# GS-Morph: Dynamic Novel View Synthesis via UDF-ARAP Gaussian Splat Morphing

David Stéphane Belemkoabga, Thomas Maugey, Christine Guillemot

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David Stéphane Belemkoabga, Thomas Maugey, Christine Guillemot. GS-Morph: Dynamic Novel View Synthesis via UDF-ARAP Gaussian Splat Morphing. CVMP 2025 - ACM SIGGRAPH European Conference on Visual Media Production, Dec 2025, London, United Kingdom. pp.1-10, 10.1145/3756863.3769701 . hal-05311279

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Distributed under a Creative Commons Attribution 4.0 International License
# GS-Morph: Dynamic Novel View Synthesis via UDF-ARAP
## Gaussian Splat Morphing

David Stéphane Belemkoabga*
INRIA
Rennes, France
bdavidstephane@hotmail.com

Thomas Maugey
INRIA
Rennes, France
thomas.maugey@inria.fr

Christine Guillemot
INRIA
Rennes, France
christine.guillemot@inria.fr

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Novel view synthesis comparison on dynamic human motion. Our method produces sharper renderings than prior approaches like 4DGS, Deformable GS, and SC-GS, and closely matches the ground truth.</figcaption></figure>

## Abstract

Monocular view synthesis in dynamic scenes remains a fundamental challenge in vision and graphics, particularly for applications like augmented reality, virtual production, and free-viewpoint video. Recovering accurate 3D geometry and realistic rendering from a single RGB-D stream is highly ill-posed due to partial, noisy, and temporally inconsistent observations under non-rigid motion. Recent methods, such as dynamic NeRFs and 4D Gaussian Splatting, attempt to jointly optimize motion and geometry. While effective near training trajectories, these entangled designs often struggle to generalize across novel views and times. We introduce a new framework that explicitly decouples geometry reconstruction and motion estimation to improve robustness and generalization. Given a monocular RGB-D sequence with known poses, we first extract per-frame point clouds and estimate frame-to-frame deformation fields using Unsigned Distance Field (UDF) registration with ARAP regularization. These are used to segment the sequence into motion-coherent Groups of Pictures (GoPs). Each GoP undergoes alternating fusion and deformation propagation to yield a consistent local geometry and dense deformation field. GoPs are then hierarchically merged into a global scene model with a unified deformation field. A spatio-temporal 3D Gaussian Splatting representation is

initialized from this model and further refined with photometric and geometric losses. To evaluate generalization, we introduce a two-level protocol: Level 1 tests novel views along the training path, while Level 2 tests novel views at unseen times or poses. We also release a new RGB-D dataset for monocular dynamic scene reconstruction. Our method sets a new state-of-the-art, outperforming prior work in both synthesis quality and deformation accuracy.

## CCS Concepts

* **Computing methodologies** → **Image-based rendering; Reconstruction; Shape modeling; Motion capture.**

## Keywords

Dynamic Novel View Synthesis, monocular RGB-D, 3D Gaussian Splatting, Deformation Field estimation, Unsigned Distance Field, ARAP regularization

## ACM Reference Format:

David Stéphane Belemkoabga, Thomas Maugey, and Christine Guillemot. 2025. GS-Morph: Dynamic Novel View Synthesis via UDF-ARAP Gaussian Splat Morphing. In *European Conference on Visual Media Production (CVMP '25), December 3-4, 2025, London, United Kingdom*. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3756863.3769701

## 1 Introduction

Monocular view synthesis for dynamic scenes is a significant challenge in computer vision and graphics, driven by applications in AR, VR, and cinema. While multi-camera setups yield high-quality results, their cost and complexity limit accessibility. Monocular RGB-D methods aim to democratize this technology, enabling capture via consumer devices like smartphones. However, synthesizing

<figure><img src="image_3.png" /></figure>

<footnote>*This work was supported and financed by Tyndall FX, Paris, France, during the author's employment as a Research Engineer.</footnote>
dynamic views from monocular input suffers from spatiotemporal ambiguity, limiting robustness and generalization.

We address this with a method that reconstructs a temporally coherent 3D model from dynamic monocular RGB-D sequences. Each frame provides an RGB image, depth map, and camera pose. The core challenge is to model non-rigid surface deformations and appearance variations while preserving geometry, enabling photo-realistic rendering at arbitrary timesteps and viewpoints. Existing approaches often conflate geometry and deformation estimation, limiting generalization. In contrast, we propose a strong a priori method that explicitly separates these components, improving robustness. We aim to: (1) estimate 3D geometry, (2) compute deformation fields between arbitrary timesteps, and (3) synthesize high-fidelity novel views across time and space.

Most prior work, including [30], [9], and [28], targets synthesis near the training trajectory, assessing only limited generalization. To address this, we introduce a structured evaluation framework with three difficulty levels: **Level 1**, where novel views match the training trajectory (blue path in fig. 4), and **Level 2**, where novel views are rendered at different timesteps from training images. Unlike prior work, we focus on this harder case, more relevant for practical deployment.

Our pipeline combines 3D Gaussian Splatting [11] with deformation fields estimated via UDF-based registration [22] and ARAP regularization [25], enabling robust propagation to unobserved regions. We process the sequence as GoPs, estimate per-GoP geometry and deformations, and fuse them into a global model. This model initializes a 3DGS representation refined for photorealistic novel view synthesis.

We also introduce a comprehensive RGB-D dataset for monocular view synthesis in dynamic scenes. Our evaluation uses PSNR, SSIM, LPIPS, and no-reference metrics to assess performance across difficulty levels, demonstrating our method's superiority even under large deformations and wide camera shifts.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Evaluation protocol illustration. **Level 1** (blue cameras) corresponds to rendering views along the training trajectory, whereas **Level 2** (orange cameras) introduces novel camera viewpoints outside the trajectory for a more challenging generalization test.</figcaption></figure>

# 2 Related Work

## 2.1 NeRFs and 3D Gaussian Splatting

Neural Radiance Fields (NeRFs) [16] were a breakthrough for implicit scene reconstruction. Numerous extensions have explored dynamic scenes [4, 14, 19, 20, 27], but they often suffer from high computational costs and long rendering times.

3D Gaussian Splatting (3DGS) [11] recently emerged as a faster alternative, encoding scenes with sparse 3D Gaussians for real-time, high-quality rendering. Several methods have adapted 3DGS for dynamic scenes, as discussed below.

## 2.2 Dynamic 3D Gaussian Splatting

Template-based methods like HUGS [12], GART [13], GauHuman [7], and CanonicalFusion [23] rely on rigged templates or morphable models. These perform well in controlled settings but lack generalization to unconstrained dynamics.

To address this, template-free methods such as 4D Gaussians [30], Deform-GS [3], and SC-GS [9] attempt to directly learn deformation in a monocular setting. However, due to the ill-posed nature of monocular reconstruction, these methods often struggle with extensive motion or noisy depth.

Other recent works incorporate priors such as motion masks, optical or scene flows, or monocular depth maps to improve learning [26, 28, 29]. While more robust, these methods typically involve long preprocessing pipelines or large training times.

## 2.3 Distance Fields for Geometry and Motion

Signed Distance Fields (SDFs) have been widely used in static scene fusion (e.g., KinectFusion [18]) and dynamic reconstruction (e.g., DynamicFusion [17], KillingFusion [24]). However, SDFs are ill-suited for partial or open surfaces common in monocular settings.

In contrast, Unsigned Distance Fields (UDFs) [6] provide a more flexible surface representation. Methods like MeshUDF [6] and NeuSurf [8] show that UDFs yield accurate reconstructions even from incomplete input, though mostly limited to static scenes. To our knowledge, UDFs have not yet been fully explored for deformation field estimation in dynamic view synthesis.

## 2.4 Incremental Fusion for Dynamic Reconstruction

Incremental fusion has a long history in RGB-D SLAM. Systems like PointFusion [10], DynamicFusion [17], and BundleFusion [2] fuse geometry over time but assume limited deformation and static scenes. Our approach extends this idea to deformable 3D Gaussian fusion, integrating motion-aware fusion with ARAP-based regularization in a monocular dynamic setting.

# 3 Method

## 3.1 Overview

Figure 3 provides an overview of our method. The algorithm takes as input an RGB-D video sequence along with corresponding camera poses. Since our focus is on dynamic scene content, we use SAM-2 [21] to segment and isolate dynamic objects. The static background is modeled separately using standard 3D Gaussian Splatting (3DGS) [11].

We propose a five-stage pipeline for novel view synthesis from dynamic monocular RGB-D sequences:

(1) **Frame-to-Frame Deformation Field Estimation:** For each frame, we extract a 3D point cloud via depth deprojection and segment dynamic regions using SAM-2. Deformation fields between consecutive point clouds are estimated
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Overview of our full pipeline for novel view synthesis in dynamic scenes from monocular RGB-D input. Given a video with estimated depth maps and camera poses, we first isolate dynamic content via motion masks and segmentations (left). We then estimate a per-frame deformation field using UDF-based registration regularized by ARAP to align consecutive point clouds. The sequence is partitioned into K Groups of Pictures (GoPs), each spanning $n_k$ frames and delimited by keyframes at times $t_k$. Partitioning is guided by the cumulative motion magnitude of the frame-to-frame deformation fields. Each GoP is processed independently by alternating fusion and ARAP-based propagation, producing temporally consistent intra-GoP models with deformation fields. These partial reconstructions are merged hierarchically to form a globally consistent canonical geometry. Finally, we initialize and refine a spatio-temporal Gaussian Splatting representation using the fused geometry and deformation priors, culminating in a dense, dynamic 4DGS model that supports high-quality photorealistic novel view rendering.</figcaption></figure>

using UDF-based registration with ARAP regularization [25]. We define frame-to-frame deformation fields as 3D deformation fields estimated between consecutive point clouds $P_t$ and $P_{t+1}$.

(2) **GoP Partitioning:** The sequence is divided into K Groups of Pictures (GoPs), based on cumulative motion magnitude. Each GoP spans a contiguous set of frames and is delimited by keyframes. In our formulation, a GoP is not only composed of RGB-D images, but is augmented with the corresponding point clouds and frame-to-frame deformation fields.

(3) **Local Reconstruction:** Each GoP is processed independently through alternating fusion and ARAP-based deformation propagation to produce a locally consistent reference geometry and intra-GoP deformation field.

(4) **Hierarchical Fusion:** GoP reconstructions are hierarchically merged via deformation-guided alignment and ARAP optimization, yielding a globally consistent canonical model with a unified deformation field.

(5) **Spatio-Temporal Splatting:** The fused model is converted into a spatio-temporal 3D Gaussian Splatting representation and optimized with combined photometric and geometric supervision. When available, the background is jointly modeled as a static component.

Each of these stages is described in detail in the following sections.

## 3.2 Frame-to-Frame Deformation Field Estimation

We consider a sequence of N consecutive point clouds $\mathcal{P} = \{P_t\}_{t=0}^N$, each obtained by deprojecting the corresponding depth map and subsampled using Furthest Point Sampling (FPS). Our goal is to compute a deformation field $\psi_{t \to t+1}$ that warps $P_t$ into $P_{t+1}$ by assigning a 3D translation vector to each point.

We estimate $\psi_{t \to t+1}$ by minimizing a combination of a UDF-based alignment loss and an As-Rigid-As-Possible (ARAP) regularization term [25]:

$$ \mathcal{L} = \mathcal{L}_{\text{UDF}} + \mathcal{L}_{\text{ARAP}}. \qquad (1) $$

### 3.2.1 UDF-based Registration Loss.
Let $\psi_{t \to t+1}(x_t)$ denote the transformed position of a point $x_t \in P_t$. The goal is to deform $P_t$ so that each point lies close to the surface underlying $P_{t+1}$. This surface is implicitly represented by an Unsigned Distance Function (UDF), defined as:

$$ \text{UDF}_{P_{t+1}}(x) := \min_{p \in S} \|x - p\|, \qquad (2) $$

where $S$ is the (unknown) surface sampled by $P_{t+1}$. The registration objective becomes:

$$ \mathcal{L}_{\text{UDF}} = \sum_{x_t \in P_t} \text{UDF}_{P_{t+1}}(\psi_{t \to t+1}(x_t)). \qquad (3) $$

To speed up computation, we approximate this using point-to-plane distances [15]:

$$ \mathcal{L}_{\text{UDF}} \approx \sum_{x_t \in P_t} |(\psi(x_t) - x^*) \cdot n_{x^*}|, \qquad (4) $$

where $x^*$ is the nearest neighbor of $\psi(x_t)$ in $P_{t+1}$, and $n_{x^*}$ is the local surface normal at $x^*$ estimated via PCA.

While prior work has leveraged UDFs for static geometry reconstruction [6, 8], we are the first to use UDF-based alignment to estimate deformation fields in dynamic scenes, enabling temporally consistent motion modeling from monocular depth maps.

### 3.2.2 ARAP Regularization.
To ensure locally plausible and rigid deformations, we add an ARAP term [25]. Given a neighborhood graph $\mathcal{N}(i)$ over the point cloud, the energy is defined as:

$$ \mathcal{L}_{\text{ARAP}} = \sum_{i} \sum_{j \in \mathcal{N}(i)} w_{ij} \|\mathcal{D}(x_i) - \mathcal{D}(x_j) - \mathbf{R}_i(x_i - x_j)\|^2, \qquad (5) $$
where $\mathcal{D}$ is the current deformation, $w_{ij}$ are proximity-based weights, and $\mathbf{R}_i$ is the optimal local rotation for node $i$.

To compute $\mathbf{R}_i$, we solve a local Procrustes problem via SVD of the covariance matrix:

$$S_i = \sum_{j \in \mathcal{N}(i)} w_{ij} (\mathcal{D}(x_j) - \mathcal{D}(x_i)) (x_j - x_i)^{\top} = U \Sigma V^{\top}, \quad (6)$$

with:

$$\mathbf{R}_i = U \text{diag}(1, 1, \det(UV^{\top})) V^{\top}. \quad (7)$$

The ARAP regularizer term promotes here local rigidity to preserve the geometry after deformation. Combined with UDF registration, it provides a robust formulation for dynamic geometry alignment.

## 3.3 GoP Partitioning

Once the frame-to-frame deformation field is estimated, we divide the video into $N$ groups of pictures (GoPs) to robustly handle large motion across long sequences. The splitting is guided by motion magnitude: for each frame, we compute the total motion as the sum of translation vectors norms, then build the cumulative distribution. Frames are partitioned into bins such that each GoP contains an equal amount of motion, ensuring uniform handling of deformation across the sequence.

This GoP-based division addresses three key challenges: (1) monocular captures provide only partial observations per frame, making it difficult to estimate deformations for unobserved regions; (2) tracking deformations over long sequences causes drift due to error accumulation; and (3) sequential processing of long videos is time-consuming, whereas independent GoPs enable parallel processing. The GoP structure reduces drift, improves geometric consistency, and enables scalable, parallel computation.

The following section details the processing within each GoP.

## 3.4 Intra-GoP Fusion

We denote by $\mathcal{G}_k^t$ the evolving geometry for the $k$-th Group of Pictures (GoP), initialized at keyframe $t_k$ with the point cloud $P_{t_k}$ and its frame-to-frame deformation field $\psi_{t_k \to t_k+1}$. Our goal is to recover a temporally consistent geometry $\mathcal{G}_k$ by iteratively fusing point clouds across the GoP:

$$\mathcal{G}_k^t = \text{Fusion} (\{P_n\}_{t_k \le n \le t_k}), \quad (8)$$

The process alternates between forward fusion and ARAP-based deformation field propagation, as illustrated in fig. 4.

**Step 1: Forward Fusion.** At each timestep $t$, the current geometry $\mathcal{G}_k^t$ is warped to $t+1$ using $\psi_{t \to t+1}$ and fused with $P_{t+1}$:

$$\mathcal{G}_k^{t+1} \leftarrow \psi(\mathcal{G}_k^t) \cup P_{t+1}. \quad (9)$$

Fusion is performed by associating each point $p_{new} \in P_{t+1}$ with the nearest point $p \in \psi(\mathcal{G}_k^t)$ and checking:

$$\min_{p} \|p_{\text{new}} - p\| < \tau. \quad (10)$$

If matched, the points are merged via confidence-weighted averaging:

$$p \leftarrow \frac{w_{\text{old}}p + w_{\text{new}}p_{\text{new}}}{w_{\text{old}} + w_{\text{new}}}, \quad (11)$$

otherwise, $p_{new}$ is added to $\mathcal{G}_k^{t+1}$.

**Step 2: ARAP Propagation.** Newly added points lack deformation vectors. To extend motion estimates, we propagate known deformation vectors via ARAP (As-Rigid-As-Possible) optimization [25] over a neighborhood graph. Each node's displacement $u_i$ is computed by solving:

$$Lu = b, \quad (12)$$

where $L$ is the Laplacian matrix and $b_i$ depends on local rotations $\mathbf{R}_i$, estimated using Procrustes alignment.

We preserve previously estimated deformations by imposing soft constraints on a subset of nodes $i \in \mathcal{H}$ with known displacements $u_i^{\text{known}}$:

$$L' = L + \lambda I_{\mathcal{H}}, \quad b_i = \begin{cases} b_i + \lambda u_i^{\text{known}} & \text{if } i \in \mathcal{H}, \\ b_i & \text{otherwise.} \end{cases} \quad (13)$$

This allows the fused geometry to inherit plausible, smooth motion while enforcing consistency with known regions.

**Iterative Completion.** We alternate fusion and ARAP propagation across the full GoP. At the final frame $t_k+n_k$, the accumulated deformation $\psi_{t_k \to t_k+n_k}$ is inverted to map the geometry back to the keyframe $t_k$, with $n_k$ denotes the number of frames in the GoP:

$$\mathcal{G}^k := \psi^{-1}(\mathcal{G}_k^{t_k+n_k}). \quad (14)$$

This produces a canonical geometry with complete intra-GoP deformation fields, ready for global fusion.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Intra-GoP fusion strategy. Each row corresponds to a stage in the fusion pipeline for a Group of Pictures (GoP). At each timestep, the current geometry is warped using the forward deformation field $\psi_{t \to t+1}$ and fused with the next point cloud $P_{t+1}$. Newly added regions lacking deformation information are completed via ARAP-based propagation using previously known motion. This alternating process of warping, fusion, and deformation field completion is repeated across all point clouds in the GoP. At the end of the GoP, the accumulated deformation is inverted to map the final geometry back to the reference time $t_k$.</figcaption></figure>

## 3.5 Hierarchical Global Model Estimation

After estimating fused reference geometries and deformation fields for each GoP, we hierarchically merge these partial reconstructions into a single, globally consistent model. Following the intra-GoP fusion strategy in Section 3.4, we iteratively fuse pairs of adjacent
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Illustration of intra-GoP fusion. (a) Input point clouds at different time steps, each capturing a distinct dynamic pose. (b) Naïve stacking of these point clouds without alignment leads to significant geometric inconsistency. (c) Our method estimates deformation fields to align the inputs before merging, producing a coherent fused model that integrates geometry across time.</figcaption></figure>

GoPs—denoted by $G_k$ and $G_{k+1}$ with their corresponding deformation fields $\psi_k$ and $\psi_{k+1}$.

At each hierarchy level, we begin by aligning one model to the next using the estimated deformation fields, followed by geometry fusion. To ensure spatial coherence, we then propagate deformation parameters across the merged domain by minimizing the ARAP energy. This effectively infers a new deformation field for the fused model that respects the structure of both contributing GoPs.

This process, illustrated in fig. 6, is repeated hierarchically: at each stage, the number of models is halved until a single global model and its associated deformation field are obtained. The hierarchical structure enables scalable and parallel processing, mitigates drift and accumulated registration error, and ensures global consistency across long and complex sequences.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Hierarchical global model fusion. Each colored box represents a local intra-GoP reconstruction. At each level, pairs of adjacent GoP models are aligned using their respective deformation fields, then fused geometrically. A new deformation field is estimated for the merged model via ARAP optimization. This process is repeated hierarchically until all local reconstructions are integrated into a single, globally consistent canonical model with a unified deformation field.</figcaption></figure>

## 3.6 Spatio-Temporal Gaussian Splatting

All previous steps—GoP fusion, global model construction, and ARAP-regularized deformation estimation—serve to initialize a spatio-temporal 3D Gaussian Splatting model. This model is then refined through a differentiable rendering loop that optimizes appearance and motion parameters using photometric and geometric supervision.

### 3.6.1 Stage 1: Refinement of Reference Gaussians.
We first convert the fused canonical geometry into a sparse set of isotropic 3D Gaussians. Each Gaussian is initialized with a center $\mu$, color $c$, and opacity $o$, and is associated with a per-Gaussian translation derived from the previously estimated deformation field.

At each training timestep, the Gaussians are deformed using their current motion parameters, rendered via the differentiable rasterizer of [11], and optimized through gradient descent to minimize a joint loss:

$$ \mathcal{L} = \lambda_{\text{UDF}} \mathcal{L}_{\text{UDF}} + \lambda_c \mathcal{L}_c + \lambda_d \mathcal{L}_d + \lambda_{\text{ARAP}} \mathcal{L}_{\text{ARAP}}. \quad (15) $$

**Photometric supervision.** We supervise the rendered RGB and depth outputs using ground-truth images:

$$ \mathcal{L}_c = \|I_{\text{pred}} - I_{\text{gt}}\|_1 + \lambda_{\text{SSIM}} [1 - \text{SSIM}(I_{\text{pred}}, I_{\text{gt}})], \quad (16) $$

$$ \mathcal{L}_d = \|D_{\text{pred}} - D_{\text{gt}}\|_1, \quad (17) $$

where $I_{\text{pred}}$ and $D_{\text{pred}}$ are produced by rendering the deformed Gaussians with the rasterizer. $\mathcal{L}_{\text{UDF}}$ aligns each Gaussian center to the zero-level set of the implicit surface, and $\mathcal{L}_{\text{ARAP}}$ regularizes the deformation to maintain locally rigid motion.

This stage refines both the geometry and motion, improving the fidelity of the reference model and correcting deformation errors via supervision on real images.

### 3.6.2 Stage 2: Dense Model with Dual- Quaternion Deformations.
Stage 1 provides a sparse and reliable initialization with per-Gaussian translations. In Stage 2, we densify the representation and switch to dual-quaternion-based deformation modeling to capture both local rotations and translations over time.

**Deformation graph.** We subsample the Gaussians from Stage 1 to define a sparse deformation graph. Each node is assigned a dual quaternion $\tilde{q}_i = (q_{r,i}, q_{d,i})$, where $q_{r,i}$ encodes rotation and $q_{d,i}$ encodes translation. These are initialized from Stage 1 by setting $q_{r,i}$ to identity and $q_{d,i}$ to the translation vector.

**Dual-quaternion blending.** The motion of each Gaussian $g$ is obtained by blending the dual quaternions of its $K$ nearest deformation nodes:

$$ \tilde{q}_g = \frac{\sum_i w_{ig} \tilde{q}_i}{\|\sum_i w_{ig} \tilde{q}_i\|}, \quad \hat{\mu}_g = \tilde{q}_g \star (0, \mu_g) \star \tilde{q}_g^{-1}, \quad (18) $$

where $\star$ denotes dual quaternion multiplication and $w_{ig}$ are radial basis weights based on distances between the Gaussian and the graph nodes.

**Optimization loop.** The deformation graph and all Gaussian parameters (excluding initial positions) are optimized by repeating the same loop: (1) deform Gaussians using dual-quaternion blending, (2) render them via the differentiable rasterizer, and (3) minimize the joint loss (15). This ensures photometric consistency, geometric fidelity, and physically plausible motion.

**Result.** The final model is a dense, spatio-temporal Gaussian scene representation with dual-quaternion motion, capable of rendering high-quality novel views with consistent dynamics across time and space.

## 4 Experiments

We evaluate the effectiveness of our method through both qualitative and quantitative analyses conducted on two distinct datasets: a synthetic benchmark and the Dycheck multi-camera dataset [5]. The synthetic dataset provides controlled conditions for direct comparisons with state-of-the-art methods, while the Dycheck dataset
assesses real-world applicability. Supplementary material includes qualitative video results demonstrating our method.

## 4.1 Dataset Description

*Synthetic Dataset.* We introduce a collection of dynamic sequences featuring complex human motions built from Adobe Mixamo characters [1]. Each sequence provides RGB images and ground-truth depth maps, enabling evaluation with Masked PSNR (M-PSNR), Masked SSIM (M-SSIM), and Masked LPIPS (M-LPIPS). Each sequence includes thirteen (13) camera views: a single monocular moving camera used for training, and twelve (12) fixed cameras positioned along the monocular trajectory for evaluation.

*Dycheck Dataset.* We also evaluate on the Dycheck multi-camera dataset [5], which consists of three camera views: one monocular moving camera for training, and two static cameras for evaluation. This dataset captures real-world dynamics under practical conditions. In this dataset, we use the provided captured depth maps.

## 4.2 Evaluation Protocol

We propose an evaluation protocol specifically designed for dynamic novel view synthesis. We categorize view synthesis difficulty into two levels, based on the pose $C_r$ and timestep $t_r$ of the novel view relative to the training set. Let $d_c$ denote a distance metric between camera poses, and let $C$ be the closest training pose to $C_r$. **Level 1 (Exact Match).** Views in $C_r^1$ exactly match both the pose and timestep of training frames and serve as reference views for standard evaluation.

**Level 2 (Generalization).** Views in $C_r^2$ deviate in either pose or time from training data, requiring the model to generalize to unseen geometry or dynamics.

## 4.3 Quantitative Results

**Level 1.** Section 4 and table 1 report Level 1 evaluation results on the synthetic dataset, comparing our method with 4D-Gaussians[30], Deformable-3DGS[31], and SC-GS[9]. For fairness, all benchmarking methods are trained using ground truth depth to initialize the scene, providing them with a strong prior—despite originally relying on random point cloud initialization. We focus on masked metrics (M-PSNR, M-SSIM, M-LPIPS), which evaluate rendering quality only on dynamic regions. As indicated, plain scores correspond to masked metrics, while overall scene metrics are shown in gray for context.

Across all sequences, our method outperforms baselines by a significant margin in masked PSNR and LPIPS. This indicates improved fidelity and structural coherence in the dynamic content. SC-GS and Deformable-3DGS show competitive performance in low-motion sequences but struggle with temporally coherent fusion under strong deformation. We present a quantitative evaluation on some scenes from the Dycheck real-world dataset in the annex. While the performance gap is narrower than in synthetic sequences, owing to sensor noise and subtler dynamics, our method still achieves higher reconstruction quality and better temporal consistency across scenes.

**Level 2.** Figure 7 presents detailed PSNR curves for each tested scenario, highlighting the robustness of our method across various camera positions and timesteps. Out of the 12 evaluation cameras, we showcase results from three fixed cameras across four representative scenes (additional results are provided in the annex). For each fixed camera, we plot the PSNR as a function of rendering timestep. High PSNR values indicate the ability to generalize beyond the training trajectory. As shown, our method consistently outperforms state-of-the-art methods, especially at timesteps distant from the training views.

## 4.4 Qualitative Analysis

In Figure 8, we visualize close-up comparisons across methods, demonstrating that our approach better preserves fine-grained details such as limbs and intricate textures, outperforming competing techniques in visual realism and detail retention.

Figure 9 shows qualitative results from level 2 evaluations, focusing on model generalization at camera positions unseen during training. Our method successfully preserves geometric integrity and provides accurate depth estimation across different timesteps, outperforming alternative methods notably in dynamic deformation scenarios.

Figure 10 visually represents the learned deformation fields through optical flow examples, clearly depicting the ability of our method to accurately model complex, non-rigid motions over time.

Section 6 showcase qualitative Level 2 results on the Dycheck Multicamera dataset. Despite the increased complexity of real-world capture, our method achieves visibly sharper reconstructions and higher fidelity than state-of-the-art baselines.

## 5 Limitations

Our method has three primary limitations. First, it relies on the accuracy of the input depth maps—higher-quality depth maps result in more accurate reconstructions. For fairness, we note that other benchmarked methods like [9] also benefit significantly from depth-based priors and show improved performance when initialized with ground truth depth.

Second, while ARAP was originally designed for meshes with well-defined connectivity, we apply it to point clouds by approximating local neighborhoods using $k$-nearest neighbor graphs. This surrogate graph structure is inherently suboptimal, and we believe that better graph design tailored to point clouds could improve deformation quality and stability.

Third, our method may fail to capture very small motions that fall within the margin of numerical or photometric error, leading to subtle misalignments during tracking.

Despite these limitations, our approach consistently delivers strong results across a wide range of complex dynamic scenes. We believe that future work incorporating more accurate depth estimation and improved graph construction will further enhance reconstruction quality and robustness.

## 6 Conclusion

In summary, our method introduces a principled pipeline for dynamic novel view synthesis from monocular RGB-D sequences. By
<table><thead><tr><th rowspan="2">Methods</th><th colspan="3">ArmWave</th><th colspan="3">Capoeira</th><th colspan="3">Climbing</th><th colspan="3">Dancing</th><th colspan="3">Floating</th></tr><tr><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th></tr></thead><tbody><tr><td>4D-Gaussians</td><td>10.62<sup>23.22</sup></td><td>0.97<sup>0.97</sup></td><td>0.40<sup>0.04</sup></td><td>5.70<sup>18.26</sup></td><td>0.96<sup>0.95</sup></td><td>0.54<sup>0.11</sup></td><td>12.86<sup>23.05</sup></td><td>0.97<sup>0.95</sup></td><td>0.32<sup>0.07</sup></td><td>10.14<sup>23.17</sup></td><td>0.97<sup>0.96</sup></td><td>0.35<sup>0.09</sup></td><td>15.86<sup>24.88</sup></td><td>0.96<sup>0.96</sup></td><td>0.23<sup>0.07</sup></td></tr><tr><td>Deformable-3DGS</td><td>23.43<sup>38.22</sup></td><td>0.99<sup>1.00</sup></td><td>0.18<sup>0.02</sup></td><td>15.38<sup>27.33</sup></td><td>0.98<sup>0.98</sup></td><td>0.31<sup>0.05</sup></td><td>26.41<sup>35.86</sup></td><td>0.99<sup>0.99</sup></td><td>0.12<sup>0.02</sup></td><td>18.80<sup>31.67</sup></td><td>0.99<sup>0.99</sup></td><td>0.13<sup>0.04</sup></td><td>28.74<sup>36.62</sup></td><td>0.99<sup>0.99</sup></td><td>0.05<sup>0.02</sup></td></tr><tr><td>SC-GS</td><td>23.77<sup>35.62</sup></td><td>0.99<sup>1.00</sup></td><td>0.14<sup>0.01</sup></td><td>17.29<sup>29.51</sup></td><td>0.98<sup>0.99</sup></td><td>0.21<sup>0.03</sup></td><td>27.64<sup>36.89</sup></td><td>0.99<sup>1.00</sup></td><td>0.10<sup>0.02</sup></td><td>9.28<sup>22.55</sup></td><td>0.97<sup>0.96</sup></td><td>0.36<sup>0.09</sup></td><td>31.57<sup>38.66</sup></td><td>0.99<sup>1.00</sup></td><td>0.03<sup>0.01</sup></td></tr><tr><td>Ours</td><td>26.17<sup>37.30</sup></td><td>0.99<sup>1.00</sup></td><td>0.14<sup>0.00</sup></td><td>24.10<sup>35.19</sup></td><td>0.99<sup>0.99</sup></td><td>0.17<sup>0.01</sup></td><td>27.59<sup>36.94</sup></td><td>0.99<sup>0.99</sup></td><td>0.09<sup>0.01</sup></td><td>26.50<sup>38.21</sup></td><td>1.00<sup>1.00</sup></td><td>0.07<sup>0.01</sup></td><td>29.31<sup>37.19</sup></td><td>0.99<sup>1.00</sup></td><td>0.05<sup>0.01</sup></td></tr></tbody></table>

<table><caption>Table 1: Evaluation on the synthetic dataset at Level 1. The primary metrics reflect performance on the masked dynamic foreground regions, while the smaller values in subscript indicate scores computed over the entire image.</caption><thead><tr><th rowspan="2">Methods</th><th colspan="3">Jumping</th><th colspan="3">Mutant</th><th colspan="3">Double Dagger Stab</th><th colspan="3">AVG.</th></tr><tr><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th><th>M-PSNR ↑</th><th>M-SSIM ↑</th><th>M-LPIPS ↓</th></tr></thead><tbody><tr><td>4D-Gaussians</td><td>6.88<sup>18.92</sup></td><td>0.96<sup>0.95</sup></td><td>0.46<sup>0.09</sup></td><td>16.83<sup>24.28</sup></td><td>0.93<sup>0.92</sup></td><td>0.37<sup>0.09</sup></td><td>13.03<sup>23.90</sup></td><td>0.96<sup>0.95</sup></td><td>0.37<sup>0.06</sup></td><td>11.49<sup>22.46</sup></td><td>0.96<sup>0.95</sup></td><td>0.38<sup>0.08</sup></td></tr><tr><td>Deformable-3DGS</td><td>16.56<sup>28.68</sup></td><td>0.98<sup>0.99</sup></td><td>0.27<sup>0.04</sup></td><td>24.58<sup>31.97</sup></td><td>0.97<sup>0.98</sup></td><td>0.17<sup>0.04</sup></td><td>20.39<sup>31.26</sup></td><td>0.98<sup>0.99</sup></td><td>0.15<sup>0.03</sup></td><td>21.79<sup>32.33</sup></td><td>0.98<sup>0.99</sup></td><td>0.17<sup>0.03</sup></td></tr><tr><td>SC-GS</td><td>21.19<sup>32.65</sup></td><td>0.99<sup>0.99</sup></td><td>0.18<sup>0.02</sup></td><td>27.67<sup>34.57</sup></td><td>0.98<sup>0.99</sup></td><td>0.09<sup>0.02</sup></td><td>25.23<sup>34.88</sup></td><td>0.99<sup>0.99</sup></td><td>0.10<sup>0.02</sup></td><td>22.96<sup>33.17</sup></td><td>0.99<sup>0.99</sup></td><td>0.15<sup>0.03</sup></td></tr><tr><td>Ours</td><td>23.33<sup>34.42</sup></td><td>0.99<sup>0.99</sup></td><td>0.18<sup>0.01</sup></td><td>28.23<sup>35.02</sup></td><td>0.99<sup>0.99</sup></td><td>0.07<sup>0.01</sup></td><td>27.79<sup>36.83</sup></td><td>0.99<sup>1.00</sup></td><td>0.07<sup>0.01</sup></td><td>26.63<sup>36.39</sup></td><td>0.99<sup>0.99</sup></td><td>0.10<sup>0.01</sup></td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>Fig. 7. PSNR curves for each evaluated method across four distinct dynamic sequences. Each plot displays PSNR scores computed by fixing the rendering camera at specific training positions (timesteps 10, 50, 90) and varying the timestep for evaluation. Notably, our method (orange curve) consistently outperforms baselines (4D-Gaussians, SC-GS, Deformable-3DGS), especially at camera positions distant from the training reference (illustrated by the significant PSNR margin in peripheral regions of each curve). This demonstrates superior spatiotemporal generalization and robustness.</figcaption></figure>

explicitly separating deformation estimation and geometric modeling, we mitigate drift and improve generalization. Our UDF-based pairwise registration with ARAP regularization enables robust frame-to-frame alignment, while the GoP-based fusion strategy incrementally builds a globally consistent reference geometry. The resulting spatio-temporal Gaussian Splatting model benefits from a well-initialized deformation prior, enabling accurate rendering of dynamic scenes across time and viewpoint. This structured approach provides a strong foundation for high-fidelity reconstruction under monocular constraints.

While we evaluate our method on selected Dycheck scenes, we note that the dataset contains only limited motion and predominantly small deformations—scenarios for which our method is not

specifically optimized. Our approach is best suited to scenes with larger ranges of motion, and we currently lack access to real-world datasets with Level 2 evaluation ground truth to fully validate this regime.
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Detailed visual comparisons at Level 1. Magnified regions clearly show our method's superior capacity to preserve fine-grained details (e.g., fingers, limb articulations, textures) compared to alternative approaches. The competing methods (4D-Gaussians, Deformable-3DGS, SC-GS) visibly suffer from blurriness or geometric distortions, while our results remain sharp and faithful to ground-truth details.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Qualitative comparison at Level 2 (generalization scenarios) at timesteps 40 and 90. Here, each method must reconstruct novel views significantly distant from the original training camera poses. Our method distinctly maintains geometric integrity and depth accuracy across challenging dynamic scenes. In contrast, competing methods exhibit visible shape distortion or depth inaccuracies, underscoring our method's improved capability in modeling unseen dynamics.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Visualization of learned deformation fields through optical flow. Color-coded vectors clearly illustrate our method's capability to capture complex non-rigid human motion with high fidelity. This detailed deformation modeling underpins our method's superior performance in dynamic novel view synthesis scenarios.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Dycheck Real Dataset – Spin Scene, Level 2 Evaluation. The camera view is fixed across time. Our method outperforms the state of the art.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Dycheck Real Dataset – Paper-windmill Scene, Level 2 Evaluation. The camera view is fixed across time. Our method outperforms the state of the art.</figcaption></figure>
## Acknowledgments

This work was supported by Tyndall FX, Paris, France. We thank Lionel Bouchard, Raou Mallart, Matis Hudon, and Guillaume Cortes for enabling this research project. We are especially grateful to Matis Hudon and Martin Alain, who supervised the author during his PhD, for their guidance and mentorship during the early stages of this research. We also thank Thibault Lescoat for insightful discussions and Binh Minh Nguyen, Arthur Lecert, and Antonin Joly for their assistance with dataset acquisition.

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