Pith. sign in

REVIEW 3 major objections 4 minor 4 cited by

XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read XSpecMesh speeds auto-regressive mesh generation 1.7x while keeping Chamfer, Hausdorff, and user-study quality level with the base model.

desk verdict A clean, honest transfer of Medusa-style multi-head speculative decoding to mesh generation, with a real 1.7x speedup but a 'quality-preserving' claim that rests on a tuned threshold rather than a formal guarantee. read the letter →

arxiv 2507.23777 v2 pith:X4QP4J2Q submitted 2025-07-31 cs.GR cs.CVcs.LG

Dian Chen , Yansong Qu , Xinyang Li , Ming Li , Shengchuan Zhang This is my paper
classification cs.GRcs.CVcs.LG
keywords auto-regressivemeshgenerationspeculativedecodingmulti-headinferenceaccelerationqualitypreservationcross-attentionheadsLoRAdistillation3D
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

XSpecMesh tries to establish that auto-regressive mesh generation can be accelerated without giving up generation quality: it attaches several lightweight cross-attention decoding heads to a pretrained mesh transformer so that one forward pass proposes multiple future tokens at once, and lets the backbone model verify each proposal. The paper reports a 1.7x end-to-end speedup on the BPT backbone, cutting average latency from 257.6 to 151.4 seconds, with Chamfer Distance, Hausdorff Distance, and anonymized user-study preferences indistinguishable from BPT itself. The claim matters because auto-regressive mesh models currently need thousands of sequential next-token predictions to emit one mesh, and the existing acceleration methods for language and image models do not transfer obviously to mesh token streams. If the claim holds, it gives a cheap, trainable add-on that makes an existing mesh generator about 70% faster with no detectable quality cost.

What carries the argument

The load-bearing mechanism is multi-head speculative decoding with probability-threshold verification. The backbone's final hidden state $h_s$ is decoded both by its own linear readout into $p_{s+1}^{(0)}$ and by $D$ cross-attention heads, where head $d$ predicts the distribution $p_{s+d+1}^{(d)}$ for the $(d+1)$-th future token conditioned on the input condition $c$. In one causal-masked forward pass the backbone computes verification probabilities for the whole proposed block, accepts the longest prefix whose tokens satisfy $p_i^{(0)}(x_i) > \delta$, and resamples from the first rejected position. A two-stage distillation, first training only the heads and then jointly training heads with a LoRA adapter while weighting the backbone loss by $\lambda=50$, pulls the heads' distributions toward the backbone's, raising acceptance without full-parameter fine-tuning.

What would settle it

Run Algorithm 1 at $\delta=0.5$ on a held-out set of shapes and compare Chamfer Distance to the same shapes generated by sampling every token from the backbone's distribution; a statistically significant quality gap would falsify the quality-preservation claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that multi-head speculative decoding preserves generation quality because the backbone's own next-token probability distribution is a sufficient gate: a candidate token is accepted only when $p_i^{(0)}(x_i) > \delta$ for the backbone's probability $p_i^{(0)}$ at position $i$, and the first rejected position is resampled from the heads for the next iteration. On 200 test meshes this yields CD 0.1168 versus BPT's 0.1165, HD 0.2261 versus 0.2223, and a user-study vote share statistically indistinguishable from BPT, while cutting average generation latency from 257.6 to 151.4 seconds. The authors present this as the first acceleration method for auto-regressive mesh generation that does not sacrifice generation quality, achieved by verifying with the backbone model rather than trusting the decoding heads.

Load-bearing premise

The load-bearing premise is that a fixed probability threshold on the backbone's next-token prediction is a sufficient gate for mesh quality, and that the threshold picked on the evaluation data will keep working on new shapes.

Editorial extensions

If this is right

  • With four decoding heads, the step compression ratio reaches 2.02, meaning more than two accepted tokens per forward pass, and the end-to-end speedup is 1.71x.
  • Average complete-mesh latency drops from 257.6 to 151.4 seconds on an RTX 3090, which makes interactive mesh generation workflows materially more usable.
  • Cross-attention heads are necessary for quality preservation: replacing them with MLP heads and adding LoRA joint training degrades CD from 0.1168 to 0.1267, so injecting the generation condition into the heads carries the quality.
  • The acceptance threshold $\delta$ is a trade-off dial: raising it strengthens quality but lowers speedup, so practitioners can choose between near-lossless acceleration and more aggressive compression.
  • The recipe is not tied to BPT-specific internals beyond a small vocabulary and a transformer backbone, so it can be applied to other auto-regressive mesh tokenizers.

Reading between the lines

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

  • Because the gate only checks each token's marginal probability, the method cannot by itself catch errors that only become visible several tokens later; a shape-level or sequence-level validator would close that gap.
  • The same multi-head recipe should transfer to other auto-regressive mesh models, but the achievable speedup will depend on how predictable the next tokens are, and models with larger vocabularies may need more heads or a different acceptance rule.
  • A testable extension is to make $\delta$ adaptive per position or per shape difficulty, using the backbone's own confidence distribution rather than a global threshold fixed on the evaluation set.
  • Since LoRA is merged at inference time, most of the remaining step-latency overhead is the cross-attention heads, so on faster hardware the optimal number of heads could shift away from $D=4$.
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 / 4 minor

Summary. The paper proposes XSpecMesh, a multi-head speculative decoding approach to accelerate autoregressive mesh generation. It attaches D lightweight cross-attention decoding heads to the final hidden state of a pretrained backbone (BPT), each predicting a future token, and then runs one forward pass of the backbone over the candidate tokens. Candidate tokens are accepted while the backbone probability p_i(x_i) exceeds a threshold δ; at the first rejected position the method resamples from the backbone and heads and continues. The heads are trained by distillation from backbone-generated sequences, with optional LoRA fine-tuning of the backbone's output layer. Experiments on 200 meshes report CD 0.1168 vs. 0.1165, HD 0.2261 vs. 0.2223, a user-study vote share of 36% vs. 37% for BPT, and a latency reduction from 257.6s to 151.4s (1.71×). The paper claims to be a first acceleration method for autoregressive mesh generation that preserves generation quality.

Significance. If the empirical claims hold, XSpecMesh would be a practically useful, low-overhead acceleration for autoregressive mesh generators, and the cross-attention head design plus distillation is a sensible response to the small vocabulary sizes of mesh tokenizers. The ablation study is well structured and covers the main design choices; the authors also honestly report the configuration (MLP heads with LoRA) where quality degrades under the same acceptance gate. The paper's main weakness is that the 'quality-preserving' guarantee is not entailed by the verification design, and the empirical support at the chosen operating point lacks uncertainty quantification. The strengths—clear ablations, internal consistency of the speedup calculations, and a concrete deployed baseline comparison—make the contribution credible, but the central claim needs reframing or additional evidence.

major comments (3)
  1. [§4.2, Algorithm 1] The verification criterion in Algorithm 1 is a confidence gate, not a distribution-preserving acceptance rule. In contrast to standard speculative decoding, which resamples from the residual distribution (p(x)-q(x))_+ so that the accepted sequence is an exact sample from the backbone model, line 11 resamples the rejected position from the backbone and all subsequent positions from the heads. Hence the output distribution is not guaranteed to match the backbone's, and the claim that quality is 'preserved' by verification (Section 4.2, last paragraph) is not implied by the design. The paper's own Table 3, configuration C, shows the same gate accepting tokens that degrade CD from 0.1165 to 0.1267, so acceptance by the gate is empirically insufficient. I ask the authors to either add a residual-correction sampling step with a formal guarantee, or to characterize the method as heuristic acceleration and support the quality claim statistically at the chosen operating point.
  2. [§5.4, Fig. 5(b)] The operating point δ=0.5 is selected using the evaluation data itself: Figure 5(b) sweeps δ on the 200-mesh test set and reports that CD and HD match the baseline at δ=0.5. Because the same data are used to choose δ and to report Table 2, the reported quality parity is partly a selection result. Please provide a validation split for hyperparameter selection, or show that the quality conclusion is stable for a range of δ (e.g., δ ∈ {0.3,0.5,0.7}) without re-tuning.
  3. [§5.3, Tables 2 and 3; Appendix §7.4] The quality-parity claim is based on point estimates without uncertainty quantification. CD 0.1168 vs 0.1165 and HD 0.2261 vs 0.2223 differ by less than 2% on 200 meshes, but no standard deviations, confidence intervals, or paired significance tests are given, and the 1,400 user-study responses are summarized only as comparable vote counts. Please report per-mesh variance, paired tests (e.g., Wilcoxon or bootstrap) for CD/HD, and a statistical test on the user-study vote distribution (e.g., binomial or chi-square) to establish that the difference is not resolvable.
minor comments (4)
  1. [Appendix §7.1] The implementation details do not state how the validation and test splits are constructed; this is directly relevant to the δ-selection issue, so please describe the split used for hyperparameter selection and for the reported comparisons.
  2. [§5.1] The sentence 'We follow the evaluation procedure of previous work and generate 200 test meshes via the generation model [46,49]' is ambiguous: clarify whether the test shapes are generated from point clouds, whether they overlap the 10K Objaverse training subset, and how the difficulty levels in Appendix §7.2 were assigned.
  3. [Algorithm 1] Line 1 initializes the first D tokens from a uniform distribution and has them verified by the threshold criterion, but the text never explains how these initial random tokens affect the early phase of generation or whether they are discarded; a short clarification would help.
  4. [Figure 6] The comparison between probability-threshold acceptance and Top-Ka acceptance is shown only qualitatively; reporting quantitative CD/HD values for each criterion would make the claim about stability more convincing.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the speedup and quality claims are anchored by external CD/HD metrics against ground truth and by the BPT baseline, not by an equation-level identification with the acceptance gate.

full rationale

The derivation chain is self-contained and externally anchored. The central claim—1.7× speedup without quality loss—is supported by measured end-to-end latency and by CD/HD computed on 200 test meshes against external ground truth and the released BPT baseline, so the headline numbers are not equal by construction to any fitted input. The decoding heads are distilled from backbone-generated sequences and verified by the backbone's own probabilities, but this is the intended speculative-decoding mechanism; acceptance (SCR) and step latency are separately measured, and CD/HD lie outside the head/backbone loop. The verification criterion p_i(x_i) > δ is an empirical heuristic, not a distribution-preserving correction, and the paper's own Table 3 (configuration C) shows that the same gate can admit quality-degrading tokens; that is a correctness/robustness concern, not circularity. Self-citations appear only as related-work background and are never load-bearing premises for the acceleration or quality result. The only mild concern is that δ=0.5 is selected on the same 200-mesh evaluation set used to report the quality metrics (Sec. 5.4, Fig. 5(b)); this is a test-set hyperparameter-selection issue that does not make the reported CD/HD values true by construction, since they are still measured against external ground truth.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical or conceptual entities beyond trainable decoding heads and a threshold criterion. The central claims depend on several hand-tuned hyperparameters (δ, D, λ, w(d)) and on domain assumptions about the reliability of the backbone's probabilities and the representativeness of the test set.

free parameters (4)
  • acceptance threshold δ = 0.5
    Chosen based on ablation (Fig 5b) to trade off speedup vs CD/HD; at lower δ speedup rises to ~1.8x but quality degrades. The central quality-preservation claim depends on this operating point.
  • number of decoding heads D = 4
    Chosen based on ablation (Fig 5a) where speedup peaks at 1.71x; D affects both SCR and step latency.
  • decoding head loss weights w(d) = 0.8^d
    Hand-chosen decaying weights to stabilize gradients for later heads.
  • backbone loss weight λ = 50
    Chosen 'relatively large' to prevent distribution drift of the backbone during LoRA joint training.
assumptions (5)
  • domain assumption BPT is a strong, high-quality auto-regressive mesh generator and its next-token probabilities are reliable indicators of token quality.
    The method uses the backbone's own probability as the acceptance criterion; if the backbone probabilities are miscalibrated, the verification can accept bad tokens. Invoked in Sec 4.2.
  • standard math The causal masking property allows a single forward pass to compute predictive distributions for positions s+1 through s+D+1 simultaneously.
    Relied on throughout; standard for decoder-only transformers, stated in Sec 3.1.
  • domain assumption Chamfer Distance and Hausdorff Distance computed from 1024 uniform samples capture generation quality.
    Metrics used to support the 'no quality loss' claim; no error bars or statistical tests are reported.
  • domain assumption The 200 test shapes generated by models [46,49] are representative of the target application distribution.
    Quality and speedup are evaluated only on this set, with no held-out validation for threshold tuning reported.
  • domain assumption Fine-tuning the backbone with LoRA does not change its output distribution enough to invalidate quality comparisons against the original BPT.
    The backbone is modified via LoRA in stage two; the paper argues LoRA prevents drift but does not directly measure the LoRA-tuned backbone's standalone quality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding." pith.science (2026). https://pith.science/paper/X4QP4J2Q

@misc{pith2026250723777,
  author       = {Pith},
  title        = {Pith review of: XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X4QP4J2Q}},
  note         = {Machine review of arXiv:2507.23777}
}
read the original abstract

Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands-or even tens of thousands-of next-token predictions during inference, resulting in substantial latency. We introduce XSpecMesh, a quality-preserving acceleration method for auto-regressive mesh generation models. XSpecMesh employs a lightweight, multi-head speculative decoding scheme to predict multiple tokens in parallel within a single forward pass, thereby accelerating inference. We further propose a verification and resampling strategy: the backbone model verifies each predicted token and resamples any tokens that do not meet the quality criteria. In addition, we propose a distillation strategy that trains the lightweight decoding heads by distilling from the backbone model, encouraging their prediction distributions to align and improving the success rate of speculative predictions. Extensive experiments demonstrate that our method achieves a 1.7x speedup without sacrificing generation quality. Our code will be released.

Figures

Figures reproduced from arXiv: 2507.23777 by the authors.

Figure 1
Figure 1. The differences between our framework and previous works. We propose XSpecMesh, a method for accelerating auto￾regressive mesh generation models via multi-head speculative decoding, instead of relying on traditional next-token prediction. In a single forward pass, multiple decoding heads predict several tokens, verify the candidate tokens, and resample candidate tokens for the next iteration. Our approach delivers a… view at source ↗
Figure 2
Figure 2. Overview of our method. Left: A pretrained transformer-based auto-regressive mesh generation model, fine-tuned with LoRA. Top-right: The transformer’s final hidden layer is decoded by D cross-attention decoding heads, the d-th head predicts the (d + 1)-th next token. Bottom-right: The complete generated token sequence is detokenized to produce the mesh. demonstrated the ability to synthesize topologically pre￾cise m… view at source ↗
Figure 3
Figure 3. Verification and resampling. The figure uses D = 3 as an example to illustrate the process. Each candidate token sam￾pled in a forward pass must be verified by the backbone model: if p (0) i (xi) > δ, token xi is accepted and verification proceeds to the next token, until the first token xi ′ that fails the verification condi￾tion. Then resample a token at position i ′ , forming the candidate tokens for the next ite… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison of our method with the base model BPT and another mesh generation model DeepMesh. Our acceleration method, built upon BPT, substantially accelerates generation while preserving BPT’s shape and topological fidelity. large weight λ = 50 to the backbone loss. S…
Figure 5
Figure 5. Figure 5: Left: ablation of the number of decoding heads [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Comparison of Probability-Threshold and Top-Ka Acceptance. Probability-threshold acceptance is more stable, generating reasonable shapes across thresholds. pare two acceptance criteria—Probability-Threshold Ac￾ceptance and Top-Ka Acceptance (a token xi is accepted if x…
Figure 7
Figure 7. Figure 7: A subset of examples from the test dataset. Our test dataset contains a rich variety of shapes and is divided into three different difficulty levels: level-0, level-1, and level-2. across all test cases. The proposed XSpecMesh framework leverages BPT as its backbone: B…
Figure 8
Figure 8. Figure 8: Additional generation results of our method versus BPT. Our acceleration method, built upon BPT, substantially accelerates generation while preserving BPT’s shape and topological fidelity [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Additional generation results of our method versus BPT. Our acceleration method, built upon BPT, substantially accelerates generation while preserving BPT’s shape and topological fidelity [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation

    cs.GR 2026-06 unverdicted novelty 7.0 of 10

    PolyFlow converts discrete meshes to continuous per-vertex representations using a topology embedder and applies flow matching for parallel artist-style mesh generation that outperforms autoregressive baselines on Toy...

  2. QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning

    cs.GR 2026-05 unverdicted novelty 7.0 of 10

    QuadLink generates anisotropic quad-dominant meshes from point clouds via anchor prediction, centroid-conditioned linking, and quad-first assembly, supporting hybrid n-gon topology.

  3. QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning

    cs.GR 2026-05 unverdicted novelty 6.0 of 10

    QuadLink generates anisotropic quad-dominant meshes from point clouds via a hybrid centroid-conditioned vertex linking model and a Tri-to-Quad data conversion operator.

  4. QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning

    cs.GR 2026-05 unverdicted novelty 4.0 of 10

    QuadLink generates anisotropic quad-dominant meshes from point clouds via autoregressive anchor prediction and centroid-conditioned linking, with a Tri-to-Quad data converter and quad-first assembly.

Reference graph

Works this paper leans on

73 extracted references · 41 canonical work pages · cited by 2 Pith papers

  1. [1]

    Meshgpt: Gen- erating triangle meshes with decoder-only transformers,

    Y . Siddiqui, A. Alliegro, A. Artemov, T. Tommasi, D. Siri- gatti, V . Rosov, A. Dai, and M. Nießner, “Meshgpt: Gen- erating triangle meshes with decoder-only transformers,” in Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pp. 19615–19625, 2024. 2

    work page 2024

  2. [2]

    Meshxl: Neural coordi- nate field for generative 3d foundation models,

    S. Chen, X. Chen, A. Pang, X. Zeng, W. Cheng, Y . Fu, F. Yin, B. Wang, J. Yu, G. Yu,et al., “Meshxl: Neural coordi- nate field for generative 3d foundation models,”Advances in Neural Information Processing Systems, vol. 37, pp. 97141– 97166, 2024. 2

    work page 2024

  3. [3]

    Meshanything: Artist-created mesh generation with autoregressive transformers,

    Y . Chen, T. He, D. Huang, W. Ye, S. Chen, J. Tang, X. Chen, Z. Cai, L. Yang, G. Yu,et al., “Meshanything: Artist-created mesh generation with autoregressive transformers,” arXiv preprint arXiv:2406.10163, 2024. 2, 3

    arXiv 2024

  4. [4]

    Scaling mesh generation via compressive tokenization,

    H. Weng, Z. Zhao, B. Lei, X. Yang, J. Liu, Z. Lai, Z. Chen, Y . Liu, J. Jiang, C. Guo,et al., “Scaling mesh generation via compressive tokenization,” in Proceedings of the Computer Vision and Pattern Recognition Conference , pp. 11093– 11103, 2025. 3, 5, 6

    work page 2025

  5. [5]

    Deepmesh: Auto-regressive artist-mesh creation with reinforcement learning,

    R. Zhao, J. Ye, Z. Wang, G. Liu, Y . Chen, Y . Wang, and J. Zhu, “Deepmesh: Auto-regressive artist-mesh creation with reinforcement learning,” arXiv preprint arXiv:2503.15265, 2025. 3, 6

    arXiv 2025

  6. [6]

    Mesh-rft: Enhancing mesh gen- eration via fine-grained reinforcement fine-tuning,

    J. Liu, J. Xu, S. Guo, J. Li, J. Guo, J. Yu, H. Weng, B. Lei, X. Yang, Z. Chen, et al., “Mesh-rft: Enhancing mesh gen- eration via fine-grained reinforcement fine-tuning,” arXiv preprint arXiv:2505.16761, 2025. 2, 6

    arXiv 2025

  7. [7]

    Fast inference from transformers via speculative decoding,

    Y . Leviathan, M. Kalman, and Y . Matias, “Fast inference from transformers via speculative decoding,” in Interna- tional Conference on Machine Learning , pp. 19274–19286, PMLR, 2023. 2, 3

    work page 2023

  8. [8]

    Accelerating large language model decoding with speculative sampling,

    C. Chen, S. Borgeaud, G. Irving, J.-B. Lespiau, L. Sifre, and J. Jumper, “Accelerating large language model decoding with speculative sampling,” arXiv preprint arXiv:2302.01318, 2023. 2, 3, 6

    arXiv 2023

Show all 73 references
  1. [9]

    Scaling laws with vocabulary: Larger models deserve larger vocabularies,

    C. Tao, Q. Liu, L. Dou, N. Muennighoff, Z. Wan, P. Luo, M. Lin, and N. Wong, “Scaling laws with vocabulary: Larger models deserve larger vocabularies,” URL https://arxiv. org/abs/2407.13623, 2024. 2

    2024 arXiv

  2. [10]

    Over-tokenized transformer: V ocabulary is gener- ally worth scaling,

    H. Huang, D. Zhu, B. Wu, Y . Zeng, Y . Wang, Q. Min, and X. Zhou, “Over-tokenized transformer: V ocabulary is gener- ally worth scaling,” arXiv preprint arXiv:2501.16975, 2025. 2

    2025 arXiv

  3. [11]

    The llama 3 herd of models,

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Ka- dian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughan, et al. , “The llama 3 herd of models,” arXiv preprint arXiv:2407.21783, 2024. 2

    2024 arXiv

  4. [12]

    Qwen3 technical report,

    A. Yang, A. Li, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Gao, C. Huang, C. Lv, et al., “Qwen3 technical report,” arXiv preprint arXiv:2505.09388, 2025. 2

    2025 arXiv

  5. [13]

    Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling,

    J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum, “Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling,” Advances in neural infor- mation processing systems, vol. 29, 2016. 2

    2016

  6. [14]

    O-cnn: Octree-based convolutional neural networks for 3d shape analysis,

    P.-S. Wang, Y . Liu, Y .-X. Guo, C.-Y . Sun, and X. Tong, “O-cnn: Octree-based convolutional neural networks for 3d shape analysis,” ACM Transactions On Graphics (TOG) , vol. 36, no. 4, pp. 1–11, 2017. 2

    2017

  7. [15]

    Diffusion probabilistic models for 3d point cloud generation,

    S. Luo and W. Hu, “Diffusion probabilistic models for 3d point cloud generation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 2837–2845, 2021. 2

    2021

  8. [16]

    Shap-e: Generating conditional 3d implicit functions,

    H. Jun and A. Nichol, “Shap-e: Generating conditional 3d implicit functions,” arXiv preprint arXiv:2305.02463, 2023

    2023 arXiv

  9. [17]

    Pointnet: Deep learning on point sets for 3d classification and segmentation,

    C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 652–660, 2017

    2017

  10. [18]

    Wildseg3d: Segment any 3d objects in the wild from 2d images,

    Y . Guo, J. Hu, Y . Qu, and L. Cao, “Wildseg3d: Segment any 3d objects in the wild from 2d images,” arXiv preprint arXiv:2503.08407, 2025. 2

    2025 arXiv

  11. [19]

    Learning implicit fields for gen- erative shape modeling,

    Z. Chen and H. Zhang, “Learning implicit fields for gen- erative shape modeling,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. 5939–5948, 2019. 2

    2019

  12. [20]

    Deepsdf: Learning continuous signed dis- tance functions for shape representation,

    J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed dis- tance functions for shape representation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 165–174, 2019

    2019

  13. [21]

    Rip-nerf: Learn- ing rotation-invariant point-based neural radiance field for fine-grained editing and compositing,

    Y . Wang, J. Wang, Y . Qu, and Y . Qi, “Rip-nerf: Learn- ing rotation-invariant point-based neural radiance field for fine-grained editing and compositing,” in Proceedings of the 2023 ACM international conference on multimedia retrieval, pp. 125–134, 2023

    2023

  14. [22]

    Sg-nerf: Semantic-guided point- based neural radiance fields,

    Y . Qu, Y . Wang, and Y . Qi, “Sg-nerf: Semantic-guided point- based neural radiance fields,” in 2023 IEEE International Conference on Multimedia and Expo (ICME) , pp. 570–575, IEEE, 2023

    2023

  15. [23]

    Nerf-dets: Enhancing multi-view 3d object detection with sampling- adaptive network of continuous nerf-based representation,

    C. Huang, X. Li, S. Zhang, L. Cao, and R. Ji, “Nerf-dets: Enhancing multi-view 3d object detection with sampling- adaptive network of continuous nerf-based representation,” arXiv e-prints, pp. arXiv–2404, 2024. 2

    2024

  16. [24]

    3d gaussian splatting for real-time radiance field rendering.,

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering.,” ACM Trans. Graph., vol. 42, no. 4, pp. 139–1, 2023. 2

    2023

  17. [25]

    Director3d: Real-world camera trajectory and 3d scene generation from text,

    X. Li, Z. Lai, L. Xu, Y . Qu, L. Cao, S. Zhang, B. Dai, and R. Ji, “Director3d: Real-world camera trajectory and 3d scene generation from text,”Advances in Neural Information Processing Systems, vol. 37, pp. 75125–75151, 2024. 2

    2024

  18. [26]

    We-gs: An in-the-wild effi- cient 3d gaussian representation for unconstrained photo col- lections,

    Y . Wang, J. Wang, and Y . Qi, “We-gs: An in-the-wild effi- cient 3d gaussian representation for unconstrained photo col- lections,” 2024

    2024

  19. [27]

    Look at the sky: Sky-aware efficient 3d gaussian splatting in the wild,

    Y . Wang, J. Wang, R. Gao, Y . Qu, W. Duan, S. Yang, and Y . Qi, “Look at the sky: Sky-aware efficient 3d gaussian splatting in the wild,” IEEE Transactions on Visualization and Computer Graphics, 2025

    2025

  20. [28]

    Evolving high-quality rendering and reconstruction in a unified framework with contribution-adaptive regular- ization,

    Y . Shen, Z. Zhang, X. Li, Y . Qu, Y . Lin, S. Zhang, and L. Cao, “Evolving high-quality rendering and reconstruction in a unified framework with contribution-adaptive regular- ization,” arXiv preprint arXiv:2503.00881, 2025

    2025 arXiv

  21. [29]

    Goi: Find 3d gaussians of interest with an optimizable open-vocabulary semantic-space hyperplane,

    Y . Qu, S. Dai, X. Li, J. Lin, L. Cao, S. Zhang, and R. Ji, “Goi: Find 3d gaussians of interest with an optimizable open-vocabulary semantic-space hyperplane,” in Proceed- ings of the 32nd ACM International Conference on Multi- media, pp. 5328–5337, 2024

    2024

  22. [30]

    Training- free hierarchical scene understanding for gaussian splatting with superpoint graphs,

    S. Dai, Y . Qu, Z. Li, X. Li, S. Zhang, and L. Cao, “Training- free hierarchical scene understanding for gaussian splatting with superpoint graphs,” arXiv preprint arXiv:2504.13153 ,

    arXiv

  23. [31]

    Dream- fusion: Text-to-3d using 2d diffusion,

    B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dream- fusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988, 2022. 2

    2022 arXiv

  24. [32]

    Prolificdreamer: High-fidelity and diverse text-to-3d gener- ation with variational score distillation,

    Z. Wang, C. Lu, Y . Wang, F. Bao, C. Li, H. Su, and J. Zhu, “Prolificdreamer: High-fidelity and diverse text-to-3d gener- ation with variational score distillation,” Advances in Neu- ral Information Processing Systems, vol. 36, pp. 8406–8441, 2023

    2023

  25. [33]

    Hifa: High-fidelity text- to-3d generation with advanced diffusion guidance,

    J. Zhu, P. Zhuang, and S. Koyejo, “Hifa: High-fidelity text- to-3d generation with advanced diffusion guidance,” arXiv preprint arXiv:2305.18766, 2023

    2023 arXiv

  26. [34]

    Sweetdreamer: Align- ing geometric priors in 2d diffusion for consistent text-to- 3d,

    W. Li, R. Chen, X. Chen, and P. Tan, “Sweetdreamer: Align- ing geometric priors in 2d diffusion for consistent text-to- 3d,” arXiv preprint arXiv:2310.02596, 2023

    2023 arXiv

  27. [35]

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

    C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y . Liu, and T.-Y . Lin, “Magic3d: High-resolution text-to-3d content creation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 300–309, 2023

    2023

  28. [36]

    Dreamgaus- sian: Generative gaussian splatting for efficient 3d content creation,

    J. Tang, J. Ren, H. Zhou, Z. Liu, and G. Zeng, “Dreamgaus- sian: Generative gaussian splatting for efficient 3d content creation,” arXiv preprint arXiv:2309.16653, 2023

    2023 arXiv

  29. [37]

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

    T. Yi, J. Fang, J. Wang, G. Wu, L. Xie, X. Zhang, W. Liu, Q. Tian, and X. Wang, “Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 6796–...

    2024

  30. [38]

    Drag your gaussian: Effective drag-based editing with score distillation for 3d gaussian splatting,

    Y . Qu, D. Chen, X. Li, X. Li, S. Zhang, L. Cao, and R. Ji, “Drag your gaussian: Effective drag-based editing with score distillation for 3d gaussian splatting,” arXiv preprint arXiv:2501.18672, 2025. 2

    2025 arXiv

  31. [39]

    Zero-1-to-3: Zero-shot one image to 3d object,

    R. Liu, R. Wu, B. Van Hoorick, P. Tokmakov, S. Zakharov, and C. V ondrick, “Zero-1-to-3: Zero-shot one image to 3d object,” in Proceedings of the IEEE/CVF international con- ference on computer vision, pp. 9298–9309, 2023. 2

    2023

  32. [40]

    Zero123++: a single image to con- sistent multi-view diffusion base model,

    R. Shi, H. Chen, Z. Zhang, M. Liu, C. Xu, X. Wei, L. Chen, C. Zeng, and H. Su, “Zero123++: a single image to con- sistent multi-view diffusion base model,” arXiv preprint arXiv:2310.15110, 2023

    2023 arXiv

  33. [41]

    Deocc-1-to-3: 3d de-occlusion from a single im- age via self-supervised multi-view diffusion,

    Y . Qu, S. Dai, X. Li, Y . Wang, Y . Shen, L. Cao, and R. Ji, “Deocc-1-to-3: 3d de-occlusion from a single im- age via self-supervised multi-view diffusion,”arXiv preprint arXiv:2506.21544, 2025. 2

    2025 arXiv

  34. [42]

    Lrm: Large recon- struction model for single image to 3d,

    Y . Hong, K. Zhang, J. Gu, S. Bi, Y . Zhou, D. Liu, F. Liu, K. Sunkavalli, T. Bui, and H. Tan, “Lrm: Large recon- struction model for single image to 3d,” arXiv preprint arXiv:2311.04400, 2023. 2

    2023 arXiv

  35. [43]

    Lgm: Large multi-view gaussian model for high-resolution 3d content creation,

    J. Tang, Z. Chen, X. Chen, T. Wang, G. Zeng, and Z. Liu, “Lgm: Large multi-view gaussian model for high-resolution 3d content creation,” in European Conference on Computer Vision, pp. 1–18, Springer, 2024

    2024

  36. [44]

    Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models,

    J. Xu, W. Cheng, Y . Gao, X. Wang, S. Gao, and Y . Shan, “Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models,” arXiv preprint arXiv:2404.07191, 2024. 2

    2024 arXiv

  37. [45]

    Clay: A controllable large-scale generative model for creating high-quality 3d assets,

    L. Zhang, Z. Wang, Q. Zhang, Q. Qiu, A. Pang, H. Jiang, W. Yang, L. Xu, and J. Yu, “Clay: A controllable large-scale generative model for creating high-quality 3d assets,” ACM Transactions on Graphics (TOG) , vol. 43, no. 4, pp. 1–20,

  38. [46]

    Structured 3d latents for scalable and versatile 3d generation,

    J. Xiang, Z. Lv, S. Xu, Y . Deng, R. Wang, B. Zhang, D. Chen, X. Tong, and J. Yang, “Structured 3d latents for scalable and versatile 3d generation,” in Proceedings of the Computer Vi- sion and Pattern Recognition Conference, pp. 21469–21480,

  39. [47]

    Hunyuan3d 2.1: From images to high-fidelity 3d assets with production- ready pbr material,

    T. Hunyuan3D, S. Yang, M. Yang, Y . Feng, X. Huang, S. Zhang, Z. He, D. Luo, H. Liu, Y . Zhao,et al., “Hunyuan3d 2.1: From images to high-fidelity 3d assets with production- ready pbr material,”arXiv preprint arXiv:2506.15442, 2025

    2025 arXiv

  40. [48]

    Direct3d: Scalable image-to-3d gen- eration via 3d latent diffusion transformer,

    S. Wu, Y . Lin, F. Zhang, Y . Zeng, J. Xu, P. Torr, X. Cao, and Y . Yao, “Direct3d: Scalable image-to-3d gen- eration via 3d latent diffusion transformer,” arXiv preprint arXiv:2405.14832, 2024

    2024 arXiv

  41. [49]

    Hunyuan3d 2.0: Scal- ing diffusion models for high resolution textured 3d assets generation,

    Z. Zhao, Z. Lai, Q. Lin, Y . Zhao, H. Liu, S. Yang, Y . Feng, M. Yang, S. Zhang, X. Yang, et al., “Hunyuan3d 2.0: Scal- ing diffusion models for high resolution textured 3d assets generation,” arXiv preprint arXiv:2501.12202, 2025. 2, 6, 1

    2025 arXiv

  42. [50]

    Marching cubes: A high resolution 3d surface construction algorithm,

    W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3d surface construction algorithm,” in Seminal graphics: pioneering efforts that shaped the field , pp. 347– 353, 1998. 2

    1998

  43. [51]

    Neural discrete repre- sentation learning,

    A. Van Den Oord, O. Vinyals, et al., “Neural discrete repre- sentation learning,” Advances in neural information process- ing systems, vol. 30, 2017. 2

    2017

  44. [52]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing sys- tems, vol. 30, 2017. 2

    2017

  45. [53]

    Meshanything v2: Artist-created mesh generation with adjacent mesh tokenization,

    Y . Chen, Y . Wang, Y . Luo, Z. Wang, Z. Chen, J. Zhu, C. Zhang, and G. Lin, “Meshanything v2: Artist-created mesh generation with adjacent mesh tokenization,” arXiv preprint arXiv:2408.02555, 2024. 2

    2024 arXiv

  46. [54]

    Edgerunner: Auto-regressive auto-encoder for artistic mesh generation,

    J. Tang, Z. Li, Z. Hao, X. Liu, G. Zeng, M.-Y . Liu, and Q. Zhang, “Edgerunner: Auto-regressive auto-encoder for artistic mesh generation,” arXiv preprint arXiv:2409.18114, 2024

    2024 arXiv

  47. [55]

    Meshtron: High-fidelity, artist-like 3d mesh generation at scale,

    Z. Hao, D. W. Romero, T.-Y . Lin, and M.-Y . Liu, “Meshtron: High-fidelity, artist-like 3d mesh generation at scale,” arXiv preprint arXiv:2412.09548, 2024. 2

    2024 arXiv

  48. [56]

    Sparsegpt: Massive language models can be accurately pruned in one-shot,

    E. Frantar and D. Alistarh, “Sparsegpt: Massive language models can be accurately pruned in one-shot,” in Interna- tional Conference on Machine Learning , pp. 10323–10337, PMLR, 2023. 3

    2023

  49. [57]

    Movement pruning: Adap- tive sparsity by fine-tuning,

    V . Sanh, T. Wolf, and A. Rush, “Movement pruning: Adap- tive sparsity by fine-tuning,”Advances in neural information processing systems, vol. 33, pp. 20378–20389, 2020. 3

    2020

  50. [58]

    Gptq: Accurate post-training quantization for generative pre-trained transformers,

    E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “Gptq: Accurate post-training quantization for generative pre-trained transformers,” arXiv preprint arXiv:2210.17323,

    arXiv

  51. [59]

    Smoothquant: Accurate and efficient post-training quanti- zation for large language models,

    G. Xiao, J. Lin, M. Seznec, H. Wu, J. Demouth, and S. Han, “Smoothquant: Accurate and efficient post-training quanti- zation for large language models,” in International Confer- ence on Machine Learning, pp. 38087–38099, PMLR, 2023. 3

    2023

  52. [60]

    Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

    W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” Journal of Machine Learning Research , vol. 23, no. 120, pp. 1–39, 2022. 3

    2022

  53. [61]

    Moa: Mixture of sparse attention for automatic large language model com- pression,

    T. Fu, H. Huang, X. Ning, G. Zhang, B. Chen, T. Wu, H. Wang, Z. Huang, S. Li, S. Yan, et al. , “Moa: Mixture of sparse attention for automatic large language model com- pression,” URL https://arxiv. org/abs/2406.14909, 2024. 3

    2024

  54. [62]

    Better & faster large language models via multi-token prediction,

    F. Gloeckle, B. Y . Idrissi, B. Rozi `ere, D. Lopez-Paz, and G. Synnaeve, “Better & faster large language models via multi-token prediction,” arXiv preprint arXiv:2404.19737 ,

    arXiv

  55. [63]

    Speech-language mod- els with decoupled tokenizers and multi-token prediction,

    X. Fan, Z. Sun, Y . Gao, J. Xiong, H. Yan, Y . Cao, J. Sun, S. Li, Z. Zhang, Z. Xi, et al. , “Speech-language mod- els with decoupled tokenizers and multi-token prediction,” arXiv preprint arXiv:2506.12537, 2025

    2025

  56. [64]

    Medusa: Simple llm inference acceleration framework with multiple decoding heads,

    T. Cai, Y . Li, Z. Geng, H. Peng, J. D. Lee, D. Chen, and T. Dao, “Medusa: Simple llm inference acceleration framework with multiple decoding heads,” arXiv preprint arXiv:2401.10774, 2024

    2024 arXiv

  57. [65]

    V ocalnet: Speech llm with multi-token predic- tion for faster and high-quality generation,

    Y . Wang, H. Liu, Z. Cheng, R. Wu, Q. Gu, Y . Wang, and Y . Wang, “V ocalnet: Speech llm with multi-token predic- tion for faster and high-quality generation,” arXiv preprint arXiv:2504.04060, 2025. 3

    2025 arXiv

  58. [66]

    Spectr: Fast speculative decoding via optimal trans- port,

    Z. Sun, A. T. Suresh, J. H. Ro, A. Beirami, H. Jain, and F. Yu, “Spectr: Fast speculative decoding via optimal trans- port,” Advances in Neural Information Processing Systems , vol. 36, pp. 30222–30242, 2023. 3

    2023

  59. [67]

    Accelerating auto-regressive text-to-image genera- tion with training-free speculative jacobi decoding,

    Y . Teng, H. Shi, X. Liu, X. Ning, G. Dai, Y . Wang, Z. Li, and X. Liu, “Accelerating auto-regressive text-to-image genera- tion with training-free speculative jacobi decoding,” arXiv preprint arXiv:2410.01699, 2024. 3

    2024 arXiv

  60. [68]

    Zipar: Parallel autoregressive image generation through spatial locality,

    Y . He, F. Chen, Y . He, S. He, H. Zhou, K. Zhang, and B. Zhuang, “Zipar: Parallel autoregressive image generation through spatial locality,” in Forty-second International Con- ference on Machine Learning, 2024. 3

    2024

  61. [69]

    Lora: Low-rank adaptation of large language models.,

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chen, et al. , “Lora: Low-rank adaptation of large language models.,” ICLR, vol. 1, no. 2, p. 3, 2022. 5

    2022

  62. [70]

    Objaverse: A universe of annotated 3d objects,

    M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi, “Objaverse: A universe of annotated 3d objects,” in Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pp. 13142–13153, 2023. 5

    2023

  63. [71]

    Break the sequen- tial dependency of llm inference using lookahead decoding,

    Y . Fu, P. Bailis, I. Stoica, and H. Zhang, “Break the sequen- tial dependency of llm inference using lookahead decoding,” arXiv preprint arXiv:2402.02057, 2024. 6

    2024 arXiv

  64. [72]

    Speculative decoding: Exploiting speculative exe- cution for accelerating seq2seq generation,

    H. Xia, T. Ge, P. Wang, S.-Q. Chen, F. Wei, and Z. Sui, “Speculative decoding: Exploiting speculative exe- cution for accelerating seq2seq generation,” arXiv preprint arXiv:2203.16487, 2022. 6 XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi...

    2022 arXiv

  65. [73]

    Implementation Details Training was performed on two NVIDIA A800 GPUs and took approximately eight hours

    Appendix 7.1. Implementation Details Training was performed on two NVIDIA A800 GPUs and took approximately eight hours. We used AdamW as the optimizer (β1 = 0.9, β2 = 0.99). To improve training stability, we applied global norm clipping to the gradients, limiting their overall...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.