.88
3
w/o G-PM
31.17
0.9721
27.39
18.45
27.37
13.31
4
w/o adaptive appearance
29.92
0.9700
29.95
5
w/o V-PM
29.57
0.9687
31.75
Table 7: Ablation study on DexYCB. Both rendering quality and pose results are shown.
comparable hand reconstruction performance to previous 3D dense supervised methods. The hand- held reconstruction performance comparison with SOTA methods on HO3D shown Table Our method achieves pose-independent reconstruction quality (CD, and F, @ 10) comparable to SOTA methods without requiring per-sequence optimization, while significantly improving pose-dependent reconstruction accuracy (CD pose), showcasing enhanced generalization capability across diverse hand-object motion. Furthermore, we conduct hand-held object pose estimation comparisons Table 5 and our method achieves superior object pose accuracy, validating the effectiveness of the proposed geometry-drine pose refinement strategy. Fig. is the qualitative results. Our method produces more plausible object shapes compared to gSDF [2]. avoiding collapses and deformations. In contrast to GOF [51), our method can better recover local geometric structures and mitigate mesh holes and incompleteness, demonstrating superior Gaussian modeling quality.
Photo-realistic Rendering. We perform a comparative analysis photo-realistic rendering of hand objects on YCB. As shown in Table 4 our method outperforms baselines on all the metrics by a large margin. Qualitative comparisons are shown in Fig. 3, Compared to 3DGS-Avale our method achieves superior color and texture fidelity and enhanced hand-object pose accuracy. Notably, ours demonstrates robust capabilities in both modeling hand-object shadow patterns (column 2) and adapting to chromatic variations caused by material reflectance under dynamic illumination conditions (columns 3-4).
(37),
Ablation Study
Impact of visual environment changes. To further validate the generalization capability of HOGS against visual environment variations, we simulate appearance alterations induced by diverse illumi- nation patterns on the Dex YCB dataset and compare the rendering quality impacts between HOGS and baseline methods. Specifically, to each RGB image, which is used for photometric supervision and rendering evaluation. We conduct comparative analyses on rendering quality under color jittering conditions across three configurations: our full model, our model without V-PM, and 3DGS-AA -DGSt-vatar, As demonstrated in Table [ while color jittering minimally affects structural distortion in both 3DGS-AvaS Avatar and ours without V-PM, it significantly degrades PSNR and LPIPS metrics due to pixel color errors and image perceptual discrepancies. contrast, our approach incorporates visual generalization awareness substantially mitigate rendering quality degradation, demonstrating effective adaptation to visual environment
apply a random color jittering within the range [-0.3, 0.3]

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3DGS-AVA -OS..Avaaa
Weo V-PM
wio G-PM
Ours
GT
Figure 3: Qualitative results of hand-object photo- rendering on Dex YCB.
ID
F
F(T(p.)
G
PSNR
SSIM
LPIPS
0
29.57
0.9687
31.75
1
30.49
0.9703
29.66
2
V
30.52
0.9704
29.50
3
31.08
0.9725
27.31
4
31.12
0.9728
26.83
Table Ablation of the vision-driven components on Dex YCB. represents global visual features, (p:( represents pixel-aligned visual features, and G represents 3DGS parameters.
changes. Furthermore, Fig- 5 inttuiiively shows that our method can adaptively capture diverse global color tones for rendering, exhibiting remarkable robustness to visual appearance variations.
Ablation on generalizable modules. We perform ablation studies to verify the effectiveness of two proposed critical components, V-PM and G-PM. As shown in Table 7 our full model demonstrates superior performance compared to configurations excluding hand-object contact optimization (ID 1) or multi-modal pre-trained 3D backbone integration (ID 2). The proposed G-PM yields significant enhancements in pose results (ID 0 vs. ID 3). Notably, although G-PM does not directly optimize 3D Gaussians, the hand-object pose refinement improves rendering quality, particularly SSIM-measured structural fidelity and LPIPS-based perceptual quality. Additionally, the removal of either the adaptive appearance modeling component (ID 4) or the V-PM (ID 5) results in significant degradation of rendering quality. The qualitative ablation is shown in Fig. 3] The V-PM can capture hand-object lighting and shadow patterns (columns 2-5), while the G-PM effectively alleviates hand-object penetration artifacts (column 1) and refines imprecise object pose (columns 6-7).
Ablation the vision-driven components. To assess the impact of different features in the vision- driven component of 3DGS, we conduct an ablation study on DexYCB using global visual features F. -xiined visual features ((T(pe)). and 3DGS parameters G. As shown Table discarding the vision-driven component altogether (ID 0) results in the lowest rendering quality. Incorporating any single feature improves performance, and the improvement particularly notable when fine-grained pixel-aligned visual features are included. The best results are achieved when all three features are used (ID 4), indicating that global context, pixel-level alignment, and 3DGS parameters provide complementary information that jointly enhances reconstruction fidelity.

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Input
gSDF
GOF
Ours
3DGS-Avatar
w/o V-PM
Ours
GT
Figure 4: Qualitative results of hand-held object reconstruction on Dex YCB.
Figure 5: Qualitative ablation for the impact of color jittering on hand-object rendering quality.
5
Conclusion
In this paper, we propose a 3DGS-based interacting hand-object modeling framework from uncon- strained monocular RGB images. The key insight lies in leveraging neural networks to endow 3D Gaussians with appearance and motion adaptability, guided by generalizable visual and geometric per- ceptual cues. Extensive experiments demonstrate the effectiveness of HOGS in achieving fine-graines interacting hand-object modeling, significantly advancing monocular hand-object reconstruction and photo-realistic rendering performance.
Limitation. While HOGS exhibits strong cross-scene and cross-motion generalization, its perfor- mance is constrained by the diversity of object categories encountered during training. Consequently, focuses on hand-object assets that generalize to unseen visual contexts and motion patterns, rather than synthesizing entirely new object categories. A promising direction for future work is large- scale training on extensive hand-object datasets, enabling zero-shot generalization to novel object categories.
Acknowledgments
This work was supported part by the National Natural Science Foundation of China under Grants (62406039, 62471055, U23B2001, 62321001, 62101064, 62171057, 62071067), the High- Quality Development Project of the MITT2240STCZB2544 the Ministry of Education and China Mobile Joint Fund (MCM20200202. MCM201 80101), BUPT Excellent Ph.D. Students Founda- tion (CX20241014), the Project funded by China Postdoctoral Science Foundation (2023TQ0039, 2024M750257, GZC20230320), the Fundamental Research Funds for the Central Universities (2024PTB-002) the 2025 Education and Teaching Reform Project Funding at Beijing University of Posts and Telecommunications (2025Y2005).
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Appendix
In Appendix contents, we provide:
Details of mesh extraction (Section A
Optimization of hand-object Gaussians (Section B
Discussion of mesh alignment (Section C
More qualitative results (Section D)
Video supplementary materials are provided as separate files.
A
Mesh Extraction
Inspired by the impressive novel view synthesis capabilities of 3D Gaussian Splatting recent studies [1] have focused on exploring the use 3D Gaussian representations for surface reconstruction. To extract explicit meshes from hand-object Gaussians, extend Gaussian Opacity Fields (GOF), a JDGS-based surface reconstruction method tailored for unbounded scenes, into an interacting hand-object mesh reconstruction framework.
A.1 Preliminary: Gaussian Opacity Fields
GOF uses an explicit ray-Gaussian intersection instead of projection, which allows evaluating the opacity value transmittance of any 3D point x along the ray. At the most basic level, given single 3D Gaussian Gk. the opacity at any point along a ray can be defined as:
(g)P() (())
if
(12)
if
where x = + fr. Intuitively, the opacity increases until it reaches its maximal value, and remains constant thereafter. Next, considering set of 3D Gaussians G, the opacity of point x is given by:
O = Σ On (,,,
II II - (1-a,,,(G,,o.r..))) j=1
(13)
As 3D point might be visible by any training view, the vanilla GOF defines the opacity field O(x) of 3D point æ as the minimal opacity value among all training views or viewing directions:
O min r,
(14)
(o.r)
A.2 Dynamic Hand-Object Reconstruction
However, for dynamic hand-object reconstruction, directly employing GOF faces two problems: (1) We observe the interacting hand-object from single viewing direction, rather than scanning various views in an unbounded scene, making it impossible to rely on multiple input view directions to model the opacity field; (2) Unlike static unbounded scenes, the hand-object motions cause instability in the position and rotation of the 3D Gaussians, resulting drastic changes in the local geometry. To address these issues, first generate inward-oriented rays that converge toward the geometric core of the hand-object, enabling systematic surface interrogation. Specifically, we uniformly sample K 16 points {ox) from the surface of a bounding sphere closely encapsulating the hand-object. For each sampled point Og, a directed ray I'g parametrized as:
C
O2
r = O +
t20
(15)
[c og
Next, we adopt a smoother manner to integrate the opacity of each 3D point from different views. Specifically, we enforce hard opacity constraints while maintaining smoothness in visible regions. The conditional branch serves two critical purposes: (1) Physical Constraint. Zero opacity is strictly enforced when any viewing ray confirms full transparency, preventing phantom geometry artifacts;
14

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(2) View Consensus. The mean operation in non transspaaprrnt regions smooths out inconsistencies from sparse view sampling. The final opacity field can be defined as:
if if (ok, rk) E S2 O Fk. =
O(x)
(16)
O(ok: Tk, t) (ok:FK)ER
Σ
otherwise
Finally, following [5T]. we use the center and corners of 3D bounding boxes around the 3D Gaussian primitives as vertex sets for the tetrahedral mesh, and utilize the Marching Tetrahedra algorithm [39 for triangle mesh extraction upon assessing the opacity at tetrahedral points.
B
Optimization
B.1 Pixel Color Loss
Beyond joint rendering for interaction modeling, we adopt independent color supervision for hands and objects, to address the color bleeding issue close interaction regions where traditional unified rendering fails to disentangle component-specifie appearances:
i-1
C=
Σ C,a, iENm
II a3) j=1
Chand
Σ iENo
c
a
(1 (1- az);
ci =
ca
E Nuand otherwise
(17)
0
Cotj =
Σ caa V
II 1=1
i-1
az),
cf d=
c;
i e Nabi otherwise
0
where Nh. DepthSort(Nnunt U. J.Nats)) ensures correct occlusion handling across components. The pixel-level RGB loss can be formulated as:
Cgb |CC C + Chaadd
Cagt hand ll + ||Cobj
C
(18)
B.2 Mask Loss
To facilitate geometry supervision, we compute the opacity value by accumulating the alpha values, performed separately for band, object, and jointly hand-object:
0
Σ a
a.)
iNN
Opand
Σ
ai
Sa: 0
i Nhand otherwise
(19)
i.N
Oarj = Σ
(1-aj),
af
as
E Nobo
0
otherwise
NEN
==1
The mask loss measures the LI distance between the rendered opacity and the corresponding ground truth mask values:
Lmack
10 M|] + [[O
Mhand | + ||Oobj
Maall1
(20)
B.3
Perceptual Loss
To enhance fegeffreyeency detail preservation and mitigate blurring artifacts synthesized inter- actions, extend the independent supervision paradigm to perceptual feature space, optimizing
15

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Method
CD,4
F. @51
F, @ 101
Hasson et al. [16
1.94
0.383
0.642
Grasping Field (20)
2.06
0.392
0.660
AlignSDF 3
1.83
0,410
0.679
gSDF 2
1.55
0.437
0.709
Ours (not rotation-aligned)
1.51
0.497
0.730
Ours
0.24
0.785
0.918
Table Comparison object reconstruction on DexYCB.
LPIPS as the perceptual loss with AlexNet 23 as the backbone. Following the RGB loss struc- ture, we employ LPIPS metric for decomposed components. The final perceptual loss aggregates multi-component measurements:
per LPIPS (R(C), ((C())
LPIPS (R(Chand)) R(Cht ((han)))
(21)
LPIPS (Coo))) ((C
where R(-) denotes the rendering function from alpha-composited colors to RGB images.
B.4
Pose Loss
To ensure physically plausible hand-object interactions, supervise the 6D pose parameters (rotation R SO(3), translation E RR3) and the corner positions Pe of manipulated objects through the
1 loss:
C SmoothL1 Rs)
Luans SmoothL1 (t, t
(22)
Comme SmoothL1 (Pc, Pei
The overall pose loss Spoce is then computed the weighted sum of these components:
Cpose Anothme + Arans + Acomer
(23)
where the weight factors Avo Auans and Agemer are set as 10, 1c4, and 1e3, respectively. our framework, the ground truth object poses are optional. For example, in comparison on the HO3D dataset, since HOLD 7 does use any pose annotations, our method does access the ground truth poses in this experiment either.
B.5 Overall Loss
In addition to the aforementioned loss functions, we further introduce several regularization terms to enhance the robustness and physical plausibility our framework. Specifically, we follow [7 to incorporate Skinning Loss Lakin for regularizing the forward skinning network. To preserve local geometric consistency during deformation, we employ an aa-isometric-as-possihe constraint, which consists of Lisa-pos and Liso-cuv restrict neighboring 3D Gaussian centers and covariance matrices, ensuring they maintain similar distances after deformation. Furthermore, we treat the centers of hand and object Gaussians as hand-object point clouds, and following (16), apply distance-based penetration loss Lpen and contact loss Lcont between them to enforce physically plausible contact and prevent interpenetration. The overall loss be formulated as:
Lional Legh + Amark Emask AperchCper + Apose
+ Avont + AponCpen + Askin
(24)
+ Aiso-poa Ciso-pos + Aiso-cov Cis--oov
where \rgh, Amasks Aperes Apocr Aconts Apens Aakins diso pass and ams are set to 1, 0.1, 0.01, 20, 10, 0. 1, and 100, respectively.
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3DGS-Avatar
Ours
GT
3DGS-Avatar
Ours
GT
Figure 6: Qualitative results on YCB dataset.
C Discussion of Mesh Alignment
reconstruct object meshes within unit bounding box using non-uniform scaling and evaluate pose-independent reconstruction via ICP with translation and scaling. In contrast, 6-DoF pose-driven methods [7] use ICP with translation, scaling, and rotation for alignment. Table 9 shows that method outperforms SDF-based approaches under both alignment strategies.
Existing SDF-based methods 3 20
D Qualitative Results
We present more qualitative comparisons of hand-object rendering on Dex Fig. 6 rendering results of our method show more delicate colors and more robust hand-object poses, and can adapt the light and shadow patterns between hand and object.
The
E Societal Impacts
This paper proposes ine-grained hand-object interaction modeling framework from monocular RGB images, with the positive impact broadening access to immersive technologies in AR/VR, HCI, and robotics applications. A possible negative impact may arise from privacy and fairness concerns due to implicit data collection and dataset bias, necessitating cautious and transparent deployment in real-world applications.
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NeurIPS Paper Checklist
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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state three contributions: (1) a hand-object Gaussian splatting framework for interacting hand-object photo-realistic reconstruction from unconstrained RGB images, without relying on dense 3D surface annotations, (2) visual and geometric perceptual generalization mechanisms yielding implicit representations generalizable across various scenes and motions, and (3) SOTA reconstruction and rendering performance on two datasets.
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It fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
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Reviews:
[{"review_id": "9PSsMK59k3", "reviewer": "Reviewer_iQZL", "summary": "The paper proposes a method, HOGS, for generalizable 3D hand-object reconstruction, based on 3D appearance and  pose priors learned from 2D hand-object interaction images. The method aims to disentangle base geometry from transient lighting and motion-induced appearance changes, improving generalization across diverse environments and motions. The framework includes two learnable modules, V-PM a transformer-based image encoder which disentangles appearance-invariant geometry from learnable transient variations and G-PM a 3D NN-based module used for object pose refinement. Experimental results show that HOGS outperforms state-of-the-art methods in monocular hand-object reconstruction and photo-realistic rendering.", "questions": "My questions are also included in the weaknesses section of this review. I would be glad to get a more detailed explanation of the training and inference process by the authors.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The paper proposes a method from scene and motion generalizable 3D hand-object reconstruction from images based on 3DGS representation and demonstrates improved performance compared to SOTA.\n\n- The paper includes ablations that help understand certain aspects of the framework.\n\n- Once trained the modules do not require per-scene fine-tuning, reducing the required time for 3D reconstruction in novel environments. However, some parts of their training and inference are unclear as mentioned below.\n\nWeaknesses:\n- The design of V-PM is not clearly motivated or explained in the paper. How is transient attribute disentanglement assured by the design of Zv? Is the simple parameter combination method utilized, (addition or multiplication) of optimized parameters and their inferred offsets enough to disentangle attributes? Also, a spherical harmonics representation is still is optimized for color, how are illumination and motion related color variations split between optimized and inferred parameters? \n\n- The training process has not been made clear to me after reading the paper. Based on the authors words, HOGS jointly optimizes deformable 3D Gaussian primitives for each hand and each object, while learning generalizable interaction priors to refine hand-object modeling. How does this process work as it requires per-scene/image optimization of 3DGS parameters and cross-scene learning of the generalizable modules? \n\n- The inference part of the method is also not well explained as far as I can understand. \nAccording to the authors, HOGS adapts to new scenes without the need for per-scene fine-tuning. Does this refer only to the two generalizable modules or all parts of the model? If so, how are the base 3DGS attributes inferred for the hands and objects? Is 3DGS optimization employed for inference, also utilizing V-PM and  G-PM for improved generalization and disentanglement? This is not well explained in the paper.\n\n- G-MP updates the object poses only, while the hand poses are initialized by a 3D hand pose estimation method and left frozen, this is also visible is Figure 3 where the hand poses do not change regardless if G-PM is used or not, while the hand poses are not detailed compared to the GT. This can be problematic when the hand pose estimation produces inaccurate results.\n\n- The authors introduce many losses in the supplementary material for which they do not offer ablation studies of a discussion of their relative effect. \n\n- Experiments miss comparison with a recent 3D hand-object reconstruction method EasyHOI [*], which uses Large Reconstruction Models and hand-object optimization to reconstruct hand-object interactions.\n\n[*] Liu Y, Long X, Yang Z, Liu Y, Habermann M, Theobalt C, Ma Y, Wang W. EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild. In Proceedings of the Computer Vision and Pattern Recognition Conference 2025 (pp. 7037-7047).", "was_revised": true, "final_justification": "After reading the authors' rebuttal (including the provided experimental results and explanations), as well as the discussions with other reviewers, my initial concerns have been covered and thus, I raise my rating to Borderline Accept.", "rebuttal": "We thank the reviewer for recognizing the novelty, performance improvements, and the advantage of not requiring per-scene fine-tuning. Below, we provide detailed clarifications to address the concerns regarding the training and inference procedures.\n\n**Clarification of V-PM**  \nThe V-PM module is designed to disentangle transient, context-dependent visual effects (e.g., lighting variations, occlusions) from stable, geometry-related attributes. It achieves this via inferred offsets predicted from ViT-based visual features, which inject rich contextual cues into the parameter space. Rather than simple additive noise, these offsets enable conditional modulation of Gaussian attributes based on visual modality, promoting disentanglement.  \nTake color modeling as an example, SH coefficients handle angular reflectance but fail to capture transient effects like global illumination, cast shadows, or motion-induced highlights. To address this, we introduce a visual-dependent color component, adaptively blended with SH via a learnable gate. Table 7 (ID 4) demonstrates the render quality improvement with this adaptive appearance modeling; Figure 3 shows the visual-driven modulation captures fine-grained transient effects (e.g., hand-induced shadows, dynamic reflections) that static baselines cannot reproduce.\n\n**Details of the Training and Inference Process**  \nHOGS is trained end-to-end on a large collection of monocular interaction sequences that include mixed objects, motions, and environments. Training data indeed covers multiple objects, but focuses more on variation in interaction and scene context. Specifically, on DexYCB, we sample 147,526 monocular images from over 1,000 sequences covering 10 subjects and 20 objects. \nThrough end-to-end training on diverse sequences with varying motions and environments, HOGS learns scene modulation capabilities. In this way, when presented with unseen sequences, HOGS can adaptively modulate the hand-object assets from monocular images in a purely feed-forward manner, without per-sequence optimization.\n\nFor the inference procedure, we clarify that HOGS is intended to ***generalize to unseen scenes and motions rather than to synthesize entirely new object categories***. Accordingly, during inference, HOGS takes monocular images from unseen sequences with novel motions and viewpoints, and directly produces scene‑adapted outputs in a purely feed‑forward manner, without any fine‑tuning.\n\n**Clarification of Pose Refinement**  \nIn G-PM, we focus solely on refining the object pose, as it critically impacts reconstruction quality. Hand poses are not updated within G-PM; instead, hand articulation is represented using a learnable LBS weight during hand Gaussian modeling, which avoids the complexity of jointly optimizing both hand and object poses.\n\n---\n\n**Ablation for Loss Functions**\n\nWe appreciate the reviewer’s suggestion to further analyze the loss terms. In response, we conducted ablation studies on the mask loss, perceptual loss, and contact/penetration losses. The results show that removing any of these losses leads to performance degradation, particularly in terms of structural and perceptual quality metrics. We will include the complete results in the final version.\n\n| Methods                            | PSNR↑  | SSIM↑  | LPIPS↓  |\n|----------------------------------|--------|--------|---------|\n| w/o $\\mathcal{L}_{mask}$          | **31.13**  | 0.9720 | 27.51   |\n| w/o $\\mathcal{L}_{perc}$          | 31.12  | 0.9720 | 27.48   |\n| w/o $\\mathcal{L}_{cont+pen}$  | 31.09  | 0.9727 | 26.93   |\n| Full model                  | 31.12 | **0.9728** | **26.83** |\n\n---\n\n**Comparison with More Recent Methods**\n\nEasyHOI is a zero-shot hand-held mesh reconstruction method. It leverages the generative capability of LRMs to model arbitrary object categories. However, in this paper, we focus more on the complexity of scenes during interactions, involving irregular object orientations, frequent motion changes, and dynamic lighting effects. Such scene complexity poses significant challenges for accurate mesh generation in zero-shot models. In contrast, HOGS is explicitly trained on diverse interaction sequences, enabling hand-object assets to generalize across varied motions and environments. This fundamental difference leads to a significant performance gap, as shown below:\n\n| Methods   | CD↓    | F@5↑   | F@10↑  |\n|-----------|--------|--------|--------|\n| EasyHOI   | 1.628  | 0.134  | 0.253  |\n| Ours      | **0.085** | **0.700** | **0.909** |\n\n> *We follow the evaluation protocol of EasyHOI and test on the same 500 images from DexYCB. We report Chamfer Distance and F‑score with 5 mm and 10 mm thresholds.*\n\nSince a fair comparison is NOT applicable between these two settings, we omitted EasyHOI in the main paper. We appreciate the reviewer’s suggestion to include more recent methods. ***To this end, we additionally report a comparison with the latest 3D-supervised SOTA monocular hand-held object reconstruction approach, HORT (concurrent work, ICCV 2025 accepted)*** [1], where our method still demonstrates superior object reconstruction performance on DexYCB.\n\n| Methods | F@5↑ | F@10↑ |\n|---------|-------|-------|\n| gSDF    | 0.44  | 0.71  |\n| HORT    | 0.63  | 0.85  |\n| Ours    | **0.79**  | **0.92**  |\n> *Results of HORT are cited from its original paper. Chamfer Distance is omitted due to inconsistent computation protocols.*\n---\n\n**References**\n\n[1] Chen Z. et al., \"HORT: Monocular Hand-held Objects Reconstruction with Transformers,\" *ICCV*, 2025."}, {"review_id": "lZ4i8yAoKo", "reviewer": "Reviewer_9ss3", "summary": "This paper proposes HOGS, a 3D Gaussian Splatting (3DGS)-based framework for generalizable hand-object reconstruction from unconstrained monocular RGB images. While there are recent implicit function-based methods proposed for hand-object reconstruction, they rely on 3D surface annotations or limited to short clip inputs constrained in motions and scenes, with requiring scene-specific optimization that limits generalizability. To address this limitation, it integrates a Vision-driven generalizable Perception Module (V-PM), and Geometry-driven Pose refinement Module (G-PM) that are directly trained to infer visual and geometry-driven Gaussian Splat parameters, without per-sequence fine-tuning to achieve better generalizability. Experiments show that HOGS achieves competitive performance in monocular hand-object reconstruction and photorealistic rendering.", "questions": "See the Weaknesses section above.", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "[Strengths]\n\n1. Good paper structure\n\nThe paper is well structured, and the teaser and method figures (Figures 1–2) are clearly presented.\n\n2. Good research direction\n\nI agree that the overall research direction—to develop a generalizable hand-object reconstruction method without requiring per-sequence training—is important for achieving higher generalizability and improved inference speed. However, I have some concerns regarding the current performance of the method.\n\n[Weaknesses]\n\n1. Empirical performance of the proposed method\n\nIn the main comparison results (Tables 2–3), the proposed method appears to perform suboptimally compared to the baselines. I am concerned that the statement \"HOGS outperforms state-of-the-art methods in monocular hand-object reconstruction and photo-realistic rendering\" (Lines 14–15) may be overstated. Additionally, the qualitative results—particularly the rendering results in Figures 3 and 5—appear very suboptimal, and in the video results, the proposed method looks worse than HOLD. While the method demonstrates better generalizability (e.g., smaller performance drop in color-modulated scenes, Table 6), I believe the main performance should still be comparable to existing state-of-the-art methods.\n\n2. Choice of baselines\n\nThe latest 3D supervised method baseline considered in Table 2 is gSDF (CVPR 2023). Is there a reason more recent baselines (e.g., G-HOP [R1]) were not included in the comparison?\n\n[R1] Ye et al., G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis, CVPR 2024.\n\n3. Writing quality\n\nThe overall writing quality should be improved to meet the acceptance standard. The paper is generally not easy to read, and several statements either (1) misrepresent existing literature or (2) contain grammatical errors. Examples include:\n\n  - (Lines 1–4, 101–102) The paper repeatedly describes 3DGS as an implicit representation, but it is an explicit representation.\n\n  - (Lines 28–34) The introduction separates the characteristics of SDF-based methods from implicit approaches, even though SDF is a type of implicit representation.\n\n  - (Line 220) \"Implement Details\" should be \"Implementation Details\".\n\n  - (Line 106, minor comment) When referring to SMPL and MANO, citations are needed.", "was_revised": true, "final_justification": "During the author–reviewer discussion, some of my questions were addressed; however, my concerns regarding two of my initially raised weaknesses—**empirical performance of the proposed method** and **writing quality**—remain.\n\nRegarding the **empirical performance of the proposed method**, I am concerned that it does not outperform existing baselines proposed for hand–object reconstruction (upper subtable of Table 2 and Table 3). The justifications provided during the author–reviewer discussion for this suboptimal performance were not very clear and did not align with the initial narrative presented in the main paper.\n\nRelated to these unclear arguments, my most serious concern is the **writing quality**. I find some of the technical arguments presented both in the paper and the rebuttal misleading—for example, repeatedly referring to 3DGS as an implicit representation in the main paper, or discussing *efficiency* and *appearance modeling* aspects in response to my question regarding suboptimal *shape reconstruction accuracy* compared to HOLD.\n\nTherefore, I am leaning towards borderline rejection at this time, to ensure that the paper does not contain misleading technical arguments. Since the proposed method does have strengths (e.g., efficiency), I believe it could be a good fit for a future conference if the technical arguments are refined to be more reasonable and consistent.", "rebuttal": "We thank the reviewer for recognizing the paper’s clear structure and the importance of our research direction. Below, we address the concerns about the current performance.\n\n**Clarification of Empirical Performance**  \nWe appreciate the reviewer’s insightful feedback on both quantitative and qualitative results. **It is important to highlight that our method operates under a substantially different and more challenging setting.** Unlike methods such as HOLD that rely on per-sequence optimization tailored to specific scenes, our model is purely feed-forward during inference, requiring no optimization. This presents a more demanding scenario where the model must generalize across diverse motions and lighting conditions without any scene-specific adaptation. Despite this, we achieve comparable or better generalization than sequence-tailored methods (e.g., Table 3), and significantly outperform other baselines under the same setting (e.g., MOHO), which we believe underscores the effectiveness of our approach.\n\n**Choice of Baselines**  \nOur comparisons focus primarily on recent 2D-supervised methods, such as MOHO (CVPR 2024) and 3DGS-Avatar (CVPR 2024). These methods leverage only 2D annotations (e.g., 2D keypoints, photometric supervision) and share a similar evaluation setting with ours. **Additionally, we compare against the latest 3D-supervised SOTA method, HORT (concurrent work, ICCV 2025 accepted)** [1], and our method, without requiring dense 3D ground-truth supervision, still demonstrates superior object reconstruction performance on DexYCB.\n\n| Methods | F@5↑ | F@10↑ |\n| :---    | :--- | :---  |\n| gSDF    | 0.44 | 0.71  |\n| HORT    | 0.63 | 0.85  |\n| Ours    | **0.79** | **0.92** |\n\nRegarding G-HOP (CVPR 2024), which pre-trains diffusion-based implicit hand-object priors for reconstructing interaction sequences, designed for video-based interaction reconstruction and cannot be directly used as a comparable baseline.\n\n**Writing Quality**  \nWe appreciate the reviewer’s suggestions and will improve the paper’s clarity and accuracy by:  \n1. Revising descriptions of implicit representations (e.g., NeRF and SDF) versus explicit representations (3DGS) for correctness.  \n2. Fixing typographical errors and adding missing citations.\n\n**Reference**  \n[1] Chen Z. et al., \"HORT: Monocular Hand-held Objects Reconstruction with Transformers,\" *ICCV*, 2025."}, {"review_id": "GNtEvCijwh", "reviewer": "Reviewer_fKDv", "summary": "The paper tackles hand-object reconstruction from a single monocular RGB video. It leverages deformable Gaussian epsilloids as the 3D representation. In order to capture the color change over time, it proposes to compute a vision-dependent color component from the pixel-aligned image features and the SH coefficients. Moreover, it leverages a lightweight 3D network to adjust the hand-relative pose offset. Experiments on DexYCB and HO3D demonstrates its effectiveness.", "questions": "Please refer to weaknesses. My main concern is about the statement of generalization ability, which requires addtional experiments to support. The failure to address this might lead to the concern of overclaiming. Despite this concern, I think the method is effective and thus recommend borderline accept as my initial rating.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. It achieves strong performance in object reconstruction, pose estimation, photorealistic rendering on DexYCB.\n2. Using deformable Gaussians for hand-object reconstruction is technically sound. The method to capture color change and refine object pose is simple and effective.\n\nWeaknesses:\n1. Generalization: The paper states that it learns generalizable priors that can be adapted to unseen environements and complex motion. However, the method is trained and tested on the same dataset (DexYCB, HO3D), which cannot support the generalization ability. The scene number and object number in both datasets is somewhat limited. Some objects in the test split are included in the training split.\n2. The symbols and equations require careful revision. Why does the sigmoid function appear in Eq 7? Moreover, alpha in Eq 6 is usually considered as the opacity in Gaussian Splatting, please use another symbol instead.\n3. No inference speed comparison is provided. Inference speed is an important factor for AR/VR applications.", "was_revised": true, "final_justification": "My concern in generalization ability is addressed by the authors, so I raise my rating.", "rebuttal": "We appreciate the reviewer’s recognition of our method’s strong performance and technical soundness. We would like to clarify some misunderstandings regarding the intended scope and generalization of our work.\n\n**Clarification of Generalization Capability**  \nHOGS focuses on generalizing hand-object assets to unseen scenes and motions rather than synthesizing entirely new object categories. Specifically, our goal is to improve over prior methods that are limited to fixed motion trajectories and environments (e.g., only hundreds of frames), by enabling assets to generalize across novel motions and lighting conditions. Thus, during inference, we evaluate on monocular images from novel sequences with unseen viewpoints, environments, and motions, while not on new object categories.\n\n**Symbol and Equation Clarity**  \nWe appreciate the reviewer’s suggestion to clarify the symbols, and will replace *σ* with *sigmoid*(·)  to avoid confusion with the opacity symbol. The sigmoid function in Eq. (7) is used as an activation for the color component, following the design used for other attributes in 3D Gaussians.\n\n\n**Inference Efficiency**  \nHOGS adapts to unseen scenes purely via feed-forward inference without any fine-tuning, achieving a **`rendering speed of 11.6 FPS`**  on NVIDIA 4090 GPU. Explicit mesh reconstruction requires additional post-processing, which takes a few seconds. By contrast, HOLD requires hours of per‑sequence optimization and lacks real‑time rendering."}, {"review_id": "noEpXiSmPC", "reviewer": "Reviewer_bphE", "summary": "This paper presents HOGS, a novel pipeline aims to tackle photorealistic hand-object rendering that are generalizable to unseen motions and environments. The key components of the system are two modules: (i) a vision-driven perception that augments the learning of 3D-GS parameters with per-pixel features encoded by a vision transformer, and (ii) a geometry-driven pose refinement module that further improves hand-object poses via pretrained point cloud encoders. These two designs lead to superior appearance and geometry accuracy over both implicit field based methods and pose estimation methods.", "questions": "1. The ablation study seems to only demonstrate quantitative improvement, can authors show more qualitative improvement regarding (i) the effects of weight quantity over vision dependent component Z for Eq.(7), (ii) the refined poses after geometry-driven pose refinement \n\n2. How is the vision transformer in L165 used? is it pretrained or also learned from the two benchmarks?\n\n3. The authors are also encouraged to clarify on the set-up of input, i.e. how is the cross-scene actually defined for the input? e.g. number of scenes involved and how are they partitioned.\n\n4. Can the authors provide some more clarification on inference time analysis, in particular the time for training the GS model and how is the adaption to unseen scenes with 0.1s achieved in L27?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 2, "originality": 3, "strengths_and_weaknesses": "1. The major concern of the work is its generalizability presented in the current paper, in particular:\n\n(i) Note that the method rely on template-based object pose estimator to obtain the 6DoF object poses, which limits its generalizablity to a few objects only (e.g. those in DexYCB and HO3D) and perhaps are not feasible to test on in-the-wild videos and objects. \n\n(ii) The set-up of the current model is also not clear, in particular how is \"cross-scene, cross-motion\" in L25 achieved? Does the model specifically trained with a mixed of environments only or trained with mixed objects as well?\n\n(iii) The environment change shown in qualitative result is actually limited to different sequences of the same benchmark only. Since HO3D and DexYCB share the same object at completely difference, how is it possible to generalize across benchmarks?\n\n2. The supplementary video also shows limited visual quality, in L40 - L42 the authors mentioned some challenging issues such as dynamic shadow (interaction-dependent) and specular effects (interaction-independent), that must be resolved with an in-depth modeling of hand-object contact. However, I do not see these points being tackled in the video other than fidelity improvement.\n\nOverall I think the current paper is a bit over-claimed, e.g. the work clearly can not handle arbitrary unconstrained RGB images as the tile indicates, and its generalizability is questionable. However, I do find the quality is superior than the baseline methods, so I am willing to increase the score if the authors are able to present more generalization evaluation.", "was_revised": true, "final_justification": "See below discussions.", "rebuttal": "We sincerely appreciate the reviewer’s insightful comments. To facilitate a more accurate interpretation of our work’s scope and contributions, we would like to provide some clarifications that may help address potential misunderstandings.\n\n**Clarification of Our Setting**  \nHOGS focuses on ***hand-object assets generalizing to unseen scenes and motions rather than synthesizing entirely new object categories***. In particular, prior methods, such as HOLD, are typically tailored to fixed motion trajectories and environments, spanning only short clips of a few hundred frames and requiring per‑sequence modeling. In contrast, HOGS generalizes hand‑object assets across novel motions and lighting conditions (e.g., diverse interactions, poses, and dynamic shadows) without any per‑sequence fine‑tuning. During inference, we run on monocular images from unseen sequences with novel viewpoints, environments, and motions, rather than on new object categories. Details follow.\n\n- **Object Pose Estimating Generalization**  \nWhile template-based monocular pose estimators may limit category generalization, this is not a concern in our setting, which focuses on cross-sequence generalization for trained hand and object assets. On the contrary, compared to prior methods that adopt per-sequence motion estimation (e.g., SfM), our monocular pose estimators significantly improve generalization by eliminating the need for sequence-specific optimization.\n\n- **Cross-scene and Cross-motion Generalization**  \nHOGS is trained end-to-end on a large collection of monocular interaction sequences that include mixed objects, motions, and environments. While the training data indeed encompasses multiple objects, it focuses primarily on variations in interaction behaviors and environmental context. Specifically, on DexYCB, we sample 147,526 monocular images from over 1,000 sequences covering 10 subjects and 20 objects. Through such training, HOGS acquires scene modulation capability and, when encountering unseen sequences with novel contacts, poses, and lighting conditions, can adaptively modulate hand-object assets from monocular images in a purely feed-forward manner without per-sequence optimization.\n\n- **Clarification on Evaluation Scope and Object Categories**  \nOur goal is not novel-object synthesis but cross-sequence generalization for trained hand and object assets. At inference time, we evaluate on unseen sequences featuring novel motions and viewpoints. Since our method is not intended for novel-category synthesis, we focus on benchmarks that share YCB object categories rather than those containing entirely new objects.\n\n**Transient Appearance Effects Visualization**  \nAs demonstrated in Figure 3 of the main paper, our visually-driven modulation effectively captures fine-grained transient phenomena such as hand-induced shadows on the can surface (columns 2–4) and dynamic reflections, which static baseline methods fail to reproduce. These effects are also observable in the supplementary video, where the can’s movement induces dynamic, transient surface reflections. Due to the transient nature of these changes, they may be overlooked; we will include more explicit annotations to better showcase them.\n\n**Qualitative Effects of V-PM and G-PM**  \nAs shown in Figures 3 (rows 2–4) and 5, we provide qualitative ablations of V-PM and G-PM: removing the vision-dependent component leads to degraded rendering quality and reduced robustness to color jitter, while removing G-PM affects geometric consistency. However, we only visualized the effect of G-PM on the rendered results. We appreciate the reviewer’s suggestion and will include explicit 6D pose visualizations (e.g., via 3D bounding boxes) in the final version to more clearly illustrate the benefits of geometry-driven pose refinement.\n\n**Details of ViT**  \nWe use a CLIP-pretrained ViT backbone and fine-tune it with an initial learning rate of 1e-6 during HOGS training.\n\n**Input Setup and Cross-Scene Definition**  \nThe input setup and cross-scene definition are detailed above in the ‘Cross-Scene and Cross-Motion Generalization’ subsection within ‘Clarification of Our Setting’. We appreciate the reviewer’s feedback and will incorporate clarifications in the final version to prevent any possible confusion.\n\n**Inference Efficiency**  \nTraining on DexYCB takes approximately 20 hours, as described in Sec. 4.3. '0.1s adaptation' means HOGS adapts to unseen scenes purely via feed-forward inference without fine-tuning, achieving **11.6 FPS rendering** on NVIDIA 4090 GPU. Explicit mesh reconstruction requires additional post-processing, taking seconds. By contrast, HOLD requires hours of per‑sequence optimization and lacks real‑time rendering.\n\n**Clarification on Wording and Claims**  \nWe regret any confusion caused by the term 'unconstrained'. Our intention was to emphasize that, unlike prior sequence-tailored methods requiring per-sequence fine-tuning, our approach generalizes to unseen monocular images in a purely feed-forward manner. We will revise the wording and clarify the setting in the final version to avoid overstating the claims."}]

Author rebuttal:

Meta-review: (a) This introduces two learnable components on top of deformable 3D Gaussians: 1) a Vision‑driven Perception Module that modulates Gaussian attributes with image features to capture transient appearance, and 2) a Geometry‑driven Pose Module to refine object pose. The method targets cross‑sequence generalization and feed‑forward inference (no per‑sequence tuning). Experimental results on DexYCB/HO3D indicate strong object reconstruction metrics and faster inference than optimization‑based pipelines.

(b) + Good inference efficiency by purely feed‑forward inferencing and no per‑sequence optimization.
     + Good Experimental results

(c) - The proposed method seems to rely on template‑based object pose and evaluation on same datasets as training, only can generalize to novel motions/environments, not novel categories, leading to weakens external generalization concerns.

(d) Three reviewers are ≥ borderline‑accept (5,4,4) and one remains borderline‑reject (3). The remaining issues (terminology/writing and the HOLD‑centric accuracy narrative) are addressable in camera‑ready without altering core methodology. Given demonstrated shape metrics under matched 2D‑supervised settings, added comparisons (HORT/EasyHOI), and a strong practical value proposition (feed‑forward, real‑time), the balance tips to poster acceptance.

(e) Three reviewers increased their rating after rebuttal. Authors Clarified scope of “generalization” in the rebuttal. Add more experimental evidence to prove their performance vs SOTAs suggested by reviewers. Generally, most reviewers provide positive feedback. Although 9ss3 remains negative primarily over the accuracy vs HOLD narrative and presentation clarity, AC believe that the writing/terminology issues are camera‑ready‑fixable, and the baseline coverage is improved  and G‑HOP’s exclusion is reasonably justified. For this reason, AC tend to accept this paper as poster.
Decision: Accept (poster)