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Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, Xiaogang Jin
TL;DR
Dynamic neural rendering methods often struggle with fine detail and real-time performance, especially for monocular dynamic scenes. The paper addresses this with canonical-space deformable 3D Gaussians, a deformation field, differential rasterization, and annealing smoothing training. Experiments report superior rendering quality and speed, while the authors identify sensitivity to viewpoint coverage, pose accuracy, Gaussian count, and complex human motion.
Problem
Implicit dynamic-scene representations often struggle to capture intricate details and achieve real-time rendering, limiting their applicability to monocular dynamic-scene tasks.
Method
The method jointly learns canonical-space 3D Gaussians and an implicit deformation field through a differential Gaussian rasterizer, with annealing smoothing training for pose-related temporal jitter.
Results
The method outperforms existing approaches in rendering quality and speed, with real-time rendering above 30 FPS on an NVIDIA RTX 3090 when using fewer than 250k Gaussians.
Takeaways & Limitations
Deformable 3D Gaussians provide a high-fidelity, editable point-based framework for monocular dynamic-scene reconstruction, novel-view synthesis, time interpolation, and real-time rendering.
Takeaways & Limitations
Performance depends on viewpoint diversity and pose accuracy, scales in temporal cost with Gaussian count, and remains unvalidated for intricate human motions such as nuanced facial expressions.
Abstract
from arXiv · showhide
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering. Nonetheless, cutting-edge dynamic neural rendering methods rely heavily on these implicit representations, which frequently struggle to capture the intricate details of objects in the scene. Furthermore, implicit methods have difficulty achieving real-time rendering in general dynamic scenes, limiting their use in a variety of tasks. To address the issues, we propose a deformable 3D Gaussians Splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with no extra overhead, which can mitigate the impact of inaccurate poses on the smoothness of time interpolation tasks in real-world datasets. Through a differential Gaussian rasterizer, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed, making it well-suited for tasks such as novel-view synthesis, time interpolation, and real-time rendering.
1. Introduction
Dynamic-scene reconstruction requires detailed, realistic rendering and efficient inference, but implicit representations often struggle with both. This paper addresses these limitations with deformable 3D Gaussians, canonical-space learning, and annealing smoothing training.
- Motivation: Implicit dynamic-scene methods often struggle to capture intricate object details and achieve real-time rendering.These limitations restrict their use in applications requiring both fidelity and speed.
- Approach: The proposed framework extends 3D-GS to model monocular dynamic scenes with deformable 3D Gaussians learned in canonical space.A deformation field is jointly trained with the learnable Gaussians rather than reconstructing each frame independently.
- Approach: A customized differential Gaussian rasterization pipeline supplies gradients for jointly optimizing the deformation field and 3D Gaussians.This preserves the point-based representation while supporting dynamic-scene reconstruction.
- Training: Annealing smoothing training improves temporal smoothness during time interpolation and allows greater rendered detail without additional computational complexity.The mechanism targets jitter caused by inaccurate poses in real-world datasets.
- Outcome: The framework is designed to combine high-fidelity reconstruction with real-time rendering for monocular dynamic scenes.The stated contributions emphasize both rendering speed and scene quality.
2. Related Work
Prior work spans implicit, pre-computed, hybrid, and differentiable point-based representations for neural rendering and dynamic scenes. The paper positions deformable 3D Gaussians as a point-based alternative intended to improve editability, rendering quality, and speed for monocular dynamics.
- Dynamic neural rendering: NeRF-based methods model dynamic scenes either by conditioning radiance fields on time or by using deformation fields to map observations into canonical space.The latter decouples motion from the canonical scene representation.
- Point-based rendering: The paper’s pipeline initializes Gaussians from SfM points, predicts time-dependent deformation offsets, and jointly optimizes them through differential rasterization.The overview includes a warm-up stage before deformation optimization.
- Dynamic neural rendering: Implicit dynamic-scene representations can provide useful modeling flexibility but often deliver unsatisfactory rendering quality and inefficient inference.The paper therefore focuses on monocular dynamic-scene reconstruction using differentiable point-based rendering.
- Rendering acceleration: Pre-computed acceleration methods cache spatial structures such as spherical-harmonic coefficients or feature vectors derived from implicit representations.These methods trade direct neural representation for faster rendering structures.
- Rendering acceleration: Hybrid methods combine neural components with explicit grids to accelerate training and inference while retaining strong representational capacity.Grid or plane structures have also been extended to time-conditioned dynamic representations.
- Point-based rendering: Differentiable point-based radiance fields, including 3D-GS, improve rendering speed through customized Gaussian rasterization pipelines.3D-GS supports rapid training and tasks such as novel-view synthesis and scene modeling.
3. Method
The method models monocular dynamic scenes with canonical-space 3D Gaussians and a jointly learned deformation field, rendered differentiably for efficient optimization. Annealing smoothing addresses pose inaccuracies during temporal interpolation while preserving dynamic detail without added computational overhead.
- Method overview: The method initializes 3D Gaussians from SfM sparse points, using time labels and calibrated camera poses from monocular image sequences.Each Gaussian includes position, opacity, covariance, and view-dependent appearance.
- Deformable 3D Gaussians: A deformation field takes Gaussian positions and time as input, producing position, rotation, and scaling offsets for time-varying canonical Gaussians.The deformed Gaussians are rendered through a differential Gaussian rasterization pipeline.
- Differentiable rendering: The 3D covariance is parameterized by learnable quaternion rotation and scaling components, then projected into image space using the view matrix and projective Jacobian.This supports differentiable rendering of canonical-space Gaussians.
- Deformable 3D Gaussians: Jointly learning the deformation field and Gaussians preserves spatiotemporal continuity for continuous monocular sequences, unlike independently trained time-specific Gaussian sets.The deformation network maps canonical Gaussians into deformed space using time and center position.
- Annealing Smooth Training: Inaccurate poses particularly affect interpolated-time rendering, where inconsistent scenes across frames can produce irregular visual jitter.The issue is more pronounced for explicit point-based rendering than for implicit representations with inherent MLP smoothness.
- Annealing Smooth Training: Annealing smooth training injects linearly decaying Gaussian noise during training to reduce pose-induced temporal jitter without additional computational overhead.The noise is intended to improve early temporal generalization while avoiding excessive late-stage smoothing and preserving object details.
4. Experiment
The method is evaluated on synthetic and real-world monocular dynamic-scene benchmarks, with quantitative, qualitative, efficiency, depth, and ablation analyses. Results indicate improved reconstruction detail, robustness to imperfect poses, temporal smoothness, and real-time rendering.
- The evaluation covers D-NeRF synthetic scenes and HyperNeRF and NeRF-DS real-world datasets, using the stated benchmark splits and image resolutions.
- Synthetic dataset: The method outperforms baselines on synthetic data, particularly on LPIPS and SSIM, which assess structural consistency.
- Synthetic dataset: Qualitative results show more consistent novel-view renderings and finer dynamic details, including delicate structures over time.
- Real-world datasets: On NeRF-DS, the method remains robust when camera poses are not perfectly accurate; HyperNeRF is excluded from quantitative analysis because some poses are highly inaccurate.
- Rendering efficiency: Over 30 FPS is achieved on an NVIDIA RTX 3090 when the number of 3D Gaussians is below 250k.
- Ablation and depth analysis: Depth visualization supports temporal deformation and geometric reconstruction, while annealing smooth training improves intricate-region convergence and temporal smoothness.
5. Limitations
The method has limitations related to viewpoint coverage, pose accuracy, computational scale, and motion complexity. These constraints define the settings in which its reconstruction quality and efficiency may degrade or remain uncertain.
- Sparse viewpoints and limited viewpoint coverage may cause overfitting because 3D-Gaussian convergence is strongly influenced by perspective diversity.
- Pose-estimation accuracy affects performance, with deviations from COLMAP poses associated with non-optimal PSNR on Nerfies and HyperNeRF.
- Larger 3D-Gaussian sets can increase training duration and memory consumption.
- The evaluations mainly cover moderate motion, leaving the method’s ability to handle nuanced facial expressions unresolved.
6. Conclusions
The method combines deformable 3D Gaussian splatting with canonical-space learning and annealing smoothing to model monocular dynamic scenes with high quality and real-time rendering.
- The method models monocular dynamic scenes with deformable 3D Gaussians learned in canonical space through a deformation field.
- Annealing smoothing reduces time-encoding overfitting while preserving intricate scene details without extra training overhead.
- Experimental results report superior rendering quality and real-time rendering compared with existing methods.
B.1. Network Architecture of the Deformation Field
The deformation field uses an MLP to map Gaussian coordinates and time to position, rotation, and scale deviations, while training optimizes the deformation network and Gaussians with image losses.
- The deformation MLP maps Gaussian coordinates and time to deviations in position, rotation, and scaling.It uses eight fully connected ReLU layers with 256-dimensional hidden features.
- The architecture is illustrated as the deformation MLP in Figure 7.
- The deformation field avoids grid- and plane-based structures because their low-rank assumption is unsuitable for dynamic scenes.
- Excessively inaccurate poses can prevent convergence on the training set.
- Training optimizes the deformation network and 3D Gaussians using a combination of L1 and D-SSIM losses.
C.1. Per-Scene Results on the NeRF-DS Dataset
On the NeRF-DS dataset, the method generally outperforms baselines, and annealing smoothing improves metrics across almost every individual scene compared with omitting AST.
- The method outperforms baselines overall on the NeRF-DS dataset.
- AST achieves superior metrics in almost every scene compared with training without AST.The passage presents this as evidence of AST generalizing to real datasets with imperfect poses.
C.2. Results on the HyperNeRF Dataset
On HyperNeRF, imperfect camera poses can make image-quality metrics favor blurred outputs, although the method's renderings often show fewer artifacts and greater clarity.
- PSNR and similar metrics can penalize minor offsets more heavily than blurring on datasets with inaccurate camera poses.
- The method's rendered images often contain fewer artifacts and greater clarity despite inconsistent quantitative metric advantages.
C.3. Results on Rendering Efficiency
The method achieves real-time rendering under moderate Gaussian counts and maintains strong rendering quality and convergence across NeRF-DS scenes. Camera-pose accuracy substantially affects the number of Gaussians required for convergence.
- Over 30 FPS is achieved on an NVIDIA RTX 3090 when the point-cloud count remains below approximately 250k.The FPS evaluation varies the number of 3D Gaussians.
- The method achieves the best rendering quality and robust convergence across most NeRF-DS scenes.The comparison uses the same metrics as the main-text evaluation, with VGG-based LPIPS.
- HyperNeRF reaches 1,000k reconstructed points, attributed to highly inaccurate camera poses, while accurate poses yield more reasonable point-cloud counts.Excessive point counts also occur with sparse viewpoints, such as the D-NeRF Lego scene trained on 50 images.
C.4. More Ablations
The ablations examine architecture, background color, deformation constraints, and failure conditions. Results favor the chosen architecture and black background overall, while exposing sensitivity to camera poses and viewpoint scarcity.
- Network architecture: The chosen implicit network architecture is reported as the most advantageous for the pipeline.The ablation contrasts MLP output components and whether rotation and scaling are included among the inputs.
- Background color: Black backgrounds generally improve rendering metrics, but white backgrounds perform better for the D-NeRF bouncing and trex scenes.The main experiments therefore use a black background for consistency.
- Deformation field: Adding an SE(3) field slightly improves D-NeRF results but reduces NeRF-DS quality and adds about 50% training time and 20% rendering-FPS loss.The method consequently uses direct addition without imposing SE(3) constraints on position transformation.
- Failure cases: Inaccurate camera poses can prevent deformable-GS convergence and create cross-time scene inconsistencies in explicit point-based rendering.Implicit representations are described as more robust to minor pose deviations because of their inherent smoothness.
- Failure cases: Four training viewpoints cause severe overfitting even with 100 training images, whereas swapping to a set with 100 viewpoints produces markedly better results.The limitation affects deformable-GS and 3D-GS on the training set.