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4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

Yuyang Yin, Dejia Xu, Zhangyang Wang, Yao Zhao, Yunchao Wei

arXiv:2312.17225v3cs.CV

TL;DR

Prior 4D pipelines rely on text or image inputs that offer limited motion capability and can require extensive prompt engineering. 4DGen grounds generation in a monocular video, using deformable 3D Gaussians, pseudo-labels, and consistency priors; its experiments report faithful input reconstruction and plausible novel-view and timestep results.

  • Problem

    Prior 4D methods driven directly by text or images have limited motion capabilities and rely on prompt engineering, while monocular-video lifting is ill-posed because multiple 4D scenes can fit the same video.

    Source: §1 · p. 2, §3 · p. 5

  • Method

    4DGen grounds deformable 3D Gaussian scenes in a static 3D asset or monocular video, using multi-view diffusion pseudo-labels and consistency priors for supervision.

    Source: §1 · p. 3, Spatial-temporal 2D Pseudo Labels · p. 8

  • Results

    4DGen outperforms per-frame generation baselines and existing 4D techniques on video-to-4D, faithfully reconstructing inputs and producing plausible renderings from novel viewpoints and timesteps.

    Source: §1 · p. 3

  • Takeaways & Limitations

    Video input gives users increased control over appearance and motion compared with prior image-to-4D and text-to-4D works.

    Source: §1 · p. 3

    Multi-object generation is outside the method’s object-centric diffusion-prior assumption; compositional and scene-level generation remain future work.

    Source: §4.8 · p. 14

Abstract

from arXiv · show

Aided by text-to-image and text-to-video diffusion models, existing 4D content creation pipelines utilize score distillation sampling to optimize the entire dynamic 3D scene. However, as these pipelines generate 4D content from text or image inputs directly, they are constrained by limited motion capabilities and depend on unreliable prompt engineering for desired results. To address these problems, this work introduces \textbf{4DGen}, a novel framework for grounded 4D content creation. We identify monocular video sequences as a key component in constructing the 4D content. Our pipeline facilitates controllable 4D generation, enabling users to specify the motion via monocular video or adopt image-to-video generations, thus offering superior control over content creation. Furthermore, we construct our 4D representation using dynamic 3D Gaussians, which permits efficient, high-resolution supervision through rendering during training, thereby facilitating high-quality 4D generation. Additionally, we employ spatial-temporal pseudo labels on anchor frames, along with seamless consistency priors implemented through 3D-aware score distillation sampling and smoothness regularizations. Compared to existing video-to-4D baselines, our approach yields superior results in faithfully reconstructing input signals and realistically inferring renderings from novel viewpoints and timesteps. More importantly, compared to previous image-to-4D and text-to-4D works, 4DGen supports grounded generation, offering users enhanced control and improved motion generation capabilities, a feature difficult to achieve with previous methods. Project page: https://vita-group.github.io/4DGen/

1 Introduction

4DGen addresses limited motion control in prior 4D generation by grounding creation in a monocular video that specifies appearance and motion. It combines deformable 3D Gaussians with anchor-frame pseudo-labels and consistency regularization; experiments report faithful input reconstruction and plausible novel views and timesteps.

1 Introduction

  • Prior text- or single-image-driven 4D methods can yield limited motion and require extensive prompt engineering, while high-resolution, long-frame rendering with Hexplanes is memory-intensive and can produce artifacts.Super-resolution may leave blurred textures and inconsistent geometry.Source: §1 · p. 2
  • Monocular video provides direct control over generated appearance and motion, addressing limited motion capability and controllability in prior methods; videos may be user-provided or synthesized from an image.The image-to-video-to-4D path uses an image-to-video diffusion model.Source: §1 · pp. 2–3
  • 4DGen uses deformable 3D Gaussians for high-resolution training renderings, with spatial-temporal pseudo-labels injecting motion at anchor frames and consistency priors refining arbitrary views and timesteps.The priors use score distillation sampling and unsupervised smoothness regularization.Source: §1 · p. 3
  • The work presents grounded 4D content generation as its central contribution.Its contributions include video-based control and dynamic 3D Gaussian scene representation.Source: §1 · p. 3
  • 4DGen outperforms per-frame generation baselines and existing 4D techniques on video-to-4D, while faithfully reconstructing inputs and producing plausible novel-view and timestep renderings.Compared with prior image-to-4D and text-to-4D work, it also offers better motion generation and increased user control.Source: §1 · p. 3

2 Related Works

Related work spans explicit and hybrid 3D representations, diffusion-prior methods for 3D generation, and approaches to dynamic content. 4DGen draws on Gaussian rendering and diffusion priors while adding video grounding and anchor-frame pseudo-label supervision.

2.1 3D Representations for Content Creation

  • 3D-GS offers visual quality and real-time rendering, and 4DGen uses 3D Gaussians with an implicit deformation network for dynamic scenes.The section contrasts meshes and point clouds, NeRF, and hybrid representations before motivating Gaussian splatting.Source: §2.1 · pp. 3–4

2.3 Dynamic Content Generation

  • For dynamic 4D generation, 4DGen differs from concurrent methods by using DDIM-sampled pseudo-labels for higher-resolution supervision and Gaussian rasterization for faster training than costly NeRF rendering.Related methods include text- or image-driven generation and lifting monocular video to 4D.Source: §2.3 · p. 5

3 4DGen: Grounded 4D Generation

Because a monocular video can correspond to multiple plausible 4D scenes, 4DGen combines video-grounded reconstruction with diffusion-generated spatial-temporal pseudo-labels and consistency regularization. SDS provides a way to transfer a diffusion prior into an optimized 3D representation through its renderings.

3 4DGen: Grounded 4D Generation

  • Since monocular video alone leaves 4D generation ill-posed, 4DGen uses a pre-trained 3D-aware multi-view diffusion model to provide spatial-temporal pseudo-labels.The framework deforms static 3D Gaussians and combines input reconstruction with anchor-frame labels, SDS, and smoothness regularization.Source: §3 · pp. 5–6, Figure 3 · p. 6

Score Distillation Sampling

  • SDS optimizes 3D parameters through rendered images by distilling a pre-trained diffusion model’s image prior.The rendered image is perturbed with randomly sampled Gaussian noise; y denotes the conditional signal, and classifier-free guidance combines conditional and unconditional model scores.Source: Score Distillation Sampling · p. 6

3D Gaussian Splatting

4DGen builds dynamic scenes from static Gaussians and monocular-video guidance, using anchor-frame pseudo-labels and consistency priors to refine appearance and motion across viewpoints and timesteps.

3D Gaussian Splatting

  • 3D Gaussian Splatting represents scenes explicitly with differentiable Gaussians parameterized by center, covariance, color, and opacity.The color compositing equation uses Gaussian weights and opacity across sampled points.Source: 3D Gaussian Splatting · p. 7

3.2 Static Gaussian Generation

  • Static Gaussians are initialized from SyncDreamer multi-view predictions and jointly optimized with a deformation field rather than kept fixed.Initialization uses DDIM-sampled images, L1 optimization, and coarse geometry from the initial frame; joint optimization can address inconsistencies in multi-view predictions.Source: §3.2 · p. 7

3.3 Spatial-temporal Anchor Frame Pseudo-labels

  • Anchor frames inject explicit 3D motion without video-diffusion priors, while SyncDreamer supplies multi-view geometry at each timestamp for distillation.SyncDreamer’s per-frame predictions serve as pseudo-labels because they do not guarantee consistency across frames or viewpoints, so they warm up the representation at anchor frames rather than directly train the final 4D scene.Source: §3.3 · p. 8, Spatial-temporal 2D Pseudo Labels · p. 8

Spatial-temporal 2D Pseudo Labels

  • SyncDreamer pseudo-labels are limited to anchor-frame warm-up because their independently generated frames and views lack guaranteed consistency.The passage notes that multiple objects can be plausible per frame and that synthesized back views are especially inconsistent.Source: Spatial-temporal 2D Pseudo Labels · p. 8

3.4 Seamless Spatial-temporal Consistency Priors

  • Random frame-rate sampling supports temporal refinement beyond anchor frames, while spatial and temporal smoothness priors constrain intermediate renderings.The temporal prior penalizes individual 3D points’ acceleration, and constant initialization of space-time planes helps preserve features when spatial content stays static.Source: §3.4 · p. 8, Arbitrary Viewpoint Spatial… · p. 9

Arbitrary Viewpoint Spatial Consistency through SDS

  • 3D-aware SDS optimizes rendered images for high likelihood under a diffusion prior, addressing inconsistent pseudo-labels and providing supervision at arbitrary viewpoints.The image-conditioned SDS uses the front view at each timestep; the consistency objective combines smoothness, total variation, and SDS terms.Source: Arbitrary Viewpoint Spatial… · pp. 8–9

4 Experiments

The described setup combines a multi-resolution deformation representation with staged optimization of static Gaussians and their motion field.

Architecture

  • A multi-resolution HexPlane encodes Gaussian deformations across three spatial and three space-time planes, with an MLP decoding features into position changes.The initial resolution is, followed by four levels that each double resolution.Source: Architecture · p. 9
  • 1,000 iterations optimize randomly initialized static Gaussians, followed by 1,000 coarse-stage iterations using Lpseudo and 3,000 fine-stage iterations.A user-provided static 3D asset can instead initialize the Gaussians from its point cloud.Source: Training · p. 10
  • 10% of fine-stage iterations sample arbitrary frame rates and starting timesteps for temporal consistency; the other 90% use anchor-frame SDS at random viewpoints.Fine-stage optimization jointly updates static Gaussians and the deformation field.Source: Training · p. 10

4.2 Baseline Methods

The evaluation compares 4DGen with frame-wise and concurrent 4D-generation baselines, assessing both rendered quality and consistency across viewpoints and time. It uses established image and video metrics alongside qualitative comparisons.

4.2 Baseline Methods

  • DreamGaussian generates T separate 3D models from video frames, while One-2-3-45 generates and fuses multi-view images; both baselines concatenate frame results into a 4D model.The comparisons also include concurrent video-to-4D, image-to-4D, and text-to-4D methods; test cases provide only a front-view reference video.Source: §4.2 · p. 10

4.3 Evaluation Metric

  • The qualitative comparisons contrast 4DGen’s arbitrary-view, arbitrary-timestep rendering with front-view references and baselines that show artifacts, blurring, or limited motion.The image-to-4D comparison uses Stable Video Diffusion-generated input sequences and highlights identity consistency against Animate124.Source: Figure 4 · p. 11, Figure 5 · p. 11, Figure 6 · p. 12
  • Table 1 reports average results across 13 datasets, while the stated evaluation aims to assess quality and consistency across space and time.The supplied table caption gives its scope but not metric values.Source: §4.3 · pp. 10, 12, Table 1 · p. 12
  • The evaluation measures view synthesis with CLIP distance, temporal smoothness with CLIP-T, and video quality with X-CLIP across front, back, side, or 360° views.CLIP-T-f, CLIP-T-b, and CLIP-T-s denote measurements from front, back, and side views; XCLIP-f, XCLIP-b, and XCLIP-r denote front, back, and 360° views.Source: §4.3 · p. 12

4.4 Video-to-4D Qualitative and Quantitative Comparisons

4DGen is reported to outperform the compared baselines across spatial and temporal consistency metrics, while producing more detailed and smoother video results. It also supports arbitrary-length sampling from arbitrary viewpoints, unlike methods limited to input frame counts.

4.4 Video-to-4D Qualitative and Quantitative Comparisons

  • DreamGaussian struggles with fine-grained details, One-2-3-45 produces inaccurate geometry and white texture artifacts, and frame-wise baselines show flickering and texture inconsistencies over time.Consistent4D exhibits color distortion and artifacts, which the authors associate with its low-resolution NeRF and super-resolution post-processing.Source: §4.4 · pp. 12–13
  • 4DGen highly surpasses the compared methods across all metrics on average results from 13 cases, although two Consistent4D metrics were not tested under identical settings.The authors report enhanced details and smooth transitions between frames.Source: §4.4 · p. 13
  • Unlike previous methods limited to the number of input images, 4DGen supports sampling videos of arbitrary length from arbitrary viewpoints.The authors attribute this capability to seamless consistency priors.Source: §4.4 · p. 13

4.5 Image/Text-to-4D Comparisons

For image-to-4D and text-to-4D, 4DGen takes video sequences generated from image inputs and reports stronger identity consistency, detail, and motion than the compared methods.

4.5 Image/Text-to-4D Comparisons

  • For image-to-4D, 4DGen’s grounded results follow the input signals more faithfully than Animate124, which retains identity inconsistency despite using textual inversion.The comparison uses input sequences generated through image-to-video diffusion.Source: §4.5 · p. 13
  • For text-to-4D, 4DGen produces better details and geometry shapes with good consistency and rich motion, whereas 4D-fy is reported to flicker and limit motion variability.The pipeline first generates a front image from text, then uses image-to-video diffusion to create the input video.Source: §4.5 · p. 13

4.6 Training Time and Memory

The authors report that 4DGen trains quickly with less memory than previous methods, while the supplied table caption identifies a separate ablation study.

4.6 Training Time and Memory

  • 4DGen trains for about an hour per scene on one RTX3090 with 15G memory, compared with reported runtimes ranging from 32 minutes to 23 hours for other methods.The cited runtimes use different hardware, including a V100, A100s, and an A6000; the authors report faster training and lower memory requirements overall.Source: §4.6 · p. 13
  • DreamGaussian takes 32 minutes at 2 minutes per frame, while Consistent4D takes 2.75 hours on a V100 with 40G memory.The paper also reports MAV3D at 6.5 hours on 8 A100s, 4D-fy at 23 hours on an A100, and Animate124 at about 4.5 hours on an A6000.Source: §4.6 · p. 13
  • Table 2 is identified as an ablation study of the proposed components.The supplied caption does not give ablation results.Source: Table 2 · p. 14

4.7 Ablation Study

Ablations on the fish dataset show that the full 4DGen model performs best, while removing components degrades detail or temporal smoothness. The method’s scope excludes multi-object generation under its object-centric diffusion-prior assumption.

4.7 Ablation Study

  • On the fish dataset, the full model delivers the best performance among the tested ablations.Removing Lrecon, LSDS, Lpseudo, LTV or Lsmooth produces distorted geometry, flickering, or temporal choppiness.Source: §4.7 · p. 14
  • Without Lrecon, LSDS or Lpseudo, outputs lose high-quality detail; without LTV or Lsmooth, outputs lose smoothness.The reported ablations assess these components on the fish dataset.Source: Figure 7 · p. 14, §4.7 · p. 14

4.8 Limitation

  • Multi-object generation is beyond the framework’s assumption because its diffusion prior is pretrained on object-centric 3D datasets.The authors identify compositional and scene-level generation as future extensions.Source: §4.8 · p. 14

5 Conclusion

4DGen grounds 4D content creation in a static 3D asset or monocular video sequence, giving users control over appearance and motion. Spatial-temporal pseudo labels and consistency regularization support deformable 3D Gaussians rendered at arbitrary viewpoints and timesteps.

5 Conclusion

  • Users can control appearance and motion by specifying a static 3D asset or monocular video sequence.The conclusion frames 4DGen as a pipeline for grounded 4D content creation.Source: §5 · p. 14
  • Spatial-temporal pseudo labels and seamless consistency regularization are used to construct deformable 3D Gaussians.The representation can be rendered from any viewpoint at any timestep.Source: §5 · p. 14
  • The framework presents faithful reconstructions of input signals and visually pleasing novel-view results at arbitrary timesteps.Source: §5 · p. 14

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