Pith. sign in

REVIEW 4 major objections 5 minor 72 references

Instruct-4DGS: Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A dynamic scene can be edited from a text instruction by changing only its static 3D Gaussians and then refining with score distillation, cutting editing time from about two hours on two GPUs to about forty minutes on one GPU while…

desk verdict A genuinely useful efficiency idea for 4DGS editing that is undercut by a metrics table measuring the wrong thing; worth refereeing after major revision. read the letter →

arxiv 2502.02091 v3 pith:U35PRU4A submitted 2025-02-04 cs.CV

Joohyun Kwon , Hanbyel Cho , Junmo Kim This is my paper · ORCID
classification cs.CV
keywords 4DGaussianSplattingdynamicsceneeditinginstruction-guidedscoredistillationsamplingstatic-dynamicseparationdeformationfieldtemporalconsistencyInstructPix2Pix
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Instruct-4DGS claims that a text-instruction edit of a 4D dynamic scene can be done without touching the motion side of the representation. The paper's target is the existing recipe that edits thousands of 2D frames across every timestep and then retrains the whole scene, which takes hours and does not scale with sequence length. Instead, the authors edit only the static canonical 3D Gaussians of a 4D Gaussian Splatting scene, using InstructPix2Pix-edited images from a single timestep, and then run a short score-distillation refinement to remove artifacts caused by recombining the edited Gaussians with the original deformation field. The reported outcome is an edited dynamic scene in about 40 minutes on one GPU, down from two hours on two GPUs, with better instruction following and lower perceptual error. If correct, editing cost stops scaling with the number of timesteps.

What carries the argument

The engine is 4D Gaussian Splatting's static-dynamic separation: a set of canonical 3D Gaussians (positions, scales, rotations, opacities, and spherical-harmonic colors) encodes the static scene, and a Hexplane-based deformation field—a compact spatio-temporal encoding built from six factorized planes and a small MLP decoder—produces per-Gaussian position, scale, and rotation offsets for each timestep. Stage 1 edits only the canonical Gaussians, supervised by InstructPix2Pix edits of the first timestep's multiview images. Stage 2 renders the pseudo-edited dynamic scene at random cameras and timesteps and optimizes the edited Gaussians against a score distillation loss delivered by Coherent-IP2P, an InstructPix2Pix variant whose self-attention is replaced by cross-attention so editing guidance stays consistent across a batch; this fixes artifacts caused by shifted Gaussian positions and previously hidden spherical-harmonic colors becoming exposed in later timesteps.

What would settle it

Render the edited scene from a fixed camera across every timestep for an edit that changes only appearance, such as 'paint it in the style of Van Gogh'; if any timestep beyond the first shows flicker, ghosting, or surfaces whose old colors become visible, the claim that score-based refinement of the static component alone restores alignment is wrong. An even sharper test is an edit that changes an object's position or shape, which the frozen deformation field cannot represent without artifacts.

Watch

Extended reading notes

Core claim

The central claim is that appearance editing of a dynamic scene separates cleanly from its motion: only the static canonical 3D Gaussians need to change. The paper argues that this static component is the minimal but sufficient part for visual editing, and that the original Hexplane-based deformation field can be left frozen provided a second stage uses score distillation with a coherence-preserving variant of InstructPix2Pix to pull the edited Gaussians back into alignment with that field. The recipe is: edit the canonical Gaussians with an L1 RGB loss against edited first-timestep multiview images, then refine only those Gaussians with score distillation losses computed from renders at random cameras and timesteps, keeping the deformation field untouched. On DyNeRF and Technicolor scenes the paper reports higher CLIP instruction similarity and consistently lower LPIPS than the previous Instruct 4D-to-4D baseline, at two to three times lower cost on a single GPU.

Load-bearing premise

The load-bearing premise is that the original deformation field still describes the motion correctly after the static Gaussians have been edited, so that fixing temporal misalignment requires only optimizing those edited Gaussians and never updating the motion field.

Editorial extensions

If this is right

  • Editing cost stops scaling with the number of timesteps: only one timestep's multiview images are ever edited, so a longer video costs about the same as a short one.
  • A single GPU at about 40 minutes replaces the two-GPU, two-hour baseline pipeline, and the gap widens as the temporal dimension grows.
  • Instruction following, measured by CLIP similarity, is better than the baseline's, and perceptual error (LPIPS) is consistently lower even where PSNR and SSIM are comparable or slightly worse.
  • Score distillation works as a temporal-alignment fix on top of an already-trained scene, not just as a text-to-3D generation tool, so the diffusion prior can be distilled into an edited scene in a few hundred iterations.
  • Refining the deformation field during the temporal-alignment stage hurts temporal coherence, so the paper's recipe keeps it frozen and refines only the static component.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same edit-static-then-distill recipe should transfer to any scene representation that separates a canonical appearance model from a frozen motion field, not just 4D Gaussian Splatting, with similar savings.
  • Because the method inherits InstructPix2Pix's strengths and limits, it can restyle or recolor but cannot change motion, and partial edits need user-provided segmentation; a video-diffusion prior with stronger temporal modeling could extend it to motion-altering edits.
  • A natural stress test the paper does not run is to edit scenes with large object or camera motion and measure per-timestep consistency; the fixed deformation field's validity should degrade as motion magnitude grows, defining the boundary of the speedup.
  • The refinement stage consumes only random renders, so the pipeline could plausibly run directly in latent space with a cheaper scene representation, pushing the 40-minute budget lower.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Instruct-4DGS, a method for instruction-guided editing of dynamic scenes represented with 4D Gaussian Splatting. The key idea is to edit only the static canonical 3D Gaussians, using IP2P-edited first-timestep multiview images as supervision, and then apply a score-distillation refinement stage with a coherence-modified IP2P to fix temporal artifacts while keeping the Hexplane deformation field fixed. The paper reports quantitative metrics on DyNeRF (Table 1), an efficiency comparison (Table 2), qualitative comparisons, and an ablation with a user study (Fig. 8). The central claims are that this reduces editing time by more than half compared to Instruct 4D-to-4D and produces comparable or better visual quality.

Significance. If the claims hold, this is a practically valuable result: it decouples appearance editing from the deformation field, avoids per-timestep dataset updates, and reduces editing time from hours to tens of minutes on a single GPU. The representation choice is well motivated, the pipeline is clearly described, and the qualitative comparisons and ablations give plausible evidence for the design. The manuscript also frankly states limitations such as dependence on IP2P and inability to edit motion. However, the quantitative evidence for the temporal dimension is currently incomplete, so the strength of the central quality claim is not yet established.

major comments (4)
  1. [Section 5.2, Table 1] As stated in Section 5.2, PSNR/SSIM/LPIPS are measured between the rendered edited dynamic scene and 'the 2D multiview images used as supervision for dynamic scene editing.' For Instruct-4DGS, those supervision images are exactly the first-timestep IP2P-edited multiview images created in Section 4.2; no later-timestep edited images are ever produced. Table 1 therefore measures how well the edited scene reproduces the Stage-1 targets, not temporal alignment or editing quality at later timesteps, and it cannot support the claim that score-based refinement 'resolves motion artifacts' (Section 4.3, Fig. 4). Please report metrics disaggregated by timestep (for example t=0 versus later timesteps) or compare against independently edited reference frames at multiple timesteps.
  2. [Section 5.2, Table 2] The efficiency comparison is underspecified. It is not clear whether the reported 40 minutes includes the 4DGS training of Section 4.1, the Stage-1 editing of Section 4.2, and the Stage-2 refinement of Section 4.3; nor is the number of timesteps and cameras used for the measurement, nor whether the baseline 2-hour figure was obtained by rerunning the baseline on the same hardware and scenes or by citing the original paper. The abstract says 'reducing editing time by more than half' while Section 5.2 says '2-3 times faster'; these statements should be reconciled with a single, precisely defined timing protocol.
  3. [Section 4.3, Fig. 8(c)] The core design choice of fixing the original deformation field is supported only by a qualitative comparison showing that refining {E,D} is harmful. No quantitative temporal-consistency metric is provided for either variant, and the mechanism by which refining the deformation field introduces artifacts is not analyzed. Since the entire efficiency gain rests on never updating the deformation field, this claim needs a stronger quantitative check, such as timestep-disaggregated metrics or a controlled user study with statistical analysis.
  4. [Section 5.2, Fig. 8] The ablation user study reports only 50 total preference rankings with no confidence intervals or significance tests, and the exact question asked of participants is not stated. This limits the strength of the claims that Coherent-IP2P reduces blur and that refining the deformation field is harmful. Please provide per-variant preference counts, error bars, and a significance test, or explicitly frame Fig. 8 as qualitative evidence only.
minor comments (5)
  1. [Section 1, contributions bullet] The bullet reads 'modifying only static 3D Guassians'; 'Guassians' should be 'Gaussians'.
  2. [Equations (1) and (2)] The diffusion timestep is denoted \tilde t while t is used for the scene timestep; this dual use is confusing, especially in Eq. (1) where the expectation subscripts mix t and \tilde t. Please use distinct symbols such as \tau for the diffusion timestep.
  3. [Section 4.2] The description of Coherent-IP2P as replacing 'the 2D convolutional layer (self-attention module)' with 'a 3D convolutional layer (cross-attention module)' conflates convolution and attention. Please clarify whether the change is in attention modules, convolutional layers, or both.
  4. [Table 1] The baseline label 'I4D24D' is obscure; use 'Instruct 4D-to-4D' or a clearly defined abbreviation.
  5. [Figure 8] The pie charts would be easier to interpret with actual counts and a explicit description of what the 1st-4th ranks represent; the current caption only states proportions of ranks.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the editing pipeline is empirically benchmarked against an external baseline; the only concern is that Table 1's metrics are computed on stage-1 supervision views, which is an evaluation caveat rather than a circular derivation.

full rationale

The paper's central derivation chain is empirical rather than deductive: it adopts 4DGS's static/dynamic factorization from prior external work, fits canonical Gaussians to IP2P-edited first-timestep images, and then refines them with an SDS objective using an external 2D diffusion prior. No equation in the paper defines the claimed result in terms of its own output, and no load-bearing step is justified solely by a self-citation. The efficiency claim is checked against an external baseline (Instruct 4D-to-4D) with reported GPU time, and the visual quality claim is compared with that baseline plus a 50-participant user study and qualitative timestep comparisons. The closest thing to a circularity concern is the quantitative protocol in Sec. 5.2: PSNR/SSIM/LPIPS are computed against the same first-timestep multiview images that served as the stage-1 supervision targets, so those numbers partly measure fit to training data rather than independent temporal quality, and they cannot by themselves substantiate the dynamic-sequence claim for t>0. However, the paper does not call these held-out predictions, and the temporal claim is separately supported by qualitative evidence and the user study; this is an evaluation limitation rather than a derivation that reduces to its own inputs. The stated limitations (e.g., motion artifacts remaining after refinement) further indicate the authors do not assert a forced or theorem-backed outcome.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim rests on several empirical assumptions that are not proven in the paper: that 4DGS cleanly separates static appearance from motion, that first-timestep IP2P edits are sufficient supervision, that SDS can fix artifacts without touching the deformation field, and that the chosen metrics reflect user intent. The free parameters are standard diffusion and training hyperparameters chosen by hand; none of them is derived from first principles. No new physical or representational entities are postulated beyond the modified Coherent-IP2P module, which reuses existing cross-attention machinery.

free parameters (4)
  • Text CFG scale s_T = 8.5 to 10.5
    Chosen by hand per instruction; directly controls how strongly the edit follows the text and therefore affects all quality metrics.
  • Image CFG scale s_I = 1.2
    Chosen by hand and held fixed; controls the influence of the input image condition in IP2P during editing and refinement.
  • Stage 1 iterations = 800 to 1000
    Selected depending on editing style; affects convergence of the L1 RGB edit on static Gaussians.
  • Stage 2 iterations = about 800
    Selected as sufficient for refinement; more iterations would improve quality but cost time, so it is a trade-off parameter.
assumptions (6)
  • domain assumption The 4DGS representation with canonical 3D Gaussians plus a Hexplane deformation field faithfully captures the target dynamic scene and supports clean static/dynamic separation.
    Invoked in Sections 4.1 and 4.2; if the decomposition is not faithful, editing static Gaussians will not propagate correctly through the deformation field.
  • domain assumption Edited first-timestep multiview images, produced by Coherent-IP2P, provide sufficient supervision for appearance editing across all timesteps.
    Section 4.2 relies on this to avoid editing images at every timestep; the paper gives no proof that first-frame edits generalize to all times.
  • domain assumption Score distillation from Coherent-IP2P transfers the 2D editing prior into 4D and can repair temporal misalignment while preserving motion.
    Section 4.3 uses SDS as the refinement mechanism; whether this converges to a temporally consistent result is an empirical assumption supported only by ablations.
  • domain assumption Keeping the original deformation field fixed, rather than refining it, is optimal or at least sufficient for temporal consistency.
    The ablation in Figure 8(c) shows refining the deformation field hurts, but this is evidence on a few scenes, not a general principle.
  • domain assumption LPIPS and CLIP similarity are valid proxies for perceptual editing quality and instruction faithfulness.
    Used as the main quantitative evidence in Table 1; these metrics are known to be imperfect and are not accompanied by human evaluation statistics.
  • standard math 3D Gaussian splatting rendering is differentiable, so gradients can flow from rendered images to Gaussian parameters.
    Used throughout Sections 4.1 to 4.3 for both L1 and SDS losses; standard in the field.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Instruct-4DGS: Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation." pith.science (2026). https://pith.science/paper/U35PRU4A

@misc{pith2026250202091,
  author       = {Pith},
  title        = {Pith review of: Instruct-4DGS: Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U35PRU4A}},
  note         = {Machine review of arXiv:2502.02091}
}
read the original abstract

Recent 4D dynamic scene editing methods require editing thousands of 2D images used for dynamic scene synthesis and updating the entire scene with additional training loops, resulting in several hours of processing to edit a single dynamic scene. Therefore, these methods are not scalable with respect to the temporal dimension of the dynamic scene (i.e., the number of timesteps). In this work, we propose Instruct-4DGS, an efficient dynamic scene editing method that is more scalable in terms of temporal dimension. To achieve computational efficiency, we leverage a 4D Gaussian representation that models a 4D dynamic scene by combining static 3D Gaussians with a Hexplane-based deformation field, which captures dynamic information. We then perform editing solely on the static 3D Gaussians, which is the minimal but sufficient component required for visual editing. To resolve the misalignment between the edited 3D Gaussians and the deformation field, which may arise from the editing process, we introduce a refinement stage using a score distillation mechanism. Extensive editing results demonstrate that Instruct-4DGS is efficient, reducing editing time by more than half compared to existing methods while achieving high-quality edits that better follow user instructions. Code and results: https://hanbyelcho.info/instruct-4dgs/

Figures

Figures reproduced from arXiv: 2502.02091 by the authors.

Figure 1
Figure 1. Illustration of dynamic scene editing processes for baseline and our method: (a) The existing method requires up￾dating the 2D images for all timesteps. (b) In contrast, our method updates only the first timestep’s dataset images, edits canonical 3D Gaussians, and efficiently completes dynamic scene editing through score-based temporal refinement. For a multi-camera dataset with T = 300, our method reduces editing t… view at source ↗
Figure 2
Figure 2. Overview of 4D Gaussian Splatting: 4DGS represents dynamic scenes by separating static (canonical 3D Gaussians Gcanon) and dynamic components (Gaussian deformation field  E(·), D(·) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overall pipeline of our proposed dynamic scene editing method (Instruct-4DGS): To obtain the target dynamic scene for editing, we first optimize the 4D Gaussians using a multi-camera captured video dataset (Sec. 4.1). We then perform 3D Gaussian editing on the static canonical 3D Gaussians by editing only the multiview images corresponding to the first timestep (Sec. 4.2). We apply score￾based temporal refinement to… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Our dynamic scene editing method effectively fol￾lows various editing styles based on the provided instruc￾tions. Leveraging the capabilities of 4DGS [63], each ren￾dered image exhibits high fidelity, accurately capturing the target details. Moreover, the rendered vide…
Figure 6
Figure 6. Figure 6: Qualitative comparison of visual quality: We compare our method with the baseline [38] on DyNeRF [28] coffee martini and sear steak scenes, as well as Technicolor [50]’s Painter and Train scenes. See supplementary for more results [PITH_FULL_IMAGE:figures/full_fig_p00…
Figure 8
Figure 8. Figure 8: Ablation study of the dynamic scene editing method: Each pie chart shows the proportion of user preferences (1st-4th ranks) for each method variant. Our proposed method (denoted as “Ours (w/o refine {E, D})”) achieves the highest preference score. Splatting (4DGS) and …
Figure 7
Figure 7. Figure 7: Qualitative comparison of temporal consistency: The baseline shows noticeable flickering artifacts across timesteps. In contrast, Instruct-4DGS effectively avoids such artifacts by editing only the static component with score-based temporal refinement. 3D Gaussians, ex…
Figure 9
Figure 9. Figure 9: Qualitative comparison of visual quality on the DyCheck [16] dataset (a monocular dataset): We compare our method, Instruct-4DGS (ours), with the baseline, Instruct 4D-to-4D [38] (baseline), on the mochi-high-five scene from the DyCheck dataset. 2 [PITH_FULL_IMAGE:fig…
Figure 10
Figure 10. Figure 10: Qualitative results of our Instruct-4DGS on the HyperNeRF [41] dataset (a monocular dataset): We evaluate our method on the Interp chickchicken scene from the HyperNeRF dataset [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Qualitative results of our Instruct-4DGS under various camera poses on the DyNeRF [28] dataset: We render the edited dynamic scene from novel camera poses to evaluate the spatial consistency of our method. Our Instruct-4DGS produces view-consistent and geometrically p…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

72 extracted references · 50 canonical work pages

  1. [1]

    Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, and David B. Lindell. 4d-fy: Text-to-4d generation using hybrid score distillation sampling. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 2, 3

    work page 2024

  2. [2]

    Lumiere: A space-time diffusion model for video generation, 2024

    Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Her- rmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Guanghui Liu, Amit Raj, Yuanzhen Li, Michael Rubinstein, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, and Inbar Mosseri. Lumiere: A space-time diffusion model for video generation, 2024. 3

    work page 2024

  3. [3]

    Align your latents: High-resolution video synthesis with la- tent diffusion models

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dock- horn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with la- tent diffusion models. In IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR), 2023. 3

    work page 2023

  4. [4]

    Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning to follow image editing instruc- tions. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 18392– 18402, 2023. 1, 3, 5, 6

    work page 2023

  5. [5]

    Hexplane: A fast representa- tion for dynamic scenes

    Ang Cao and Justin Johnson. Hexplane: A fast representa- tion for dynamic scenes. CVPR, 2023. 2, 3, 4

    work page 2023

  6. [6]

    Chan, Connor Z

    Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3D generative adversarial networks. In CVPR, 2022. 4

    work page 2022

  7. [7]

    Dge: Direct gaussian 3d editing by consistent multi-view editing

    Minghao Chen, Iro Laina, and Andrea Vedaldi. Dge: Direct gaussian 3d editing by consistent multi-view editing. arXiv preprint arXiv:2404.18929, 2024. 3

    arXiv 2024

  8. [8]

    Gaussianeditor: Swift and control- lable 3d editing with gaussian splatting

    Yiwen Chen, Zilong Chen, Chi Zhang, Feng Wang, Xi- aofeng Yang, Yikai Wang, Zhongang Cai, Lei Yang, Huaping Liu, and Guosheng Lin. Gaussianeditor: Swift and control- lable 3d editing with gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21476–21485, 2024. 3

    work page 2024

Show all 72 references
  1. [9]

    Progressive3d: Progres- sively local editing for text-to-3d content creation with com- plex semantic prompts

    Xinhua Cheng, Tianyu Yang, Jianan Wang, Yu Li, Lei Zhang, Jian Zhang, and Li Yuan. Progressive3d: Progres- sively local editing for text-to-3d content creation with com- plex semantic prompts. In The Twelfth International Confer- ence on Learning Representations, 2024. 3

    2024

  2. [10]

    Plug and play language models: A simple approach to controlled text generation

    Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. Plug and play language models: A simple approach to controlled text generation. In International Conference on Learning Representations, 2020. 1

    2020

  3. [11]

    ViCA-neRF: View- consistency-aware 3d editing of neural radiance fields

    Jiahua Dong and Yu-Xiong Wang. ViCA-neRF: View- consistency-aware 3d editing of neural radiance fields. In Thirty-seventh Conference on Neural Information Process- ing Systems, 2023. 3

    2023

  4. [12]

    Tenen- baum, and Jiajun Wu

    Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenen- baum, and Jiajun Wu. Neural radiance flow for 4d view synthesis and video processing. In Proceedings of the IEEE/CVF International Conference on Computer Vision ,

  5. [13]

    4d-rotor gaussian splatting: Towards efficient novel view synthesis for dynamic scenes

    Yuanxing Duan, Fangyin Wei, Qiyu Dai, Yuhang He, Wen- zheng Chen, and Baoquan Chen. 4d-rotor gaussian splatting: Towards efficient novel view synthesis for dynamic scenes. In Proc. SIGGRAPH, 2024. 2

    2024

  6. [14]

    Fast dynamic radiance fields with time-aware neural vox- els

    Jiemin Fang, Taoran Yi, Xinggang Wang, Lingxi Xie, Xi- aopeng Zhang, Wenyu Liu, Matthias Nießner, and Qi Tian. Fast dynamic radiance fields with time-aware neural vox- els. In SIGGRAPH Asia 2022 Conference Papers, New York, NY , USA, 2022. Association for Computing Machinery. 4

    2022

  7. [15]

    K-planes: Explicit radiance fields in space, time, and appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In CVPR, 2023. 2, 3, 4

    2023

  8. [16]

    Monocular dynamic view synthesis: A reality check

    Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, and Angjoo Kanazawa. Monocular dynamic view synthesis: A reality check. In NeurIPS, 2022. 6, 1, 2

    2022

  9. [17]

    Motion-aware 3d gaussian splatting for effi- cient dynamic scene reconstruction, 2024

    Zhiyang Guo, Wengang Zhou, Li Li, Min Wang, and Houqiang Li. Motion-aware 3d gaussian splatting for effi- cient dynamic scene reconstruction, 2024. 2

    2024

  10. [18]

    Instruct-nerf2nerf: Edit- ing 3d scenes with instructions

    Ayaan Haque, Matthew Tancik, Alexei Efros, Aleksander Holynski, and Angjoo Kanazawa. Instruct-nerf2nerf: Edit- ing 3d scenes with instructions. In Proceedings of the IEEE/CVF International Conference on Computer Vision ,

  11. [19]

    Delta de- noising score

    Amir Hertz, Kfir Aberman, and Daniel Cohen-Or. Delta de- noising score. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 2328–2337,

  12. [20]

    Classifier-free diffusion guidance, 2022

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance, 2022. 6

    2022

  13. [21]

    Denoising diffu- sion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffu- sion probabilistic models. CoRR, abs/2006.11239, 2020. 1, 3

    2006 arXiv

  14. [22]

    Video dif- fusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video dif- fusion models. arXiv:2204.03458, 2022. 3

    2022 arXiv

  15. [23]

    Instruct 3d-to-3d: Text in- struction guided 3d-to-3d conversion

    Hiromichi Kamata, Yuiko Sakuma, Akio Hayakawa, Masato Ishii, and Takuya Narihira. Instruct 3d-to-3d: Text in- struction guided 3d-to-3d conversion. arXiv preprint arXiv:2303.15780, 2023. 3

    2023 arXiv

  16. [24]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 2, 3, 4

    2023

  17. [25]

    Collaborative score distil- lation for consistent visual editing

    Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong, Kihyuk Sohn, and Jinwoo Shin. Collaborative score distil- lation for consistent visual editing. In Advances in Neural Information Processing Systems, 2023. 3

    2023

  18. [26]

    Auto-encoding varia- tional bayes, 2022

    Diederik P Kingma and Max Welling. Auto-encoding varia- tional bayes, 2022. 6

    2022

  19. [27]

    Posterior distil- lation sampling

    Juil Koo, Chanho Park, and Minhyuk Sung. Posterior distil- lation sampling. In CVPR, 2024. 3

    2024

  20. [28]

    Neural 3d video synthesis from multi-view video

    Tianye Li, Mira Slavcheva, Michael Zollh¨ofer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, and 9 Zhaoyang Lv. Neural 3d video synthesis from multi-view video. In Proceedings of the IEEE/CVF Conference on Com...

    2022

  21. [29]

    Focaldreamer: Text- driven 3d editing via focal-fusion assembly, 2023

    Yuhan Li, Yishun Dou, Yue Shi, Yu Lei, Xuanhong Chen, Yi Zhang, Peng Zhou, and Bingbing Ni. Focaldreamer: Text- driven 3d editing via focal-fusion assembly, 2023. 3

    2023

  22. [30]

    Spacetime gaus- sian feature splatting for real-time dynamic view synthesis

    Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. Spacetime gaus- sian feature splatting for real-time dynamic view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 8508–8520,

  23. [31]

    Gaufre: Gaussian deformation fields for real-time dynamic novel view synthesis, 2023

    Yiqing Liang, Numair Khan, Zhengqin Li, Thu Nguyen- Phuoc, Douglas Lanman, James Tompkin, and Lei Xiao. Gaufre: Gaussian deformation fields for real-time dynamic novel view synthesis, 2023. 2

    2023

  24. [32]

    Magic3d: High-resolution text-to-3d content creation

    Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), 2023. 3

    2023

  25. [33]

    Align your gaussians: Text-to-4d with dynamic 3d gaussians and composed diffusion models

    Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fi- dler, and Karsten Kreis. Align your gaussians: Text-to-4d with dynamic 3d gaussians and composed diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 8576–8588,

  26. [34]

    Devrf: Fast deformable voxel radiance fields for dynamic scenes

    Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Devrf: Fast deformable voxel radiance fields for dynamic scenes. arXiv preprint arXiv:2205.15723, 2022. 2

    2022 arXiv

  27. [35]

    Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024

    Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jian- feng Gao, Lifang He, and Lichao Sun. Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024. 3

    2024

  28. [36]

    Dynamic 3d gaussians: Tracking by per- sistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by per- sistent dynamic view synthesis. In 3DV, 2024. 2

    2024

  29. [37]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. In ECCV, 2020. 1

    2020

  30. [38]

    In- struct 4d-to-4d: Editing 4d scenes as pseudo-3d scenes using 2d diffusion

    Linzhan Mou, Jun-Kun Chen, and Yu-Xiong Wang. In- struct 4d-to-4d: Editing 4d scenes as pseudo-3d scenes using 2d diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20176– 20185, 2024. 1, 2, 3, 5, 6, 7, 8

    2024

  31. [39]

    Instant neural graphics primitives with a multires- olution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a multires- olution hash encoding. ACM Trans. Graph. , 41(4):102:1– 102:15, 2022. 4

    2022

  32. [40]

    Barron, Sofien Bouaziz, Dan B Goldman, Steven M

    Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. ICCV, 2021. 2

    2021

  33. [41]

    Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields

    Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin- Brualla, and Steven M Seitz. Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021. 6, 1, 3

    2021 arXiv

  34. [42]

    Scalable diffusion mod- els with transformers

    William Peebles and Saining Xie. Scalable diffusion mod- els with transformers. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision (ICCV) , pages 4195–4205, 2023. 1

    2023

  35. [43]

    Barron, and Ben Milden- hall

    Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Milden- hall. Dreamfusion: Text-to-3d using 2d diffusion. In The Eleventh International Conference on Learning Representa- tions, 2023. 2, 3, 5

    2023

  36. [44]

    D-NeRF: Neural Radiance Fields for Dynamic Scenes

    Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-NeRF: Neural Radiance Fields for Dynamic Scenes. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, 2020. 2

    2020

  37. [45]

    Learning transferable visual models from natural language supervision, 2021

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021. 6

    2021

  38. [46]

    Hierarchical text-conditional image gener- ation with clip latents, 2022

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- ation with clip latents, 2022. 1

    2022

  39. [47]

    Dreamgaussian4d: Genera- tive 4d gaussian splatting

    Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Genera- tive 4d gaussian splatting. arXiv preprint arXiv:2312.17142,

    arXiv

  40. [48]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, 2022. 1, 3

    2022

  41. [49]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), page...

    2023

  42. [50]

    Dataset and pipeline for multi-view light-field video

    Neus Sabater, Guillaume Boisson, Benoit Vandame, Paul Kerbiriou, Frederic Babon, Matthieu Hog, Remy Gendrot, Tristan Langlois, Olivier Bureller, Arno Schubert, and Va- lerie Allie. Dataset and pipeline for multi-view light-field video. In Proceedings of the IEEE Conference on ...

    2017

  43. [51]

    Tensor4d: Efficient neu- ral 4d decomposition for high-fidelity dynamic reconstruc- tion and rendering

    Ruizhi Shao, Zerong Zheng, Hanzhang Tu, Boning Liu, Hongwen Zhang, and Yebin Liu. Tensor4d: Efficient neu- ral 4d decomposition for high-fidelity dynamic reconstruc- tion and rendering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023. 2

    2023

  44. [52]

    Control4d: Efficient 4d portrait editing with text

    Ruizhi Shao, Jingxiang Sun, Cheng Peng, Zerong Zheng, Boyao Zhou, Hongwen Zhang, and Yebin Liu. Control4d: Efficient 4d portrait editing with text. 2024. 2 10

    2024

  45. [53]

    MVDream: Multi-view diffusion for 3d gen- eration

    Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. MVDream: Multi-view diffusion for 3d gen- eration. In The Twelfth International Conference on Learn- ing Representations, 2024. 2, 3, 5, 7

    2024

  46. [54]

    Make-a-video: Text-to-video generation without text-video data

    Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. Make-a-video: Text-to-video generation without text-video data. In The Eleventh International Conference on Lea...

    2023

  47. [55]

    Nerf- player: A streamable dynamic scene representation with de- composed neural radiance fields.IEEE Transactions on Visu- alization and Computer Graphics , 29(5):2732–2742, 2023

    Liangchen Song, Anpei Chen, Zhong Li, Zhang Chen, Lele Chen, Junsong Yuan, Yi Xu, and Andreas Geiger. Nerf- player: A streamable dynamic scene representation with de- composed neural radiance fields.IEEE Transactions on Visu- alization and Computer Graphics , 29(5):2732–2742, ...

    2023

  48. [56]

    Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction

    Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5459–5469, 2022. 4

    2022

  49. [57]

    Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation. InThe Twelfth International Con- ference on Learning Representations, 2024. 3

    2024

  50. [58]

    Raft: Recurrent all-pairs field transforms for optical flow (extended abstract)

    Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow (extended abstract). In Proceed- ings of the Thirtieth International Joint Conference on Ar- tificial Intelligence, IJCAI-21 , pages 4839–4843. Interna- tional Joint Conferences on Artificia...

    2021

  51. [59]

    Non- rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video

    Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollh¨ofer, Christoph Lassner, and Christian Theobalt. Non- rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video. InIEEE International Conference on Computer Visi...

  52. [60]

    Imagedream: Image-prompt multi-view diffusion for 3d generation

    Peng Wang and Yichun Shi. Imagedream: Image-prompt multi-view diffusion for 3d generation. arXiv preprint arXiv:2312.02201, 2023. 3

    2023 arXiv

  53. [61]

    Bovik, H.R

    Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing , 13(4): 600–612, 2004. 6

    2004

  54. [62]

    Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 3

    2023

  55. [63]

    4d gaussian splatting for real-time dynamic scene render- ing

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4d gaussian splatting for real-time dynamic scene render- ing. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 2...

    2024

  56. [64]

    Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

    Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In Proceedings of the IEEE/CVF International Conference...

    2023

  57. [65]

    Tiger: Text-instructed 3d gaussian retrieval and coherent editing

    Teng Xu, Jiamin Chen, Peng Chen, Youjia Zhang, Jun- qing Yu, and Wei Yang. Tiger: Text-instructed 3d gaussian retrieval and coherent editing. arXiv preprint arXiv:2405.14455, 2024. 3

    2024 arXiv

  58. [66]

    Real- time photorealistic dynamic scene representation and render- ing with 4d gaussian splatting

    Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang. Real- time photorealistic dynamic scene representation and render- ing with 4d gaussian splatting. In International Conference on Learning Representations (ICLR), 2024. 2

    2024

  59. [67]

    Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models

    Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In CVPR,

  60. [68]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 3836–3847, 2023. 1

    2023

  61. [69]

    Efros, Eli Shecht- man, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2018. 6

    2018

  62. [70]

    Dreameditor: Text-driven 3d scene editing with neural fields

    Jingyu Zhuang, Chen Wang, Liang Lin, Lingjie Liu, and Guanbin Li. Dreameditor: Text-driven 3d scene editing with neural fields. In SIGGRAPH Asia 2023 Conference Papers, New York, NY , USA, 2023. Association for Computing Ma- chinery. 3 11 Instruct-4DGS: Efficient Dynamic Scene...

    2023

  63. [71]

    Additional Qualitative Results 7.1. Results on Monocular Datasets While 4D dynamic scene editing typically relies on multi- view video datasets to sufficiently capture spatio-temporal information, we evaluate our method on the DyCheck [16] and HyperNeRF [41] datasets to explor...

  64. [72]

    Make the person a statue

    Full Set of Editing Instructions Here, we provide the full set of editing instructions used for our dynamic scene editing experiments. We used “Make the person a statue”, “Make the person a marble Roman sculpture”, and “Make the person a wood sculpture” for Tab. 1. We used “Wh...

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.