Source-linked AI summary
DreamGaussian4D: Generative 4D Gaussian Splatting
Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, Ziwei Liu
TL;DR
Existing 4D generation methods face long optimization times, limited motion controllability, and insufficient detail quality. DG4D combines static Gaussian Splatting, HexPlane-based deformation, driving-video motion, and video-to-video texture refinement; it reduces generation to minutes while supporting controllable motion and temporally coherent mesh textures. Its quality remains below many photorealistic methods and depends substantially on the quality of the driving video.
Problem
Existing 4D generation methods suffer from long optimization times, limited motion controllability, and low detail quality, while consistency and high quality remain unresolved.
Method
DG4D uses static 3D Gaussian Splatting with HexPlane deformation, driving-video motion learning, and optional video-to-video texture refinement for image-to-4D generation.
Results
DG4D reduces optimization from several hours to minutes, generates a 14-frame animation in approximately 6.5 minutes, or 10 minutes with optional texture refinement, and enables explicit 3D-motion control.
Takeaways & Limitations
DG4D supports controllable 4D content generation, mesh extraction, and temporally coherent texture optimization for real-world applications.
Takeaways & Limitations
Generation quality is below that of many photorealistic 4D methods, and quality depends substantially on having a good-quality driving video.
Abstract
from arXiv · showhide
4D content generation has achieved remarkable progress recently. However, existing methods suffer from long optimization times, a lack of motion controllability, and a low quality of details. In this paper, we introduce DreamGaussian4D (DG4D), an efficient 4D generation framework that builds on Gaussian Splatting (GS). Our key insight is that combining explicit modeling of spatial transformations with static GS makes an efficient and powerful representation for 4D generation. Moreover, video generation methods have the potential to offer valuable spatial-temporal priors, enhancing the high-quality 4D generation. Specifically, we propose an integral framework with two major modules: 1) Image-to-4D GS - we initially generate static GS with DreamGaussianHD, followed by HexPlane-based dynamic generation with Gaussian deformation; and 2) Video-to-Video Texture Refinement - we refine the generated UV-space texture maps and meanwhile enhance their temporal consistency by utilizing a pre-trained image-to-video diffusion model. Notably, DG4D reduces the optimization time from several hours to just a few minutes, allows the generated 3D motion to be visually controlled, and produces animated meshes that can be realistically rendered in 3D engines.
1 Introduction
DG4D addresses unresolved consistency and quality challenges in 4D generation with an efficient framework combining explicit Gaussian-based representations, controllable driving-video motion, and video-to-video texture refinement. It generates 14-frame animations in approximately 6.5 minutes, or 10 minutes with optional refinement, while improving quality and shortening optimization relative to existing methods.
- Existing 4D generation methods have not fully resolved consistency and high-quality generation for dynamic 3D scenes.
- DG4D combines static 3D Gaussian Splatting with HexPlane-based dynamic displacement maps and initializes the static scene using DreamGaussianHD.
- 6.5 minutes produces a 14-frame 4D animation, increasing to 10 minutes with optional video-to-video texture refinement.
- Driving-video motion learning enables flexible selection and visual control of expected 3D motions while maintaining motion diversity.
- Video-to-video texture refinement improves the quality and temporal consistency of exported animated mesh sequences.
2 Related works
Related work covers image-to-3D generation, dynamic 4D scene representations, and methods for generating dynamic 3D content. These approaches use image features, deformation-based representations, and video diffusion models, while facing limitations in quality and computational cost.
- Image-to-3D Generation: Image-to-3D methods create 3D assets from a single reference image, commonly using diffusion models or image features.Point-E and Shap-E generate point clouds or NeRFs, but their quality is limited by spatial resolution and high-quality 3D dataset availability.
- 4D Representations: 4D scene representations model spatial coordinates with time or latent codes, or deform static canonical 3D scenes through deformation fields.Extensive computational time is identified as a primary challenge in 4D representations.
- 4D Generation: 4D generation targets dynamic 3D scenes for applications including animation, gaming, and virtual reality.Recent methods use text-to-video diffusion models, HexPlane or K-plane representations, camera trajectories, and SDS computed on rendered videos.
3 Our Approach
DG4D combines static 3D Gaussian Splatting with time-dependent deformation and video-driven refinement to generate controllable 4D scenes efficiently. Its pipeline improves static detail, models motion explicitly, exports animated meshes, and enhances temporal texture consistency.
- Image-to-4D GS Generation: DG4D generates static 3D GS with DreamGaussianHD, then estimates timestamp-specific Gaussian deformations from a driving video.The deformation field is optimized against driving-video reconstruction and diffusion-based SDS objectives.
- DreamGaussianHD for Static Generation: DreamGaussianHD improves static generation through multi-view optimization and a fixed white background, producing more detailed geometry and textures.Sampling more views improves unseen-region geometry, while black backgrounds introduce noise and blurriness.
- Gaussian Deformation for Dynamic Generation: Users control motion by selecting a driving video generated or chosen independently from the later optimization process.This supports flexible motion selection and diversity while preserving temporal consistency through HexPlane regularization.
- Gaussian Deformation for Dynamic Generation: HexPlane predicts each Gaussian’s displacement, rotation, and scale from spatial coordinates and time, while its resolution regularizes motion smoothness.The representation decomposes the 4D field into six coordinate-pair feature planes.
- Gaussian Deformation for Dynamic Generation: Zero-initializing the final deformation layers preserves the static model at training start and avoids undesirable appearance changes during dynamic optimization.Residual connections keep the network trainable despite the zero-deformation initialization.
- Video-to-Video Texture Refinement: Video-to-video refinement updates UV-space textures across all time steps and produces smoother temporal changes than independent image-to-image refinement.The refinement loss is back-propagated through the rendered video to improve texture maps while reducing adjacent-frame flicker.
4 Experiments
Experiments evaluate DG4D across image-to-4D, video-to-4D, motion representations, texture refinement, controllability, and deployment-oriented mesh rendering. Results indicate strong qualitative performance, efficient optimization, and improved temporal consistency, while ablations identify HexPlane and tuned resolutions as important choices.
- Experimental Setup: DG4D generates 14-frame sequences with static optimization taking 2 minutes and dynamic optimization taking 4.5 minutes on one A100 GPU.The implementation uses Stable Video Diffusion for driving-video generation and 500 static plus 200 dynamic iterations.
- Image-to-4D: DG4D achieves better faithfulness to input images, larger motions, and greater details than Animate124 in qualitative image-to-4D comparisons.The image-to-4D study evaluates image alignment, 3D appearance, and motion quality using 12 images and 38 participants.
- Motion Representation: HexPlane with static initialization achieves the best motion-generation performance among evaluated representations.Framewise fitting is time-intensive, while MLP-based motion can produce misalignments and local jitter.
- Mesh Texture Refinement: Video-to-Video refinement enhances UV texture quality and consistency across extracted mesh sequences.The refinement addresses noise and artifacts in mesh textures, while V2V optimization alleviates temporal flickering seen with image-to-image optimization.
- Advantages and Applications: Different driving videos produce distinct 3D motions for the same input image, enabling controllable and diverse motion generation.DG4D can export 4D Gaussian Splatting into textured meshes and compose dynamic scenes in engines such as Blender.
5 Conclusion
DG4D is presented as an efficient 4D Gaussian Splatting framework that reduces optimization time, enables motion control, and supports coherent textured mesh extraction. The authors also identify lower photorealistic quality and dependence on driving-video quality as limitations.
- Conclusion: DG4D reduces 4D-generation optimization time from several hours to minutes while enabling explicit control of generated 3D motion.The framework also supports mesh extraction and temporally coherent texture optimization for practical deployment.
- Limitations: DG4D's generation quality is not as high as that of many photorealistic 4D-generation methods.This limitation bounds the method's visual-quality advantage despite its efficiency.
- Limitations: DG4D assumes a good-quality driving video, whose quality substantially influences the resulting 4D generation quality.The limitation concerns the input motion source rather than only the optimization procedure.
- Broader Impacts: The approach is intended for applications including videos, gaming, and mixed reality, but the authors caution against malicious impersonation.The broader-impacts statement combines an application scope with a misuse warning.
A More implementation details
The implementation initializes static Gaussian Splatting with fixed Gaussian, resolution, and optimization settings. The dynamic representation uses 32 spatial and temporal resolution with specified grid and learning-rate parameters.
- Static Initialization: Static 3D Gaussian Splatting is initialized with 5,000 Gaussians without Spherical Harmonics.DreamGaussianHD uses dense percentage 0.1, densification interval 100, and gradient threshold 0.05.
- Dynamic Representation: The dynamic representation uses spatial and temporal resolutions of 32, grid feature dimension 32, and no multi-resolution hierarchy.The MLP and HexPlane grid learning rates are 0.00064 and 0.0064, respectively.
A.3 Evaluation settings.
Evaluation covers image-to-4D alignment, 3D appearance, and motion quality, plus video-to-4D similarity from novel views. The protocol uses specified reference images, videos, viewpoints, and comparison assets.
- Image-to-4D Evaluation: The image-to-4D user study evaluates image alignment, 3D appearance, and motion quality.These axes ask participants to compare input resemblance, geometry and visual quality, and naturalness of motion.
- Image-to-4D Evaluation: CLIP-I image alignment is computed using eight images provided on the official website.The metric compares CLIP image embeddings between reference-view renders and the reference image.
- Alternative Static Initialization: LGM can replace DreamGaussianHD for static 3D generation, with azimuth selection based on minimum RGB-space L2 distance to the input image.The selected model is rendered from the chosen azimuth and three orthogonal viewpoints to obtain four multiview images.
B More quantitative results
The paper reports detailed evaluation metrics for the video-to-4D benchmark in Table 5.
- Table 5 reports detailed evaluation metrics for the video-to-4D benchmark.
C More qualitative results
Additional image-to-4D qualitative results are presented with renders across changing timestamps and view angles.
- Figure 15 presents additional image-to-4D renders across gradually changing timestamps and view angles.
C.1 More qualitative result on video to 4D
Additional video-to-4D qualitative results are shown from multiple novel views and timesteps.
- Figure 16 shows video-to-4D renders from two novel views at two timesteps.
D More Ablations and Analysis
The paper analyzes motion representations and temporal loss, while presenting qualitative video-to-4D results alongside these ablations.
- D More Ablations and Analysis: The deformation-field ablation compares a pure MLP network with frame-wise Gaussians initialized from static 3D GS.
- D More Ablations and Analysis: Figure 16 provides qualitative video-to-4D results using two novel views at two timesteps.
- D More Ablations and Analysis: Adding the temporal loss described by [30] produces no significant improvement in the reported comparison.
- D More Ablations and Analysis: Figure 17 presents an ablation on motion representation.
- D More Ablations and Analysis: Figure 18 shows the temporal-loss comparison.