Pith. sign in

REVIEW 3 major objections 2 minor 38 references

AdaLTM: Adaptive Layer-wise Task Vector Merging for Categorical Speech Emotion Recognition with ASR Knowledge Integration

T0 review · 3 major / 2 minor · reviewed 2026-07-13 · grok-4.5

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

Pith's one-line read Merging separately trained ASR and SER task vectors into a frozen speech backbone, with a learnable weight at each layer, improves categorical emotion recognition without joint-training conflicts.

desk verdict We only have the AdaLTM abstract; the cached full text is the wrong paper (MoRGS), so the central claim cannot be audited. read the letter →

arxiv 2603.25041 v2 pith:7HY4DTTV submitted 2026-03-26 eess.AS

Chia-Yu Lee , Huang-Cheng Chou , Tzu-Quan Lin , Yuanchao Li , Ya-Tse Wu , Shrikanth Narayanan , Chi-Chun Lee This is my paper
classification eess.AS
keywords speechemotionrecognitionautomatictaskvectorsmodelmergingWavLMlayer-wiseadaptationmulti-tasklearningparalinguistics
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

Automatic speech recognition can give emotion models useful word-level context, but conventional feature fusion often plateaus and multi-task training pits the two objectives against each other. This paper argues that the right move is not to train them together. Fine-tune separate in-domain ASR and SER models on emotion data, extract their task vectors relative to a shared base, and fold those vectors back into a frozen WavLM-Large model using a learnable coefficient at every transformer layer. The coefficients let each depth balance linguistic and paralinguistic knowledge on its own terms, without gradient interference. On MSP-Podcast the approach is shown to mitigate the usual ASR–SER conflicts and improve categorical speech emotion recognition.

What carries the argument

AdaLTM (Adaptive Layer-wise Task Vector Merging): task vectors from separately fine-tuned ASR and SER models are integrated into a frozen base via learnable scalar coefficients at each transformer layer, enabling depth-aware mixing without joint gradients.

What would settle it

On MSP-Podcast, if AdaLTM with learned layer-wise coefficients fails to outperform both conventional feature-fusion and multi-task baselines on categorical emotion metrics, or if the learned coefficients collapse to uniform averaging that matches naive merging, the central claim is false.

Watch

Extended reading notes

Core claim

Adaptive layer-wise merging of in-domain ASR and SER task vectors into a frozen WavLM-Large base, controlled by learnable per-layer coefficients, balances linguistic and paralinguistic knowledge for categorical speech emotion recognition and avoids the optimization conflicts of multi-task learning and the bottlenecks of simple feature fusion.

Load-bearing premise

Task vectors taken from separately fine-tuned ASR and SER models remain additive and can be re-weighted layer by layer inside a frozen WavLM so that linguistic and paralinguistic knowledge mix without reintroducing the same interference joint training causes.

Editorial extensions

If this is right

  • Categorical SER can use ASR-derived linguistic context without paying the usual multi-task optimization cost.
  • Per-layer coefficients become a diagnostic of which depths prefer linguistic versus paralinguistic cues.
  • Only the merge coefficients need training once the base is frozen, keeping adaptation lighter than full multi-task fine-tuning.
  • The same separate-then-merge pattern can be tried on other speech task pairs that currently conflict under joint training.

Reading between the lines

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

  • If the coefficients prove stable across corpora, a single frozen backbone plus small coefficient packs could serve many speech task combinations.
  • The results imply that much ASR–SER interference is an optimization artifact rather than an irreducible clash of representations.
  • A direct follow-up is whether coefficients transfer across emotion taxonomies or acoustic conditions without re-learning.
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

3 major / 2 minor

Summary. The submission under review is titled AdaLTM and claims an Adaptive Layer-wise Task Vector Merging framework on WavLM-Large for categorical speech emotion recognition. According to the abstract, ASR and SER task vectors are extracted from separately fine-tuned in-domain models and merged into a frozen base via learnable per-layer coefficients, thereby balancing linguistic and paralinguistic knowledge without multi-task gradient interference; experiments on MSP-Podcast are said to show effective conflict mitigation relative to feature fusion and joint multi-task learning. However, the full manuscript body supplied with this review is not AdaLTM: it is an unrelated computer-vision paper (MoRGS) on online per-Gaussian motion reasoning for streamable dynamic 3D scenes. Consequently only the AdaLTM abstract is available for assessment; no equations, training protocol, baselines, tables, or ablations for AdaLTM can be inspected.

Significance. If the AdaLTM claims held under proper evaluation, the work would be of moderate significance to speech emotion recognition: task-vector merging is established in NLP/CV but comparatively underexplored for speech foundation models, and a depth-aware, interference-free way to inject ASR knowledge into SER would be practically useful. That potential cannot be credited here, because the experimental evidence, method definitions, and ablations required to support the claim are absent from the materials provided.

major comments (3)
  1. Manuscript mismatch / missing body: the paper under review is AdaLTM (arXiv 2603.25041, eess.AS), but the full text supplied is MoRGS (dynamic 3D Gaussian streaming, arXiv 2603.25042, cs.CV). No AdaLTM sections, equations, figures, or tables are present. The central claim that layer-wise task-vector merging 'effectively mitigates conflicts between ASR and SER' on MSP-Podcast is therefore uninspectable and cannot be audited for soundness, baselines, or statistical support.
  2. Undefined method (abstract only): the abstract asserts extraction of ASR/SER task vectors and integration via layer-wise learnable coefficients into frozen WavLM-Large, but provides no definition of the task vectors (e.g., theta_task - theta_base), no statement of how coefficients are optimized or regularized, no loss, and no description of which layers receive which mix. Without these, the premise that linguistic and paralinguistic knowledge are depth-separable and additively re-weightable cannot be checked.
  3. Unsupported experimental claim: the abstract states that experiments on MSP-Podcast demonstrate conflict mitigation and improved SER. No results table, baseline list (feature fusion, multi-task learning, naive merging), ablation of layer-wise vs global coefficients, or comparison isolating additivity is available. The load-bearing empirical claim therefore rests on an uninspectable assertion.
minor comments (2)
  1. Abstract alone is clear on motivation (feature-fusion bottlenecks; multi-task optimization conflicts) and on the high-level design (separate fine-tunes, frozen base, layer-wise coefficients), but that clarity does not substitute for a complete methods and results section.
  2. If a corrected AdaLTM PDF is supplied, the review should re-open with focus on: (i) formal task-vector and merge equations; (ii) coefficient optimization and any regularization; (iii) MSP-Podcast protocol, baselines, and ablations of depth-aware vs global merging; (iv) evidence that interference is reduced rather than merely that accuracy rises.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: empirical methods paper (MoRGS body; AdaLTM abstract only) with no derivation that reduces to its inputs by construction.

full rationale

The supplied materials mix an AdaLTM abstract (task-vector merging for SER) with a full MoRGS manuscript (online per-Gaussian motion for dynamic 3DGS). Auditing the inspectable full text (MoRGS): the method is an engineering pipeline—sparse optical-flow supervision (Eqs. 4–7), a learnable per-Gaussian motion offset (Eq. 8 + L_off), and mask-guided motion confidence weighting residuals (Eqs. 9–12)—trained end-to-end under a composite loss (Eq. 13) and evaluated on held-out views of N3DV/Meet Room with ablations (Tabs. 4–5). Flow comes from an external pretrained model (SEA-RAFT); masks use SAM2; claims are experimental (PSNR/SSIM/LPIPS, mTV, qualitative motion maps), not first-principles predictions forced by a fit or self-definition. No uniqueness theorem, no self-citation load-bearing for the central claim, and no renaming of a known closed-form result. The AdaLTM abstract alone likewise states an empirical merge-coefficient recipe without a circular derivation. Mild optimization of learnable coefficients on the target task is standard supervised learning, not definitional circularity. Score 0; steps empty.

Assumptions & free parameters 2 free parameters · 3 assumptions · 1 invented entities

Abstract-only review. Load-bearing premises are the standard transferability assumptions of task arithmetic plus the paper-specific claim that layer-wise scalar coefficients suffice to balance linguistic vs. paralinguistic knowledge inside frozen WavLM without joint gradients. Free parameters (the per-layer coefficients and any fine-tuning hyperparameters) are implied but not numerically reported. No new physical entities are postulated.

free parameters (2)
  • layer-wise merge coefficients
    Learnable scalars (one or more per transformer layer) that weight ASR vs. SER task vectors; central to the 'adaptive' claim and fitted on the target SER objective.
  • ASR and SER fine-tuning hyperparameters
    Learning rates, epochs, and data schedules used to produce the two task vectors; not reported in the abstract but determine the vectors being merged.
assumptions (3)
  • domain assumption Task vectors obtained by subtracting a base model from a fine-tuned model approximately encode task-specific knowledge and can be linearly recombined.
    Imported from NLP/CV task-arithmetic literature; the abstract treats this as given when applying the idea to speech.
  • ad hoc to paper Linguistic (ASR) and paralinguistic (SER) knowledge are distributed differently across WavLM transformer depths and can therefore be balanced by per-layer coefficients.
    Core design premise of AdaLTM; motivates layer-wise rather than global merging.
  • domain assumption Freezing the base model and avoiding joint multi-task gradients removes optimization conflict between ASR and SER.
    Stated motivation in the abstract; standard multi-task interference narrative.
invented entities (1)
  • AdaLTM (Adaptive Layer-wise Task Vector Merging) framework
    purpose: Name for the procedure that extracts in-domain ASR/SER task vectors and merges them into frozen WavLM with learnable per-layer coefficients.
    System name introduced by the paper; not an ontological new entity, but the central invented method object.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AdaLTM: Adaptive Layer-wise Task Vector Merging for Categorical Speech Emotion Recognition with ASR Knowledge Integration." pith.science (2026). https://pith.science/paper/7HY4DTTV

@misc{pith2026260325041,
  author       = {Pith},
  title        = {Pith review of: AdaLTM: Adaptive Layer-wise Task Vector Merging for Categorical Speech Emotion Recognition with ASR Knowledge Integration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7HY4DTTV}},
  note         = {Machine review of arXiv:2603.25041}
}
read the original abstract

Integrating Automatic Speech Recognition (ASR) into Speech Emotion Recognition (SER) enhances modeling by providing linguistic context. However, conventional feature fusion faces performance bottlenecks, and multi-task learning often suffers from optimization conflicts. While task vectors and model merging have addressed such conflicts in NLP and CV, their potential in speech tasks remains largely unexplored. In this work, we propose an Adaptive Layer-wise Task Vector Merging (AdaLTM) framework based on WavLM-Large. Instead of joint optimization, we extract task vectors from in-domain ASR and SER models fine-tuned on emotion datasets. These vectors are integrated into a frozen base model using layer-wise learnable coefficients. This strategy enables depth-aware balancing of linguistic and paralinguistic knowledge across transformer layers without gradient interference. Experiments on the MSP-Podcast demonstrate that the proposed approach effectively mitigates conflicts between ASR and SER.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

38 extracted references · 2 linked inside Pith

  1. [1]

    HexPlane: A Fast Representa- tion for Dynamic Scenes

    Ang Cao and Justin Johnson. HexPlane: A Fast Representa- tion for Dynamic Scenes. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  2. [2]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. InEuropean conference on computer vision, pages 333–350. Springer, 2022

    2022

  3. [3]

    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 Special Interest Group on Computer Graphics and Interac- tive Techniques Conference Conference Papers (SIGGRAPH), 2024

    2024

  4. [4]

    K-planes: Explicit Radiance Fields in Space, Time, and Appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk War- burg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit Radiance Fields in Space, Time, and Appearance. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  5. [5]

    Recon-gs: Continuum- preserved guassian streaming for fast and compact reconstruc- tion of dynamic scenes.arXiv preprint arXiv:2509.24325, 2025

    Jiaye Fu, Qiankun Gao, Chengxiang Wen, Yanmin Wu, Siwei Ma, Jiaqi Zhang, and Jian Zhang. Recon-gs: Continuum- preserved guassian streaming for fast and compact reconstruc- tion of dynamic scenes.arXiv preprint arXiv:2509.24325, 2025

    arXiv 2025

  6. [6]

    HiCoM: Hierarchical coherent motion for dy- namic streamable scenes with 3d gaussian splatting

    Qiankun Gao, Jiarui Meng, Chengxiang Wen, Jie Chen, and Jian Zhang. HiCoM: Hierarchical coherent motion for dy- namic streamable scenes with 3d gaussian splatting. InPro- ceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  7. [7]

    QUEEN: QUantized Effi- cient ENcoding of Dynamic Gaussians for Streaming Free- viewpoint Videos

    Sharath Girish, Tianye Li, Amrita Mazumdar, Abhinav Shri- vastava, Shalini De Mello, et al. QUEEN: QUantized Effi- cient ENcoding of Dynamic Gaussians for Streaming Free- viewpoint Videos. InProceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  8. [8]

    4DGC: Rate- Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video

    Qiang Hu, Zihan Zheng, Houqiang Zhong, Sihua Fu, Li Song, Guangtao Zhai, Yanfeng Wang, et al. 4DGC: Rate- Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), 2025

    2025

Show all 38 references
  1. [9]

    3D Gaussian Splatting for Real-Time Radi- ance Field Rendering.ACM Transactions on Graphics (TOG), 2023

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3D Gaussian Splatting for Real-Time Radi- ance Field Rendering.ACM Transactions on Graphics (TOG), 2023

    2023

  2. [10]

    Fully explicit dynamic gaussian splat- ting

    Junoh Lee, ChangYeon Won, Hyunjun Jung, Inhwan Bae, and Hae-Gon Jeon. Fully explicit dynamic gaussian splat- ting. InProceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  3. [11]

    Streaming Radiance Fields for 3D Video Synthesis

    Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, and Ping Tan. Streaming Radiance Fields for 3D Video Synthesis. In Proceedings of the Advances in Neural Information Process- ing Systems (NeurIPS), 2022

    2022

  4. [12]

    Neu- ral 3D Video Synthesis from Multi-view Video

    Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, et al. Neu- ral 3D Video Synthesis from Multi-view Video. InProceed- ings of the IEEE/CVF Conference on Computer Vis...

    2022

  5. [13]

    Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis

    Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  6. [14]

    Efficient neural radiance fields for interactive free-viewpoint video

    Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. InSIGGRAPH Asia 2022 Conference Papers, pages 1–9, 2022

    2022

  7. [15]

    Dn-4dgs: Denoised deformable network with temporal-spatial aggregation for dynamic scene rendering

    Jiahao Lu, Jiacheng Deng, Ruijie Zhu, Yanzhe Liang, Wenfei Yang, Tianzhu Zhang, and Xu Zhou. Dn-4dgs: Denoised deformable network with temporal-spatial aggregation for dynamic scene rendering. InProceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  8. [16]

    Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis. InInternational Conference on 3D Vision (3DV), 2024

    2024

  9. [17]

    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 Synthesis. InProceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  10. [18]

    Nerfies: Deformable neural radiance fields

    Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. InProceedings of the IEEE/CVF international conference on computer vision, pages 5865–5874, 2021

    2021

  11. [19]

    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. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), 2021

    2021

  12. [20]

    Sam 2: Segment any- thing in images and videos.arXiv preprint arXiv:2408.00714, 2024

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment any- thing in images and videos.arXiv preprint arXiv:2408.00714, 2024

    2024 arXiv

  13. [21]

    Structure- from-motion revisited

    Johannes L Schonberger and Jan-Michael Frahm. Structure- from-motion revisited. InProceedings of the IEEE confer- ence on computer vision and pattern recognition, pages 4104– 4113, 2016

    2016

  14. [22]

    Tensor4D: Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering

    Ruizhi Shao, Zerong Zheng, Hanzhang Tu, Boning Liu, Hong- wen Zhang, and Yebin Liu. Tensor4D: Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  15. [23]

    NeRF- Player: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields.IEEE Transactions on Visualization and Computer Graphics (TVCG), 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 Decomposed Neural Radiance Fields.IEEE Transactions on Visualization and Computer Graphics (TVCG), 2023

    2023

  16. [24]

    3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free- Viewpoint Videos

    Jiakai Sun, Han Jiao, Guangyuan Li, Zhanjie Zhang, Lei Zhao, and Wei Xing. 3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free- Viewpoint Videos. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (C...

    2024

  17. [25]

    Non- Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

    Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, and Christian Theobalt. Non- Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. InProceedings of the IEEE/CVF International Confe...

    2021

  18. [26]

    Neural residual radiance fields for streamably free-viewpoint videos

    Liao Wang, Qiang Hu, Qihan He, Ziyu Wang, Jingyi Yu, Tinne Tuytelaars, Lan Xu, and Minye Wu. Neural residual radiance fields for streamably free-viewpoint videos. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  19. [27]

    Inv: Towards streaming incremen- tal neural videos.arXiv preprint arXiv:2302.01532, 2023

    Shengze Wang, Alexey Supikov, Joshua Ratcliff, Henry Fuchs, and Ronald Azuma. Inv: Towards streaming incremen- tal neural videos.arXiv preprint arXiv:2302.01532, 2023

    2023 arXiv

  20. [28]

    Sea-raft: Simple, efficient, accurate raft for optical flow

    Yihan Wang, Lahav Lipson, and Jia Deng. Sea-raft: Simple, efficient, accurate raft for optical flow. InEuropean Confer- ence on Computer Vision, pages 36–54. Springer, 2024

    2024

  21. [29]

    4D Gaussian Splatting for Real-Time Dynamic Scene Ren- dering

    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 Ren- dering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  22. [30]

    Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene

    Jiahao Wu, Rui Peng, Zhiyan Wang, Lu Xiao, Luyang Tang, Jinbo Yan, Kaiqiang Xiong, and Ronggang Wang. Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene. InInterna- tional Conference on Learning Representations (IC...

    2025

  23. [31]

    Tetrirf: Temporal tri-plane radiance fields for efficient free-viewpoint video

    Minye Wu, Zehao Wang, Georgios Kouros, and Tinne Tuyte- laars. Tetrirf: Temporal tri-plane radiance fields for efficient free-viewpoint video. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  24. [32]

    4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time rendering of temporally complex dynamic scenes

    Jinbo Yan, Rui Peng, Luyang Tang, and Ronggang Wang. 4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time rendering of temporally complex dynamic scenes. InProceedings of the ACM Inter- national Conference on Multimedia (ACM MM), 2024

    2024

  25. [33]

    Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting

    Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, and Ronggang Wang. Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting. InProceedings of the IEEE/CVF Conference on Computer Vision and...

    2025

  26. [34]

    Nerf-ds: Neural radiance fields for dynamic specular objects

    Zhiwen Yan, Chen Li, and Gim Hee Lee. Nerf-ds: Neural radiance fields for dynamic specular objects. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  27. [35]

    Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

    Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), 2024

    2024

  28. [36]

    Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting

    Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang. Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting. InInternational Conference on Learning Representations (ICLR), 2024

    2024

  29. [37]

    SplineGS: Learning Smooth Trajectories in Gaussian Splat- ting for Dynamic Scene Reconstruction

    Jihwan Yoon, Sangbeom Han, Jaeseok Oh, and Minsik Lee. SplineGS: Learning Smooth Trajectories in Gaussian Splat- ting for Dynamic Scene Reconstruction. InInternational Conference on Learning Representations (ICLR), 2025

    2025

  30. [38]

    Motiongs: Exploring explicit motion guidance for deformable 3d gaussian splatting

    Ruijie Zhu, Yanzhe Liang, Hanzhi Chang, Jiacheng Deng, Ji- ahao Lu, Wenfei Yang, Tianzhu Zhang, and Yongdong Zhang. Motiongs: Exploring explicit motion guidance for deformable 3d gaussian splatting. InProceedings of the Advances in Neu- ral Information Processing Systems (Neur...

    2024

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.