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DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors

Tianyu Huang, Haoze Zhang, Yihan Zeng, Zhilu Zhang, Hui Li, Wangmeng Zuo, Rynson W. H. Lau

arXiv:2406.01476v3cs.CV

TL;DR

Dynamic 3D content is difficult because physics-based simulation requires manually assigned material properties, while video-diffusion deformation can produce small, discontinuous motions. DreamPhysics learns a material field from video diffusion priors, uses MPM simulation, and introduces motion distillation sampling plus KAN-based frame boosting; experiments report more realistic motion and high-quality 4D content under image and text conditions.

  • Problem

    Physics-based animation requires precise manual material properties, while video-diffusion deformation often produces small and discontinuous motions.

    Source: Introduction · p. 1, 3D Animation · p. 3

  • Method

    DreamPhysics distills video-diffusion motion priors into a material field, then uses an MPM simulator with motion distillation sampling and a KAN-based material field with frame boosting.

    Source: Introduction · pp. 1–2, Parameter Estimation with Material Field · p. 5

  • Results

    DreamPhysics produces high-quality 4D content with both text and image conditions and more realistic motion than state-of-the-art works.

    Source: Introduction · p. 2, Conclusion · p. 7

  • Takeaways & Limitations

    Combining learned physical properties with physics-based simulation supports realistic dynamic 3D synthesis without pre-generated ground-truth videos.

    Source: Introduction · pp. 1–2

    Current frameworks struggle to combine all motion types in one simulator and handle scene-level interactions beyond a few target objects.

    Source: Conclusion · p. 7

Abstract

from arXiv · show

Dynamic 3D interaction has been attracting a lot of attention recently. However, creating such 4D content remains challenging. One solution is to animate 3D scenes with physics-based simulation, which requires manually assigning precise physical properties to the object or the simulated results would become unnatural. Another solution is to learn the deformation of 3D objects with the distillation of video generative models, which, however, tends to produce 3D videos with small and discontinuous motions due to the inappropriate extraction and application of physics priors. In this work, to combine the strengths and complementing shortcomings of the above two solutions, we propose to learn the physical properties of a material field with video diffusion priors, and then utilize a physics-based Material-Point-Method (MPM) simulator to generate 4D content with realistic motions. In particular, we propose motion distillation sampling to emphasize video motion information during distillation. In addition, to facilitate the optimization, we further propose a KAN-based material field with frame boosting. Experimental results demonstrate that our method enjoys more realistic motions than state-of-the-arts do.

Introduction

DreamPhysics addresses realistic dynamic 3D synthesis by learning material properties from video diffusion priors and applying them in physics-based simulation. It combines motion-focused distillation with a KAN-based material field and frame boosting to generate realistic 4D content under image or text conditioning.

Introduction

  • Manual physical-property assignment can produce unnatural simulation, while direct video-diffusion deformation tends to yield small, discontinuous motions.These limitations motivate learning and applying physics knowledge from video generative models more appropriately.Source: Introduction · p. 1
  • DreamPhysics learns a material field from video diffusion priors and uses an MPM simulator to model time-dependent deformation of 3D Gaussian kernels.This design combines learned physical properties with physics-based motion generation.Source: Introduction · p. 1
  • Motion distillation sampling emphasizes motion information by reducing color-bias interference during video-diffusion distillation.The method is introduced because standard SDS may focus more on color than motion.Source: Introduction · pp. 1–2
  • A KAN-based material field with frame boosting facilitates optimization across the large physical-parameter range and sequential MPM simulation.The contributions also include frame boosting to support training supervision over video frames.Source: Introduction · p. 2
  • DreamPhysics generates high-quality 4D content with either image- or text-conditioned optimization and produces more realistic motion than state-of-the-art works.The method does not require pre-generated ground-truth videos from a video generator.Source: Introduction · p. 2

3D Generation

3D generation methods include supervised approaches that directly produce 3D assets and 2D-lifting approaches that optimize 3D representations using 2D diffusion priors. The section highlights data scarcity for supervised methods and 3D inconsistency risks for 2D lifting.

3D Generation

  • 3D generation methods are broadly divided into 3D supervised and 2D lifting approaches.Supervised methods use text-3D data, whereas 2D lifting methods leverage priors from 2D diffusion models.Source: 3D Generation · pp. 2–3
  • 3D supervised methods directly produce representations such as point clouds, SDFs, and 3DGS from text prompts.Examples include Point-E, Shap-E, and LGM.Source: 3D Generation · p. 2
  • Limited 3D training data constrains supervised methods’ open-world capability relative to image or video generators.The passage states that available 3D datasets are much smaller than 2D or video datasets.Source: 3D Generation · p. 3
  • 2D lifting methods use extensive 2D diffusion priors to optimize 3D representations but are prone to 3D inconsistency known as the Janus problem.DreamFusion introduced score distillation sampling for this class of methods.Source: 3D Generation · p. 3
  • Recent multi-view methods improve consistency by generating images from multiple viewpoints or adding depth information.Zero-1-to-3, MVDream, and Wonder3D represent these strategies.Source: 3D Generation · p. 3

3D Animation

3D animation methods target applications such as games, virtual reality, and robotics, but existing approaches either rely on deformation prediction or require manually assigned physical properties. DreamPhysics instead distills video-diffusion priors into simulation environments to automatically set those properties.

  • 3D animation supports applications including video games, virtual reality, and robotic simulation, but manually creating 4D content is time-consuming and expertise-intensive.Source: 3D Animation · p. 3
  • Video-diffusion methods supervise or distill deformation fields, yet their deformation predictions are inaccurate.Source: 3D Animation · p. 3
  • Physics-based methods model object deformation but require manually setting physical properties before simulation.Source: 3D Animation · p. 3
  • DreamPhysics distills video-diffusion priors into simulation environments, enabling automatic physical-property setting.Source: 3D Animation · p. 3

Preliminaries

The preliminaries describe 3D Gaussian Splatting as a point-based representation, MPM as a compatible continuum simulator, and score-distillation methods as diffusion-prior optimizers for static or dynamic 3D content.

Point-Based Representation

  • 3D Gaussian Splatting represents scenes with Gaussian kernels whose centers, opacities, covariances, and spherical-harmonic coefficients support real-time rendering and interaction.Source: Point-Based Representation · p. 3

Material Point Method

  • MPM represents a continuum with particles in a grid-based space and provides each Gaussian kernel with time-dependent deformation states.Source: Material Point Method · p. 3

Score Distillation Sampling

  • SDS distills pretrained diffusion models into 3D-representation parameters, while SDS-T applies video diffusion to time-dependent deformation using a camera trajectory.Source: Score Distillation Sampling · pp. 3–4
  • DreamPhysics’s overview initializes physical parameters with a KAN-based material field, simulates a 3D video with MPM, and back-propagates motion-distillation gradients to refine them.Source: Figure 2 · p. 4
  • VSD adds a LoRA term that learns the distribution of current 3D scenes and attaches it to the diffusion score.Source: Score Distillation Sampling · p. 4

DreamPhysics

DreamPhysics learns physical parameters for a 3D Gaussian-Splatting scene through video-motion distillation and MPM simulation. Its KAN material field and frame boosting address unstable parameter optimization and limited video-frame supervision.

Method Overview

  • DreamPhysics initializes Gaussian-kernel physical parameters with a KAN material field, simulates time-dependent states using MPM, and iteratively refines them through rendered-video distillation.Source: Method Overview · p. 4

Parameter Optimization with MDS

Parameter Estimation with Material Field

Experiments

The experiments evaluate DreamPhysics on text- and image-conditioned 4D generation against three prior methods, using MPM simulations, video diffusion models, and aesthetic scoring across seven scenes.

Experiments

  • The study presents text- and image-conditioned 4D generation, compares prior methods, and performs ablations of the proposed components.Source: Experiments · p. 5

Experimental Setup

  • Experiments compare DreamPhysics with PhysGaussian, PhysDreamer, and DreamGaussian4D across seven scenes involving rotation and collision motions.Source: Experimental Setup · p. 5
  • Text-conditioned optimization uses ModelScope, image-conditioned optimization uses Stable Video Diffusion, and frame boosting uses M = 5 groups.Source: Experimental Setup · p. 5
  • Video quality is evaluated with VBench’s aesthetic score from 0 to 10, reflecting aspects such as video naturalness.Source: Experimental Setup · p. 5

3D Dynamics Generation

DreamPhysics generates text- and image-conditioned dynamics whose optimized physical properties produce more realistic motion than compared methods. Ablations show that KAN material fields, motion distillation sampling, and frame boosting improve quality or convergence.

3D Dynamics Generation

  • Our results are closest to ground truth in aesthetic quality across four evaluated scenes, while PhysDreamer and DreamGaussian4D score lower.The comparison is limited to four scenes because PhysDreamer’s training implementation was unavailable.Source: 3D Dynamics Generation · p. 6, Table 1 · p. 6
  • Young’s modulus optimization makes ficus swaying more natural, while optimized ball properties reduce excessive deformation during ground collision.Space-time slices show a more realistic optimized trajectory for the ficus.Source: 3D Dynamics Generation · p. 6
  • Compared with previous methods, DreamPhysics produces space-time motion slices closer to ground truth in amplitude and frequency.PhysGaussian often generates extreme motions, whereas DreamGaussian4D lacks physical constraints and PhysDreamer shows some energy dissipation.Source: 3D Dynamics Generation · p. 6, Figure 4 · p. 7

Ablation Study

  • The vanilla SDS-T baseline has the worst score and convergence, while adding a KAN-based material field improves optimization and generation quality.The ablation evaluates seven scenes using aesthetic quality and training iterations.Source: Ablation Study · p. 6, Table 2 · p. 6
  • Motion distillation sampling further improves aesthetic quality, and the final method reaches faster optimization within 30 training iterations.Frame boosting targets parameter convergence rather than optimization quality, so the final score remains similar to the motion-distillation variant.Source: Ablation Study · pp. 6–7, Table 2 · p. 6
  • Ablation space-time slices show that motion distillation sampling and the final method are closer to ground truth than earlier variants.The visual results are consistent with the quantitative ablation results.Source: Ablation Study · p. 7, Figure 5 · p. 7

Conclusion

DreamPhysics learns physical properties for 3D Gaussian Splatting from video diffusion priors and uses motion distillation sampling with a KAN-based material field and frame boosting. Experiments show high-quality 4D content under text and image conditions, while broader motion integration and scene-level interaction remain open problems.

Conclusion

  • DreamPhysics learns 3D Gaussian Splatting physical properties from video diffusion priors for physics-based dynamic content.Source: Conclusion · p. 7
  • Motion distillation sampling transfers motion priors into physical parameters, while a KAN-based material field with frame boosting facilitates optimization.Source: Conclusion · p. 7
  • Experiments demonstrate high-quality 4D content under both text and image conditions.Source: Conclusion · p. 7
  • Current systems struggle to combine independently constrained motions and support scene-level interactions involving environments such as changing shadows.The authors identify these issues as future-work directions.Source: Conclusion · p. 7

Additional Experiments

The additional experiments assess user preferences and the KAN material-field design. User studies favor DreamPhysics, while KAN improves collision behavior compared with an MLP encoder.

User Study

  • The study evaluates generated videos using realism and coherence preferences from 28 volunteers, including five 3D-art professionals.Participants selected their preferred clip from four alternatives in each scenario.Source: User Study · p. 11
  • DreamPhysics is the most favorable method in both state-of-the-art comparisons and ablation experiments.The user-study results are generally consistent with the aesthetic-quality evaluation metric.Source: User Study · p. 11

Ablation Study on KAN

  • In collision scenarios, an MLP encoder makes the optimized ball too soft, whereas KAN preserves its original shape.The ablation replaces KAN layers with a classic MLP encoder in the final method.Source: Ablation Study on KAN · p. 11

Video Visualization Results

  • Generated videos are provided in supplementary HTML and MP4 files for direct visualization.The supplementary materials identify results.html and the videos folder as access points.Source: Video Visualization Results · p. 11

Experimental Details

The experiments use seven scenes spanning elastic-object motion and collisions, evaluate realism with VBench aesthetic quality and user studies, and provide supplementary code and pseudocode.

Dataset Scenes

  • The seven experimental scenes include elastic-object swaying, a dropping ball, and complicated cushion-and-pillow collisions.Scenes are drawn from PhysDreamer, PhysGaussian, and an LGM-generated ball setup.Source: Dataset Scenes · p. 11

Evaluation Metrics

  • Aesthetic quality is used because conventional image metrics and FVD emphasize frame content rather than motion fidelity.The authors state that VBench motion smoothness also poorly discriminates physics-based simulations because all methods exceed 0.99.Source: Evaluation Metrics · p. 11
  • The user study complements aesthetic quality by evaluating the realism and coherence of generated videos.The study is reported in Table 3.Source: Evaluation Metrics · p. 11

Code and Pseudocode

  • Supplementary materials provide the implementation code and Algorithm 1 pseudocode.The code is located in the “code” folder.Source: Code and Pseudocode · p. 11

Theoretical Details

The theoretical details describe MPM as a particle-grid simulator and SDS as a diffusion-prior optimization method. DreamPhysics combines these operations by extracting material properties, simulating motion, rendering videos, and applying motion distillation.

Material Point Method

  • MPM couples material particles carrying physical state with a background grid for spatial derivatives and external-force application.The method combines particle-based and grid-based computation for deformable-material simulation.Source: Material Point Method · p. 11
  • MPM alternates Particle-to-Grid transfer, grid updates from forces and stresses, and Grid-to-Particle updates of velocity and deformation.Particle mass and momentum are interpolated to grid nodes, after which updated quantities return to the particles.Source: Material Point Method · p. 12
  • The MPM formulation updates particle velocity, position, affine velocity gradient, and deformation gradient after grid computation.The supplied formulation identifies these as the particle quantities updated during Grid-to-Particle transfer.Source: Material Point Method · p. 12
  • MPM supports large deformations, fractures, and complex interactions while combining Lagrangian particles with Eulerian grid computation.The passage also notes applications to solids, fluids, granular media, and textiles, with GPU-parallelization suitability.Source: Material Point Method · p. 12

Score Distillation Sampling

  • DreamPhysics initializes a material field and MPM simulator, extracts physical parameters at Gaussian locations, and simulates time-dependent states.The simulator outputs particle positions, covariance-related states, and opacity-related states for the Gaussian representation.Source: Score Distillation Sampling · p. 13
  • Motion distillation samples viewpoints and diffusion timesteps, renders video frames, groups them into videos, and applies a motion-distillation score during optimization.The algorithm repeats these steps until convergence and then returns the optimized physical parameters.Source: Score Distillation Sampling · p. 13
  • SDS optimizes differentiable 3D parameters so rendered images align with plausible outputs from a frozen 2D diffusion model.Its gradient uses predicted noise, actual noise, and the image Jacobian with respect to 3D parameters.Source: Score Distillation Sampling · p. 13

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