pith. sign in

arxiv: 2606.23455 · v1 · pith:7MKBGRYDnew · submitted 2026-06-22 · 💻 cs.CV

MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing

Zesong Yang , Yuanhang Lei , Liyuan Cui , Yihang Chen , Jiaer Huang , Boming Zhao , Peter Yichen Chen , Hujun Bao
show 1 more author
Zhaopeng Cui
This is my paper

Pith reviewed 2026-06-26 08:59 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingthermomechanical dynamicsphase transitionsscene editingphysics simulationneural renderingtemperature attributes
0
0 comments X

The pith

MeGAS adds temperature attributes and thermomechanical dynamics to 3D Gaussian Splatting for realistic phase-change simulation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes MeGAS to incorporate temperature-driven phase changes into neural rendering using 3D Gaussian Splatting. It combines a heat advection-diffusion solver with material point method dynamics to model thermophysical processes like melting and solidification. This allows physically consistent behavior in edited scenes while keeping photorealistic quality. The approach includes a topology-adaptive rendering to handle large deformations. A sympathetic reader would care because it bridges photorealistic reconstruction with physics-based animation for more realistic virtual worlds.

Core claim

MeGAS is a framework that augments 3D Gaussian Splatting with temperature attributes and employs a heat advection-diffusion solver with MPM dynamics incorporating phase transitions, enabling physically plausible and visually realistic synthesis of thermophysical phenomena, with a topology-adaptive Gaussian rendering strategy to mitigate artifacts under extreme deformation.

What carries the argument

thermomechanical dynamic Gaussian Splatting representation that augments standard 3DGS with temperature attributes and integrates heat advection-diffusion solving with MPM dynamics for phase transitions

If this is right

  • Produces physically consistent thermomechanical behavior in rendered scenes
  • Maintains high-fidelity photorealistic rendering during dynamic changes
  • Enables synthesis of phenomena such as melting and solidification
  • Advances the development of physics-integrated world models

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Scenes could be edited by adjusting temperature parameters to trigger phase changes without manual modeling
  • This method might generalize to other physical factors beyond temperature if similar solvers are added
  • Real-time applications could benefit if the computational overhead of the dynamics solver is optimized

Load-bearing premise

The topology-adaptive Gaussian rendering strategy successfully mitigates cracking and floaters under extreme deformation caused by phase transitions.

What would settle it

A test rendering where phase transitions cause visible cracking, floaters, or temperature dynamics that do not match physical expectations in the visual output.

Figures

Figures reproduced from arXiv: 2606.23455 by Boming Zhao, Hujun Bao, Jiaer Huang, Liyuan Cui, Peter Yichen Chen, Yihang Chen, Yuanhang Lei, Zesong Yang, Zhaopeng Cui.

Figure 1
Figure 1. Figure 1: Thermophysical editing and photorealistic rendering of real scenes. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Thermo-geometry evolution with MeGAS. Under user-specified heat￾ing source, MeGAS performs thermomechanically consistent phase-change simulation, steering the evolution of temperature fields and shape and delivering controllable, tar￾geted melting without compromising geometric smoothness. struction with physically grounded realism. Early works [12, 24] embed contin￾uum and elastodynamic models into NeRF, … view at source ↗
Figure 3
Figure 3. Figure 3: System overview. Starting from posed RGB sequences, we reconstruct the scene with 3DGS. To mitigate floaters and interior artifacts under extreme deforma￾tion, we introduce an interior-free uniform-Gaussian regularization that suppresses anisotropy and prunes unsupported interior splats. We then provide volumetric sup￾port via ray-tracing–based internal filling. Next, we augment 3DGS with per-Gaussian temp… view at source ↗
Figure 4
Figure 4. Figure 4: Naïve combination fails under extreme topology changes. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Our ray-tracing internal filling. Starting from an interior-free, uniformly distributed GS model, we cast rays from uniform grids and de￾tect interior samples by enforcing di￾rectional consistency with Gaussian ray-traced normals. This produces uniform, well-conditioned volumetric filling for physically plausible melting. Ray-Tracing-Based Internal Filling. To enable a physically consistent melt￾ing simula… view at source ↗
Figure 6
Figure 6. Figure 6: Implicit-surface–guided adaptive densification. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Melting-style scene editing comparisons. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Extreme melting deformation comparisons. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Ablations on Topology-Adaptive strategy. [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ablations on phase￾change switching. Without phase change, melting is triggered globally and collapses prematurely. Deform with 𝑇! Deform with 𝑇! Init State & Temp. W. Small Diffusion Ratio W. Large Diffusion Ratio Init State & Temp. W. Small Viscosity -> 10 W. Large Viscosity -> 100 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Bidirectional phase-change editing. Controlled heating induces melting, while subsequent cooling drives solidification, demonstrating consistent thermomechan￾ical evolution across phase transitions and enabling general thermophysical editing [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Scalability to complex scenes. Our method naturally supports multi￾object interactions with multi-material and collisions (e.g., an ice cream ball). Please view the dynamic videos in Adobe Acrobat Reader! driven solidification shown in [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
read the original abstract

Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene reconstruction and physics-based animation. However, existing approaches focus on mechanically driven dynamics while neglecting temperature, a fundamental yet invisible physical factor underlying phenomena such as melting, solidification, and other thermomechanical processes. In this paper, we propose MeGAS, a novel framework that incorporates thermomechanical phase-change dynamics into 3D Gaussian Splatting (3DGS). Specifically, we propose a new thermomechanical dynamic Gaussian Splatting representation that augments 3DGS with temperature attributes and employs a heat advection-diffusion solver with MPM dynamics incorporating phase transitions, enabling physically plausible and visually realistic synthesis of thermophysical phenomena. Furthermore, a new topology-adaptive Gaussian rendering strategy is proposed to mitigate cracking and floaters under extreme deformation. Extensive experiments demonstrate that MeGAS produces physically consistent thermomechanical behavior while maintaining high-fidelity photorealistic rendering, advancing toward physics-integrated world models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper proposes MeGAS, a framework integrating thermomechanical phase-change dynamics into 3D Gaussian Splatting (3DGS). It augments 3DGS with temperature attributes, employs a heat advection-diffusion solver combined with MPM dynamics that incorporate phase transitions, and introduces a topology-adaptive Gaussian rendering strategy to mitigate cracking and floaters under extreme deformation. The central claim is that extensive experiments demonstrate physically consistent thermomechanical behavior alongside high-fidelity photorealistic rendering, advancing physics-integrated world models.

Significance. If the technical claims hold, the work would meaningfully extend recent integrations of Newtonian dynamics with neural rendering by incorporating temperature as a driver of phase changes (e.g., melting, solidification). This addresses a gap in existing mechanically focused approaches and could enable more realistic thermophysical scene editing and animation.

major comments (1)
  1. [Abstract] Abstract: the claim that 'extensive experiments demonstrate physically consistent thermomechanical behavior' cannot be evaluated because the provided manuscript text contains no methods section, equations, implementation details, quantitative results, figures, or tables.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and the opportunity to clarify the manuscript. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'extensive experiments demonstrate physically consistent thermomechanical behavior' cannot be evaluated because the provided manuscript text contains no methods section, equations, implementation details, quantitative results, figures, or tables.

    Authors: The full manuscript contains a complete Methods section that details the thermomechanical dynamic Gaussian representation, the heat advection-diffusion solver, MPM dynamics with phase transitions, the topology-adaptive rendering strategy, implementation specifics, quantitative metrics, ablation studies, and multiple figures and tables. The abstract is a concise summary of those results. If the version under review was truncated or missing these sections, we will ensure the complete manuscript is supplied; otherwise the claims are directly supported by the provided content. revision: no

Circularity Check

0 steps flagged

No significant circularity in provided text

full rationale

The abstract presents a high-level description of a novel framework integrating temperature attributes, heat advection-diffusion solver, MPM dynamics with phase transitions, and topology-adaptive Gaussian rendering into 3DGS. No equations, parameter-fitting procedures, self-citations, or derivation steps are supplied that could reduce any claimed result to its own inputs by construction. Without access to methods, equations, or citations in the manuscript, no load-bearing circular steps of the enumerated kinds can be identified or quoted. The derivation chain is therefore self-contained at the level of the given text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be extracted from a full technical description.

pith-pipeline@v0.9.1-grok · 5726 in / 1098 out tokens · 35140 ms · 2026-06-26T08:59:53.903976+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

49 extracted references · 5 canonical work pages · 3 internal anchors

  1. [1]

    GPT-4 Technical Report

    Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  2. [2]

    In: Proceedings Visualization, 2001

    Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.T.: Point set surfaces. In: Proceedings Visualization, 2001. VIS’01. pp. 21–29. IEEE (2001)

    2001

  3. [3]

    Fluid Flow Handbook1, 1–49 (2002)

    Ashgriz, N., Mostaghimi, J.: An introduction to computational fluid dynamics. Fluid Flow Handbook1, 1–49 (2002)

    2002

  4. [4]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    2022

  5. [5]

    Cambridge University Press (2000)

    Batchelor, G.K.: An Introduction to Fluid Dynamics. Cambridge University Press (2000)

    2000

  6. [6]

    Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

    Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., et al.: Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 (2023)

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  7. [7]

    IEEE Transactions on Visualization and Computer Graphics (2024)

    Chen, D., Li, H., Ye, W., Wang, Y., Xie, W., Zhai, S., Wang, N., Liu, H., Bao, H., Zhang, G.: Pgsr: Planar-based gaussian splatting for efficient and high-fidelity sur- face reconstruction. IEEE Transactions on Visualization and Computer Graphics (2024)

    2024

  8. [8]

    In: European Conference on Computer Vision

    Chen, M., Laina, I., Vedaldi, A.: Dge: Direct gaussian 3d editing by consistent multi-view editing. In: European Conference on Computer Vision. pp. 74–92. Springer (2024)

    2024

  9. [9]

    In: European Conference on Computer Vision

    Chen, Q., Shu, S., Bai, X.: Thermal3d-gs: Physics-induced 3d gaussians for thermal infrared novel-view synthesis. In: European Conference on Computer Vision. pp. 253–269. Springer (2024)

    2024

  10. [10]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Dai, Q., Ni, X., Shen, Q., Chen, W., Chen, B., Chu, M.: Rainygs: Efficient rain synthesis with physically-based gaussian splatting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16153–16162 (2025)

    2025

  11. [11]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Feng, Y., Feng, X., Shang, Y., Jiang, Y., Yu, C., Zong, Z., Shao, T., Wu, H., Zhou, K., Jiang, C., et al.: Gaussian splashing: Unified particles for versatile motion syn- thesis and rendering. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 518–529 (2025)

    2025

  12. [12]

    In: IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition

    Feng, Y., Shang, Y., Li, X., Shao, T., Jiang, C., Yang, Y.: Pie-nerf: Physics-based interactive elastodynamics with nerf. In: IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition. pp. 4450–4461 (2024)

    2024

  13. [13]

    Physical Review E65(4), 046308 (2002)

    Guo, Z., Zheng, C., Shi, B.: Discrete lattice effects on the forcing term in the lattice boltzmann method. Physical Review E65(4), 046308 (2002)

    2002

  14. [14]

    In: 2025 International Conference on 3D Vision (3DV)

    Hsu, H.Y., Lin, C.H., Zhai, A.J., Xia, H., Wang, S.: Autovfx: Physically realistic video editing from natural language instructions. In: 2025 International Conference on 3D Vision (3DV). pp. 769–780. IEEE (2025) MeGAS: Thermomechanical Dynamic Gaussian Splatting 17

    2025

  15. [15]

    ACM Transactions on Graphics37(4), 1–14 (2018)

    Hu, Y., Fang, Y., Ge, Z., Qu, Z., Zhu, Y., Pradhana, A., Jiang, C.: A moving least squares material point method with displacement discontinuity and two-way rigid body coupling. ACM Transactions on Graphics37(4), 1–14 (2018)

    2018

  16. [16]

    In: ACM SIGGRAPH 2024 Conference Papers

    Huang, B., Yu, Z., Chen, A., Geiger, A., Gao, S.: 2d gaussian splatting for geo- metrically accurate radiance fields. In: ACM SIGGRAPH 2024 Conference Papers. pp. 1–11 (2024)

    2024

  17. [17]

    ACM Transactions on Graphics34(4), 1–10 (2015)

    Jiang, C., Schroeder, C., Selle, A., Teran, J., Stomakhin, A.: The affine particle- in-cell method. ACM Transactions on Graphics34(4), 1–10 (2015)

    2015

  18. [18]

    In: ACM SIGGRAPH 2024 Conference Papers

    Jiang, Y., Yu, C., Xie, T., Li, X., Feng, Y., Wang, H., Li, M., Lau, H., Gao, F., Yang, Y., et al.: Vr-gs: A physical dynamics-aware interactive gaussian splatting system in virtual reality. In: ACM SIGGRAPH 2024 Conference Papers. pp. 1–1 (2024)

    2024

  19. [19]

    ACM Transactions on Graphics42(4) (July 2023),https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

    Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics42(4) (July 2023),https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

    2023

  20. [20]

    Klár, G., Gast, T., Pradhana, A., Fu, C., Schroeder, C., Jiang, C., Teran, J.: Drucker-pragerelastoplasticityforsandanimation.ACMTransactionsonGraphics 35(4), 1–12 (2016)

    2016

  21. [21]

    Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: Benchmarking large-scalescenereconstruction.ACMTransactionsonGraphics36(4),1–13(2017)

    2017

  22. [22]

    ACM Transactions on Algorithms (TALG)4(2), 1–25 (2008)

    Kolluri, R.: Provably good moving least squares. ACM Transactions on Algorithms (TALG)4(2), 1–25 (2008)

    2008

  23. [23]

    In: International Conference on Learning Representations

    Lei,Y.,Zhao,B.,Yang,Z.,Li,X.,Cheng,T.,Peng,H.,Zhang,R.,Yang,Y.,Huang, S., Shen, Y., et al.: Diffwind: Physics-informed differentiable modeling of wind- driven object dynamics. In: International Conference on Learning Representations

  24. [24]

    arXiv preprint arXiv:2303.05512 (2023)

    Li, X., Qiao, Y.L., Chen, P.Y., Jatavallabhula, K.M., Lin, M., Jiang, C., Gan, C.: Pac-nerf: Physics augmented continuum neural radiance fields for geometry- agnostic system identification. arXiv preprint arXiv:2303.05512 (2023)

    work page arXiv 2023

  25. [25]

    Li,Y.,Lin,Z.H.,Forsyth,D.,Huang,J.B.,Wang,S.:Climatenerf:Extremeweather synthesisinneuralradiancefield.In:IEEE/CVFInternationalConferenceonCom- puter Vision. pp. 3227–3238 (2023)

    2023

  26. [26]

    In: International Conference on Learning Representations

    Lin, Y., Lin, C., Xu, J., MU, Y.: Omniphysgs: 3d constitutive gaussians for gen- eral physics-based dynamics generation. In: International Conference on Learning Representations

  27. [27]

    In: 2024 IEEE International Conference on Computational Pho- tography (ICCP)

    Lin, Y.Y., Pan, X.Y., Fridovich-Keil, S., Wetzstein, G.: Thermalnerf: Thermal radiance fields. In: 2024 IEEE International Conference on Computational Pho- tography (ICCP). pp. 1–12. IEEE (2024)

    2024

  28. [28]

    In: European Conference on Computer Vision

    Liu, S., Ren, Z., Gupta, S., Wang, S.: Physgen: Rigid-body physics-grounded image-to-video generation. In: European Conference on Computer Vision. pp. 360–

  29. [29]

    arXiv preprint arXiv:2409.07200 (2024)

    Lu, R., Chen, H., Zhu, Z., Qin, Y., Lu, M., Zhang, L., Yan, C., Xue, A.: Thermal- gaussian: Thermal 3d gaussian splatting. arXiv preprint arXiv:2409.07200 (2024)

    work page arXiv 2024

  30. [30]

    ACM Transactions on Graphics44(4) (Jul 2025).https://doi.org/10

    Luo, Z., Cui, Z., Luo, S., Chu, M., Li, M.: Vr-doh: Hands-on 3d modeling in virtual reality. ACM Transactions on Graphics44(4) (Jul 2025).https://doi.org/10. 1145/3731154

    2025

  31. [31]

    Commu- nications of the ACM65(1), 99–106 (2021)

    Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Commu- nications of the ACM65(1), 99–106 (2021)

    2021

  32. [32]

    ACM Transactions on Graphics (2024) 18 Z

    Moenne-Loccoz, N., Mirzaei, A., Perel, O., de Lutio, R., Martinez Esturo, J., State, G., Fidler, S., Sharp, N., Gojcic, Z.: 3d gaussian ray tracing: Fast tracing of particle scenes. ACM Transactions on Graphics (2024) 18 Z. Yang et al

    2024

  33. [33]

    In: European Confer- ence on Computer Vision

    Özer, M., Weiherer, M., Hundhausen, M., Egger, B.: Exploring multi-modal neural scene representations with applications on thermal imaging. In: European Confer- ence on Computer Vision. pp. 82–98. Springer (2024)

    2024

  34. [34]

    In: Computer Graphics Forum

    Oztireli, C., Guennebaud, G., Gross, M.: Feature preserving point set surfaces based on non-linear kernel regression. In: Computer Graphics Forum. vol. 28, pp. 493–501. Wiley (2009)

    2009

  35. [35]

    In: European Conference on Computer Vision (2024)

    Qiu, R.Z., Yang, G., Zeng, W., Wang, X.: Language-driven physics-based scene synthesis and editing via feature splatting. In: European Conference on Computer Vision (2024)

    2024

  36. [36]

    Runway AI, Inc.: Introducing runway gen–4.https://runwayml.com/research/ introducing-runway-gen-4.5(2025), accessed: 2025-12-1

    2025

  37. [37]

    In: International Conference on Learning Representations (ICLR) (2026)

    Shen, Q., Tao, N., Dai, Q., Chen, T., Qin, M., Zhang, Y., Chu, M., Chen, W., Chen, B.: Fierygs: In-the-wild fire synthesis with physics-integrated gaussian splatting. In: International Conference on Learning Representations (ICLR) (2026)

    2026

  38. [38]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Shen, S., Shao, T., Zhou, K., Jiang, C., Yang, Y.: Enliveninggs: Active locomotion of 3dgs. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 896–905 (2025)

    2025

  39. [39]

    Oxford University Press (2001)

    Succi, S.: The Lattice Boltzmann Equation: For Fluid Dynamics and Beyond. Oxford University Press (2001)

    2001

  40. [40]

    In: Advances in Neural Information Processing Systems (2025)

    Vasile, F., Qiu, R.Z., Natale, L., Wang, X.: Gaussian-augmented physics simula- tion and identification with complex colliders. In: Advances in Neural Information Processing Systems (2025)

    2025

  41. [41]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Wan, Team, Wang, A., Ai, B., Wen, B., Mao, C., Xie, C.W., Chen, D., Yu, F., Zhao, H., Yang, J., et al.: Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  42. [42]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Wang, M., Zhang, Y., Xu, W., Ma, R., Zou, C., Morris, D.: Decoupledgaussian: Object-scene decoupling for physics-based interaction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11361–11372 (2025)

    2025

  43. [43]

    In: International Confer- ence on Learning Representations (2026),https://openreview.net/forum?id= P2Nw2LMkjH

    Wang, Z., Ling, H., Zhang, W., Sun, Y., Sun, Q.: ETGS: Explicit thermodynamics gaussian splatting for dynamic thermal reconstruction. In: International Confer- ence on Learning Representations (2026),https://openreview.net/forum?id= P2Nw2LMkjH

    2026

  44. [44]

    In: IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition

    Xie, T., Zong, Z., Qiu, Y., Li, X., Feng, Y., Yang, Y., Jiang, C.: Physgaussian: Physics-integrated 3d gaussians for generative dynamics. In: IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition. pp. 4389–4398 (2024)

    2024

  45. [45]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Yang, K., Liu, Y., Cui, Z., Liu, Y., Zhang, M., Yan, S., Wang, Q.: Ntr-gaussian: Nighttime dynamic thermal reconstruction with 4d gaussian splatting based on thermodynamics. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 691–700 (2025)

    2025

  46. [46]

    In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Ye, T., Wu, Q., Deng, J., Liu, G., Liu, L., Xia, S., Pang, L., Yu, W., Pei, L.: Thermal-nerf: Neural radiance fields from an infrared camera. In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1046–

    2024

  47. [47]

    ACM Transactions on Graphics 34(5), 1–20 (2015)

    Yue, Y., Smith, B., Batty, C., Zheng, C., Grinspun, E.: Continuum foam: A ma- terial point method for shear-dependent flows. ACM Transactions on Graphics 34(5), 1–20 (2015)

    2015

  48. [48]

    In: European Conference on Computer Vision

    Zhong,L.,Yu,H.X.,Wu,J.,Li,Y.:Reconstructionandsimulationofelasticobjects with spring-mass 3d gaussians. In: European Conference on Computer Vision. pp. 407–423. Springer (2024) MeGAS: Thermomechanical Dynamic Gaussian Splatting 19

    2024

  49. [49]

    In: SIGGRAPH Asia 2023 Conference Papers

    Zong, Z., Li, X., Li, M., Chiaramonte, M.M., Matusik, W., Grinspun, E., Carlberg, K., Jiang, C., Chen, P.Y.: Neural stress fields for reduced-order elastoplasticity and fracture. In: SIGGRAPH Asia 2023 Conference Papers. pp. 1–11 (2023)

    2023