TRIM: Scalable 3D Gaussian Diffusion Inference with Temporal and Spatial Trimming
Authors: Zeyuan Yin
Xiaoming Liu
Venue: NeurIPS 2025 poster ()
Keywords: 3D Generation
Gaussian Splatting
Stable Diffusion
Inference-Time Scaling
Primary area: deep_learning
TL;DR: We propose TRIM, a post-training framework that accelerates 3D Gaussian diffusion by pruning trajectories and background tokens, enabling efficient inference-time scaling without sacrificing quality.

Abstract: Recent advances in 3D Gaussian diffusion models suffer from time-intensive denoising and post-denoising processing due to the massive number of Gaussian primitives, resulting in slow generation and limited scalability along sampling trajectories.
To improve the efficiency of 3D diffusion models, 
we propose $\textbf{TRIM}$ ($\textbf{T}$rajectory $\textbf{R}$eduction and $\textbf{I}$nstance $\textbf{M}$ask denoising), a post-training approach that incorporates both temporal and spatial trimming strategies, to accelerate inference without compromising output quality while supporting the inference-time scaling for Gaussian diffusion models.
Instead of scaling denoising trajectories in a costly end-to-end manner, we develop a lightweight selector model to evaluate latent Gaussian primitives derived from multiple sampled noises, enabling early trajectory reduction by selecting candidates with high-quality potential.
Furthermore, we introduce instance mask denoising to prune learnable Gaussian primitives by filtering out redundant background regions, reducing inference computation at each denoising step.
Extensive experiments and analysis demonstrate that TRIM significantly improves both the efficiency and quality of 3D generation.

TRIM: Scalable 3D Gaussian Diffusion Inference with Temporal and Spatial Trimming
Zeyuan Yin Xiaoming Liu
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA {zeyuan, liuxm @msu edu
DifiSplar
"A candy house."
TRIM
DidiSplat
TRIM
Input
Figure Comparison between baseline DiftSplat [1] (Rows 1&3) and our TRIM (Rows 2&4). text-to-3D, TRIM yields houses more aligned with the "candy" characteristics. For image-to-3D, TRIM generates more realistic details on eyes, tails, and horns. TRIM also reduces inference time from 8 to seconds.
Abstract
Recent advances in 3D Gaussian diffusion models suffer from inte-intensive denoising and post-denoising processing due the massive number of Gaussian primitives, resulting in slow generation and limited scalability along sampling trajectories. To improve the efficiency of 3D diffusion models, propose TRIM (Trajectory Reduction and Instance Mask denoising), post-training approach that incorporates both temporal and spatial trimming strategies, accelerate inference without compromising output quality while supporting the inference-tine scaling for Gaussian diffusion models. Instead of scaling denoising trajectories in costly end-to-end manner, we develop a lightweight selector model to evaluate latent Gaussian primitives derived from multiple sampled noises, enabling early trajectory reduction by selecting candidates with high-quality potential. Furthermore, we introduce instance mask denoising to prune learnable Gaussian primitives by filtering out redundant background regions, reducing inference computation each denoising step. Extensive experiments and analysis demonstrate that TRIM significantly improves both the efficiency and quality of 3D generation.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Recent advancements [2, 3, 1] in text-to-3D generation have transformed creative industries, enabling the synthesis of high-fidelity 3D objects from textual descriptions for applications in filmmaking,
gaming design, and virtual reality. Inspired by diffusion models' success in 2D image generation [4, 5], text-to-3D generation has made significant progress with the integration of diffusion models, enabling high-quality 3D synthesis from text prompts. Recent works, such as DiffSplat [1] and Gaussian [6], have shown that re-purposing image diffusion models with 2D prior for Gaussian primitives generation can produce highly realistic and semantically consistent 3D objects.
To better utilize generative models, numerous post-training techniques have been proposed to en- hance models' efficiency and output quality. In the 2D domain, image generation speed has been effectively increased via efficient inference methods like diffusion distillation [7, 8] and model com- pression [9, 10J. Concurrently, generation quality is often improved via post-training strategies such reinforcement learning (RL)-based one-ttnnig [11, 12] and best-of-N selection with inference-time scaling [13, 14]. However, migrating these 2D diffusion post-training techniques to 3D diffusion is widely regarded as difficult due to two main restrictions. First, the unstructured nature of the 3D Gaussian Splatting representation, where numerous primitives are scattered in 3D space, complicates structured compression and optimization. The second is the extensive computational pipeline where the 3DGS diffusion model sequentially executes image-to-3D reconstruction, 3DGS generation, and rendering, shortly as Lenoon-Ge--endde process. Compared to simple 2D denoising, this three-stage process incurs considerably more computation cost and hinders its inference time scaling ability and further RL-based fine-tuning.
To address these challenges, we propose TRIM (Trajectory Reduction and Instance Mask denoising). novel post-training framework designed for the 3D diffusion's 's Recon-Gen Gen-Render pipeline to enhance the inference efficiency and scalability without compromising quality. Our design is motivated by identifying and resolving two key inefficiencies in existing 3D diffusion pipelines. (1) At the trajectory level, we observe that inference-time scaling by increasing the number of sampled trajectories significantly improves the chance of producing high-quality 3D assets, but requires extensive computation in 3D diffusion models. To overcome this costly end-to-end denoising, our TRIM introduces a latent selector identify promising latent trajectories early in the denoising process. This significantly reduces the number of fully denoised trajectories and 3D renderer calls. (2) At the token level, we identify a key drawback of prior works: the unnecessary optimization of transparent background regions, which leads to inefficient denoising. To resolve this. TRIM develops an instance masking mechanism to detect and climinate background splat tokens. This focuses computational resources on the foreground object. Subsequently. a post-denoisngg correction module utilizes the instance mask to mitigate rendering artifacts by adjusting Gaussian primitive parameters.
Our TRIM conducts the temporal and spatial trimming to address both trajectory-level and token-level redundancies, achieving substantial savings in computational resources. Our main contributions summarized as follows:
We propose a novel framework for accelerating 3D Gaussian diffusion inference by per- forming trajectory- and token-level pruning, which involves a three-stage inference procedure of trajectory reduction, instance mask denoising, and post-denoising correction.
Our post-training framework is model-agnostic and can be integrated into variety of transformer-based 3D diffusion model backbones without the need of retraining.
Extensive experiments demonstrate that TRIM achieves higher efficiency in text-to-3D and image-to-3D generation and improves generation quality across multiple benchmarks.
Related Work
3D Generation. Diffusion models [15] have become a dominant approach for the visual generative
task. There are a lot works [2, 16, 17, 1, 18] proposed for 3D object generation in the 3D domain. Specifically, GVGEN [2] achieves 3D Gaussian diffusion by transforming sparsely located 3D Gaussians into more structured 3D volumes. LRM [16] is large reconstruction model by scaling the training data and parameters in feed-forward transformer-based models. InstantMesh [17] further integrates novel view synthesis models into LRM with more reference images from different

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Trajectory Reduction
Instance Mask Denoising
Post-denoising Processing
2
Selector
Docodar
Renderer
ZK
7K AK
Zo
Figure 2: Overview of our TRIM framework. It consists of three stages: given a text prompt A rocking horse with scroll-word work, in the first stage, multiple denoising trajectory candidates reduced to one trajectory with high-quality potential. In the second stage, instance mask performed to simplify background regions during denoising process. In last stage, the Gaussian primitive parameters are corrected by the mask.
viewpoints. To leverage the prior knowledge in the advanced image diffusion models, like PixArt- E [4] and Stable Diffusion [5], which are trained on the large-scale Internet data, DifiSplat [i] and Gaussian Atlas [6] transform the 2D model structure for 3D Gaussian representation enable
fine-tuning a well-pretrained image diffusion for 3D Gaussian generation.
Inference-Time Scaling, Inference-time scaling has been demonstrated be effective strategy for improving performance in Large Language Models (LLMs), enhancing reasoning capabilities and output quality through longer inference [19, 20, 21]. Inspired by I.LMs, similar trends explored and validated in image diffusion models [22, 13, 14), where sampling multiple denoising trajectories noise initializations improves generation quality. Several recent works emphasize
the significance of diverse noise seeds in diffusion sampling. and propose strategies to optimize or select better denoising trajectories through scoring [13], training-free search [14, 23], or optimization mechanisms [24]. However, inference-tin scaling remains underexplored in 3D diffusion models.
Compared image generation, 3D generation models, like DiftSplat [1], involve additional rendering process and higher denoising costs due to large amount of Gaussian primitives, making it impractical to directly apply evaluation or optimization on noisy 3D assets. this work, we address this gap by exploring inference-time scaling in 3D diffusion via a lightweight trajectory selection framework that enables efficient quality improvement with minimal overhead.
Efficient Diffusion Models. Recent works have explored various strategies to improve the efficiency of diffusion- generative models. One prominent direction is to reduce the token complexity in transformer-base architectures. SANA [10] introduces a linear attention mechanism tailored for high-resolution synthesis, enabling linear-time complexity with respect sequence length. Token Merging (ToMe) [25] proposes to merge redundant tokens with similar representation, significantly accelerating diffusion inference with minimal quality loss. Similarly, Dif[CR [26] learns adaptive token compression ratios per layer and timestep, providing fine- iine-grained trade-off between speed and quality. However, these methods often rely on architecture updates and model retraining. which limit their applicability in 3D domains. In contrast, we propose training-free instance mask denoising method to eliminate redundant background tokens during inference, which easily plugged into the off-the-shelf transformer-based 3D diffusion models without any retraining cost.
3 Method
In this section, we present the proposed TRIM framework. First, provide overview of the method Sec. 3.1. Then, we detail two main components integrated into diffusion inference: (1) Trajectory Reduction in Sec. 3.2 and (2) Instance Mask Denoising in Sec. 3.3.
3.1 Overview of TRIM
Our proposed TRIM framework accelerates diffusion inference for 3D generation by integrating two novel components: (1) Trajectory Reduction, which uses a latent selector to identify and retain the most promising trajectory an early timestep, thereby reducing redundant denoising on multiple trajectories; and (2) Instance Mask Denoising, which leverages a training-free mechanism to detect and progressively mask background regions to reduce token computation in denoising transformers.

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As illustrated in Figure 2. TRIM initially samples diverse noises and generates multiple denoising trajectories. A lightweight Latent Selector is trained to evaluate intermediate latents and identify the trajectory with high-quality potential. The chosen trajectory continues for the remaining denoising steps while other trajectories are terminated, significantly reducing inference cost. In parallel. TRIM introduces a spatial mask strategy to compress background tokens during denoising. This mask is self- detected from latent features using a reference-attention mechanism and progressively expanded to ensure stability. Integrated with token merging and post-denoising correction, TRIM enables efficient background filtering while preserving generation quality. These two components are integrated into the diffusion inference for faster and higher-qualig 3D generation.
3.2 Trajectory Reduction
To enable early trajectory reduction, we introduce a latent selector to identify the candidate with the highest quality potential during the denoising process in the latent space. We formulate training for the latent selector as a knowledge distillation problem. The goal is to distill knowledge from the Geroder-Renderee Renderer-Evaluator joint model, where the selector learns to predict the quality of latent representations by approximating the relationship between latent splats on the denoising trajectory and scores evaluated at the final rendered images.
Given a prompt p and a denoising latent trajectory, denoted as T (ZT: 27-1," selector is optimized by aligning its prediction on an intermediate latent z, with the evaluation score of the Evaluator on the rendered images derived from Zo. Specifically, the objective is formulated as:
3.) the latent
gelector = arg min L (Selectoro(p. zi), Evaluator (p. Renderer (CM: Decoder(Z)))))
(I)
0
where (() denotes a distillation loss function and Cm represents the camera matrix used for Gaussian splatting rendering. The latent is selected at timestep from the denoising trajectory T.
Instead of directly training the selector by distilling knowledge from 3ID diffusion model, adopt an offline distillation strategy, consisting of two decoupled stages: data synthesis and selector training, to reduce training costs significantly. In the first stage, we infer the text-to-3D diffusion model to generate 3D views and apply metric-based evaluations, constructing a dataset of (trajectory, score) pairs. In the second stage, we train the latent selector on this pre-processed dataset for pairwise selection tasks, predicting the trajectory with higher quality. This offline distillation strategy effectively alleviates the difficulty in training the latent selector.
Data Synthesis. As shown in Algorithm in the Appendices, we construct a triplet dataset of (description prompts, denoising trajectories, evaluation scores). Specifically, we leverage ChatGPT to generate a set of 100 vivid textual prompts, denoted as P E TR. 100 x Lyssert each describing a single object, multiple objects, or an object within a scene, For each prompt Pj P. we generate 64 diverse latent trajectories using a denoising model G. Each trajectory, denoted T, = [zip. 2 -1:.:..... z where zi is the latent state at timestep t, is produced by inferring & with the random seed r. The final latent state z of each trajectory is decoded into a 3D object representation O; using a VAE decoder and then this object is rendered into a set of images I₁- E R. Vou xW from Vou viewpoints, specified by a camera matrix Ca.. The quality of the rendered images is evaluated using an off-the-shell image metric evaluator, yielding a scalar score s;. This evaluation score s; E serves as the proxy for the overall quality of the whole denoising trajectory T, that produced z
where N is the number of prompts and M is the number of random seeds (N = 100, M = 64 in our settings). This generation approach ensures diversity in the generated outputs by leveraging stochasticity introduced by the distinct seeds, enabling an expansive exploration space for selector training.
Finally, we assemble a triplet dataset D { (pj, (TT,,, st)],M 1
N
Latent Selector Training. Considering challenges in distilling knowledge from a large Decoder- Senderer-Evaluator joint model to a lightweight selector in Eq. I, we turn to train the latent selector under pairwise selection setting. Given two intermediate latent codes z, and z at an intermediate timestep (0 < I < T' along two denoising trajectories Ti and Tz conditioned on the same prompt, the model is trained to predict which latent corresponds to higher-quality 3D object. The supervision signal is determined based on the associated evaluation scores, with the latent corresponding to the higher score designated as the preferred choice.

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Temporal Trimming Scheme
Spatial Trimming Scheme
Selector
Selector
Reference
Flateen
Attention
Spil
Selector
Schaduler
Nergo
Figure 3: Details about temporal (left) and spatial (right) trimming schemes. In temporal trimming, high-quality trajectories are selected early using a lightweight selector. In spatial trimming. the mask is detected and utilized to separate and merge background tokens, reducing the number of tokens processed during denoising.
Our lightweight selector architecture comprises a CNN feature extractor and an MLP discriminator with a prompt fusion mechanism. Specifically, given two latent representations z! and z with scores $1 and the selector is trained to predict the preference label = 1ss > $2), which is if the evaluation score 81 exceeds s2 and 0 otherwise. Firstly, we extract the latent features fi = CNN(z)) and fg = CNN(zī). The MLP discriminator, equipped with prompt fusion mechanism, concatenates the feature difference - fz with the prompt embedding ep (from prompt p). then predicts the label ý = MLP((f. - f2; epl). The selector (CNN + MLP) is optimized using a binary cross-entropy loss:
CBCE(y, 1) = (y log ( + (1 y) log(1 (())))
(2)
where (-) denotes the sigmoid activation function.
Temporal Trimming Scheme. In the diffusion model inference with N samples, we leverage the well-trained selector to reduce denoising trajectories at a given timestep using a pairwise tournament selection strategy, shown in the left scheme of Figure 3. Identifying high-quality trajectory early reduces the total denoising steps, originally NT. With the reduction strategy, we denoise N candidates from timestep T to t, then I candidate from t - to 0, totaling NT - (N 1)t steps, saving (N - 1)t steps. Furthermore, since the number of final latents is reduced from N to 1, the post-denoising processing costs, including VAE decoding and Gaussian splatting rendering, also reduced by N times.
3.3 Instance Mask Denoising
We introduce an instance masking mechanism during denoising to eliminate redundant background splats in the Gaussian splatting grid. Specifically, as shown in the right scheme of Figure 3, we first detect a mask to distinguish the instance and background based on the latent representation. Then the mask is gradually applied drop the background and maintain the instance region in the latent representation, effectively reducing the number of tokens processed in the denoising transformer.
Instance Mask Detection via Reference Attention. Inspired by the patch-level attention mechanism in DINO [27]. which highlights semantically salient regions via attention between the [CLS] token and patch tokens, we propose lightweight, model-free instance mask detection via corner-veference attention to detect binary segmentation mask that distinguishes instance regions from the background on the latent representation. Specifically, we observe that the four corners of the latent feature grid typically correspond to transparent background regions. We extract and aggregate features from these corner regions to form a reference token, denoted as [REF]. For each patch in the feature grid, we compute the similarity between its feature and the [REF] token. Regions with low similarity to [REF] are treated as potential instance regions. We threshold the similarity map using a predefined value 7 to obtain the binary mask, where and represent the foreground instance and background regions, respectively. Corner-reeerence attention is less reliable in early denoising steps due to noisy latent representations, which hinder accurate instance localization. As denoising progresses, the latent becomes more structured, enabling more precise mask detection. Thus, we apply the mask selectively during the middle-to-late denoising stages.
Progressive Mask Expansion Scheduler. enable a smooth transition from unmasked to masked region denoising, we adopt progressive mask expansion, spatially controlled strategy that gradually expands the masked area from the outer boundary toward the center of the latent grid. As illustrated in the scheduler module of Figure 3, assuming 16 x 16 latent patch grid (e.g., in Stable Diffusion 3),
5

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Text Prompt
TRIM
DifiSplat
Image Input
TRIM
Dillppat
perfare
botts
an ancleet lotter book
skiins hinpily wits bit
Figure 4: Qualitative results and comparisons on Text-to-3D (left) and Image-to-3D (right) generation.
we divide the denoising process into four phases. In the first phase, only the outermost row/column patches are subjected to background masking. In subsequent phases, the masked region is expanded to include 4, 6, and finally 8 (entire grid) row/column patches. The progressive expansion reduces the risk of artifacts introduced by hard masking and improves robustness during early-to-min denoising steps, while still enabling efficient background reduction in later stages.
Token Merging and Padding, Once the instance mask is obtained via corner-reference attention and the mask region is scheduled through progressive mask expansion, they are combined produce final binary mask that separates foreground instance and background regions. All background-region tokens in the latent grid are aggregated into a merged background token, denoted as [BG], and then concatenated to the flattened foreground instance token sequence. The instance-region tokens along with [BG] token are fed into the denoising transformer. After denoising, the output [BG] token is padded back in the masked background positions, restoring the full 2D latent grid structure. The token merging and padding process ensures compatibility with downstream modules, including VAE decoding and the Gaussian splatting rendering.
Post-denoising Correction. After denoising, the latent representation is decoded into a set of Gaussian primitives with explicit parameters, which are then rendered into images using Gaussian splatting guided by a camera matrix Cas- However, since our approach is training-free and directly employs an off the-hh Gaussian diffusion model for inference, the model lacks prior knowledge to the introduced mask and [BG] token during training. Consequently, the [BG] token not optimally denoised to a fully transparent background, leading to artifacts in the rendered images. To address this, we utilize the mask generated from the last step's latent representation to correct the parameters of Gaussian primitives, as shown in Phase 3 of Figure 2. Specifically, we set the background- region Gaussian primitives' opacity values to zero. The corrected parameter effectively eliminates background artifacts and the influence of background primitives on the rendered images.
4 Experiments
4.1
Experimental Settings
We adopt DifiSplat [1] our main backbone model, trained on the G-Objaverse dataset [28]. For the text-to-31) generation task, we evaluate on ThBench [29]. which consists of 300 descriptive prompts about a single object, single object with surrounding context, or multiple objects. We report the CLIP Similarity Score [30] and CLIP R-Precision [31] using ViT-B/32 to measure aligmment between the input prompts and the rendered images. Additionally, we utilize ImageReward [32] to assess perceptual quality based on human aesthetic preference. For the image-to-3D generation task, we randomly select 300 objects from the Google Scanned Objects (GSO) dataset [33]. For each object, the front-facing image serves as input for 3D generation, while rendered images from other viewpoints act as ground-truth. We evaluate reconstruction fidelity using PSNR, SSIM, and LPIPS [34]. For our TRIM configuration, trajectory reduction is performed at the midpoint of the denoising process (t = T/2), and the selector model is trained on latent representations extracted at this intermediate timestep. More details about experiments are contained in the Appendices.

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Table Quantitive results on Bench text-to-3D generation.
Benchmark
Metric
GVGEN [2]
DIRECT-3D [3]
LGM [35]
DifiSplat [1]
TRIM (Ours)
CLIP Sim.%
23.66
24.80
29.96
30.95
31.58
Single
CLIP R- Prec.s,
23.25
30.75
78.00
81.00
81.42
Object
ImageReward
-2.15
-2.00
-0.72
-0.49
0.12
Single
CLIP Sim.%
22.65
23.05
27.79
30.20
31.48
Object
CLIP R-Prec.%
26.75
25.75
55.00
80.75
88.25
w/ Sur.
ImageReward
-2.25
-2.19
-1.77
-0.67
-0.50
CLIIP S
21.48
21.89
27.07
29.46
30.11
Multiple
CLIP R-Prec.%
8.00
7.75
51.00
69.50
70.01
Objects
ImageReward
-2.27
-2.24
-1.73
-0.84
-0.24
Table 2: Quantitive results on GSO dataset for image-to Image-to-3D reconstruction.
Metrie
LGM [35]
InstantMesh [17]
DifiSplat [1]
TRIM (Ours)
PSNRT
14.90
15.53
16.20
16.78
SSIMT
0.71
0.77
0.79
0.82
LPIPS
0.25
0.22
0.19
0.17
4.2 Text-to-3D Generation
As shown Table 1, on the Single Object category of Bench,, TRIM achieves the highest CLIP Similarity and CLIP R- Precision, indicating stronger semantic alignment between the generated 3D assets and the input text prompts. Moreover, TRIM the only method to yield a positive ImageReward score of 02, suggesting higher visual fidelity from human preference perspective. On more challenging cases such as Single Object with Surroundings and Multiple Objects, TRIM maintains clear lead over all the baseline methods, with improvements of 0.73 and 0.65 in CLIP Similarity over DiffSplat, respectively. The ImageReward scores these sub remain competitive, highlighting TRIM's 's robustness in complex scenes. These results demonstrate the effectiveness of TRIM's temporal and spatial trimming strategies on enhancing the quality of generated 3D assets.
4.3 Image-to-3D Reconstruction
Table 2 and Figure 4 report the quantitative and qualitative results the GSO dataset for the image- to-3D reconstruction task. TRIM improves the reconstruction performance over strong baselines such DiffSplat, but the gains relatively smaller than those observed in the text-to-3D setting. We attribute this to the stronger conditioning signal provided by the input images compared to text prompts, which inherently reduces the diversity among different sampled trajectories. As result, the benefit from inference-time trajectory scaling becomes less obvious. Nevertheless, TRIM consistently enhances the PSNR, SSIM, and LPIPS scores, demonstrating its general effectiveness in improving 3D reconstruction quality under image conditioning.
4.4 Ablation and Analysis
Qualitative Analysis of Inference Step Scaling. Figure 5 presents qualitative comparisons of 3D assets generated by DiffSplat with an SDD-3.5-Medium backbone using different numbers of inference steps 7' [10, 40, 80). As the number of steps increases, the generated outputs generally become more refined and detailed. However, beyond a certain step count, the visual improvements begin saturate or even degrade slightly. In the first example about single object generation, excessive denoising introduces fake details or artifacts, such debris-lite -ike details, which aren' implied by the prompt. In the second example about multiple object generation, longer denoising leads to semantic inconsistencies: the box handle becomes unnaturally large, and the number of pocket watches conflicts with the prompt description. In the third example about the single object with surroundings, model fails to preserve semantic details such as the human figure at the higher inference steps. This phenomenon, where more inference steps may cause semantic drift or over-smoothing, particularly for complex scenes, has been observed in recent studies [36, 37, 13]. These observations highlight the
7

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Procupt
T 10
View Compationn
T=
T
Bew bydeem.
A sikingg gol pocka vunch
A gundrarr eancrigg planccat
Figure 5: Qualitative comparisons inference step scaling. Best viewed with
31.5
mapeRewerd Difisgias Imagelloward
31.6
0.0
Difisoiat
CUP Similanty 0%)
31.4-
31.0
-0.2
30.5
31.0-
-0.4
30.8
InageRoward
30.0
20
Budget (sless)
40
80
-05
20
Selector Application Timestep (%)
80
100
Figure Trajectory scaling vs. inference step scaling. TRIM improves steadily via trajectory scaling, while DiftSplat plateaus and eventually degrades with step scaling.
Figure Ablation results on selector application time. Each data point indicates the performance when applying the selector at certain timestep.
limitation of naive inference-step scaling and motivate the need for smarter inference-tin time trajectory scaling strategies.
Analysis of Inference-time Scaling, Figure 6 compares the evaluation results of Trajectory scaling and inference step scaling. To ensure a fair comparison under the total number of inference steps, the baseline method DiftSplat scales the number of denoising steps from 10 to 80 on one trajectory, while TRIM fixes the denoising step count to 10 and scales the number of sampled trajectories from 1 to 8. The results reveal that TRIM with trajectory scaling achieves steadily higher performance across both CLIP Similarity and ImageReward scores as more trajectories are sampled. contrast, the DiffSplat with the inference step scaling shows limited improvement in CLIP scores and clear decline in ImageReward scores with more denoising steps, likely due to over-smoothing or the accumulation of artifacts. These findings suggest that inference-ting time computation is better spent on trajectory diversity rather than excessively long denoising steps.
Ablation on Selector Architecture. We explore the design space of the selector model using a C-layer CNN feature extractor followed by L--ayer MLP the diseriminator, optionally conditioned on the text prompt from Beenhh-Singl.. To evaluate selector performance, we report two kinds of results on selector training and 3D generation: (1) pairwise accuracy measures binary classification accuracy on the validation set of pairwise comparisons during selector training; (2) CLIP similarity and ImageReward scores measure the generation quality of output 3D objects after applying the selector to trajectory reduction and rendering. Table 3 shows that convolutional feature extractor crucial for selector performance. Specifically, adding one convolutional layer, from FC2 to Comv/-FC2, leads to substantial improvement in both pairwise accuracy of 20.82% and CLIP similarity of 0.75%. We further notice that increased model complexity does not yield additional gains for the pairwise comparison task. This suggests that lightweight selector with single convolutional layer and a two-layer MT.P is sufficient to capture discriminative latent features for effective selection. Moreover, its minimal computational overhead ensures that it does not introduce

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Table 3: Ablation results Selector architecture.
Table Computation resource comparison.
Pairwise Acc.g
CLIP
ImageReward Score?
FLOPs
Mem. (GB))
Throughput
Architecture
Method
Sim.% t
(T)L
(step/s)f
w/o selector
30.44
-0.51
SD 1D-3.5-Medium
195.68
33.26
13.18
FC2
53.36
30.83
-0,40
IM
165.60
32.85
18.09
Conv1-FC2
74.18
31,58
-0.16
TR
110.07
33.55
13.18
ConvI-FC.
73.90
31.54
-0.20
TRIM
106.31
33.13
18.09
Conv2-FC2
73.09
31.31
-0.22
Conv1- 1-FC2- FCC2--Pptt
73.73
31.14
-0.26
Conv 1-FC3-FCCFF00 Prompt
72.91
31.09
-0.28
31.4
314
31.4
8
Simlarit
20.2
31.0
31.0
20.8
30.8
30.8
TX
WTB Witn BY
WT TRUM
w0b TRIM
30.4
6
30.4g
(seconds))
Runtime (secands)
(seconds)
(a) Ablation results TR.
Ablation results IM.
Ablation studies TRIM.
Figure 8: Ablation results on trajectory scaling with or without TRIM. The integration of TRIM improves generation quality under reduced computational budgets.
noticeable latency to the diffusion inference process, making it practical and efficient component for trajectory reduction in 3D diffusion inference scaling.
Ablation on Selector Application Time. We investigate the impact of applying the trajectory selector different denoising timesteps. Figure 7 shows that both CLIP similarity and ImageReward scores improve as the selector is applied in later timesteps, but the performance gains plateau when applying after the 50% progress. This reflects a trade-off between efficiency and quality: applying the selector too early yields poor performance due to high noise in the latent features at the early stage, while applying too late diminishes the efficiency gain of early pruning. Thus, we adopt the midpoint of the denoising process, 50% of the total steps, as the default selector application point in TRIM.
Ablation TRIM. We analyze the contributions of Trajectory Reduction (TR) and Instance Masking (IM) in Figure Sa and Figure 8b, respectively. We observe that TR primarily improves the CLIP similarity score of the generated 3D outputs with slight increase in processing time, while IM is more effective at reducing overall runtime while maintaining similar CLIP scores. Thus, as shown in Figure 8c, combining both components in TRIM leads improvements in both generation quality and inference efficiency. Notably, TRIM gains generation quality and denoising efficiency under less inference runtime, benefiting from our proposed early trajectory reduction mechanism. This demonstrates that TRIM not only accelerates inference but also leads to higher-quality final 3D outputs.
Diversity Analysis. To investigate the output diversity, we measure the diversity of generated outputs with or without trajectory reduction the Bench-Siinlle dataset. We repeat the generation process 8 and 16 times, and report the evaluation results on both semantic and geometric diversity via various metrics, including CLIP similarity, ImageReward scores, and Chamfer Distance. Specifically, CLIP similarity and IewgeReward scores primarily focus on capturing semantic alignment, while Chamfer
Table 5: Results on output diversity with and without trajectory reduction.
Metric
TR
# repeat of
# repcat of 16
30.89 0.21
30.95 + 0.16
CLIP Sim.
/
31.51 0.14
31.53 + 0.1
-0.45 = 0.05
-0.48 + 0.04
IewaeReward
0.12 + 0.04
0.11 = 0.03
1840, [952, 3060]
1791, [699, 4435] 2310, [1187, 4079]
Chamfer Distance
2042, [1187, 2813]

---

Distance is used for measuring the distance between two point clouds, which is more sensitive to geometric diversity.
In Table 5, we report the average evaluation performance along with the standard deviation on CLIP similarity and ImageReward scores, where the standard deviation is generally regarded key indicator for evaluating diversity. Table 5 also presents the averaged Chamfer Distance (non- normalized), along with the minimum and maximum values (avg.. [min., max.]. via the pair-wise comparison among all the repeated generation results. The reported maximum value of the Chamfer Distance is important to reflect the maximum pairwise difference among multiple outputs and indicate the upper bound of diversity. For the implementation details about Chamfer Distance, we first extract the locations of Gaussian primitives, converting each generated 3D output into a point cloud, as shown in the rightmost column of Figure 1. We then calculate the Chamfer Distance for every pair of point cloud outputs among the or 16 generated objects. Take the 8-output setting as an example: we calculate the Chamfer Distance for (2) 28 pairs of outputs and report the average, minimum, and maximum values.
For the semantic diversity shown in the Table 5, we observe that the model's diversity is slightly reduced when using trajectory reduction, with an increase in the average performance. We attribute this to the selector's effect with an early discarding of unpromising trajectories. By filtering out the less promising generation trajectories, the selector effectively shifts the output distribution of diversity toward higher quality range. This means that TRIM sacrifices diversity in the low-quality outputs to improve the average quality of final outputs. For the geometric diversity, we notice that the range of Chamfer Distances is slightly narrowed and the upper bound is consistently lower when using trajectory reduction across both settings. These observations indicate that the trajectory reduction strategy slightly hurts the diversity of outputs and makes the output distribution narrower.
Thus, we can conclude that initial noises primarily drive the diversity of the outputs, and our latent selector acts as quality filter, enabling the effective selection from a pool of diverse candidates to high-quality outputs with narrower variance/diversity. These results are well-aligned with our motivation and support method to prune unpromising trajectories and shift the output distribution towards a high-quality and narrow range.
Computation Analysis. We analyze the computational efficiency of our proposed TRIM and present the detailed comparison results in Table of the Appendix. Specifically, we measure the total FLOPs across all denoising steps. Compared to the baseline Diff'Splat with the SD-3.5-Medium backbone, Instance Masking (IM) reduces FLOPs by 15.7% and increases throughput from 13.18 to 18.09 steps/s, while also slightly lowering memory usage. In contrast, Trajectory Reduction (TR) reduces the total number of denoising steps in the temporal axis, thus achieving less total FLOPs. Specifically, by selecting a high-quality trajectory at an intermediate timestep 4, the total denoising cost drops from NT to NT - (N 1)4, saving (N - 1)L steps. Additionally, as only one trajectory proceeds to post-denoising, the cost of VAE decoding and Gaussian rendering is also reduced by N times. Therefore, TRIM leverages trajectory reduction and instance masking to improve inference efficiency along the temporal and spatial axes, respectively.
5 Conclusion
In this paper, we propose TRIM, post-training framework for accelerating 3D Gaussian diffusion inference through temporal and spatial inference-time trimming. By introducing latent trajectory reduction and instance masking, TRIM effectively reduces low-quality denoising trajectories and redundant background regions while preserving and improving generation quality. Extensive experi- ments across multiple benchmarks demonstrate that TRIM not only enhances inference efficiency but also enables effective inference-time scaling, outperforming prior state-of-the-an methods in both semantic alignment and aesthetic quality.
Limitations and Future Work. Current 3D diffusion pipelines heavily rely on repurposed 2D backbones, leading to inefficient and repetitive 3D-to-2D structural transformations. This limits spatial trimming to the denoising transformer blocks and prevents its application across the full generative pipeline. To overcome this, it is promising to investigate 3D-structure-aware diffusion that not only leverages the 2D priors but also enables end-to-end spatial trimming for more efficient training and inference.
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A Implementation Details
Datasets. For the text-to-3D generation task, Bench consists of three groups of prompts: single object, a single object with surroundings and multiple objects. Each subgroup contains 100 text descriptions. For the image-to-3D) generation task, due to the access to the data selection list and rendering parameters used in baseline methods, we randomly select 300 objects from the GSO dataset. Each object rendered from four orthogonal viewpoints: front, back, left, and right sides, with fixed elevation angle of 0%
3D Generation Setting. Our 3D generation model with the backbone of Stable- Saable-Diffusion33555 Medium uses the original flow matching Euler ODE solver with 28 steps in the main experiments. The classifier-fis freee guidance scale is set to 7 for text-to-3D generation and 2 for image-to-3D generation. The image input is center cropped and resized to 256x 256 resolution.
Pairwise Data Synthesis. We first use ChatGPT-44 to generate N = 100 text prompts describing diverse objects with various decorations. Following Algorithm 1, we perform data synthesis by sampling M = 64 denoising trajectories per prompt using distinct random seeds. For each trajectory, we decode and render the final denoised output Zo and compute the alignment score 8 using a CLIP-based evaluator.
For data processing, we construct pairwise training and testing data from the 100 latent-score pairs. Each data point consisted of (z). z,. $1 s2), where $1 82 represents the difference between the scores of the two latent trajectories. The dataset was then split into 7:3 ratio for training and testing, respectively. A smaller score distance indicates minimal differentiation between two latent trajectories, making accurate distinction challenging but less critical for selection. Conversely, a larger score distance signifies substantial differences, highlighting the importance of correct classification for effective selection. Due to the pairwise combination method, the data distribution imbalanced, with disproportionately large portion of data samples exhibiting small score distances. To ensure balanced data training, we group the data into bins based on absolute score distance |s| 82), with intervals of 0.1, i.e. [0, 0.1), 10.1, 0.2), .... [1, oo). We ensure that each group contains 200 samples, resulting in a total training dataset 2200 samples.
Selector Training Setting. We employ the Adam W optimizer with a learning rate of 0.001, weight decay of 0.01, and cosine weight decay schedule. Training conducted with a batch size of 64 for 20 epochs. The model takes a pair of latent features (z, z?) ) as input and predicts the pairwise comparison outcome. The target label is defined as (ss > $2). and the loss computed using binary cross-entropy over the softmaxed prediction. The trained selector achieves over 70% test accuracy on the overall pairwise validation set Table 3 and exceeds 90% accuracy for test samples with large score gaps ([8) 82| > 1). This indicates that the well-trained selector effectively captures discriminative features in the latent space and is capable of reliably identifying higher- quality trajectories during inference.
Algorithm Data Synthesis for Latent Selector Training
1: Input: Prompt set P { # trajectories per prompt M, # denoising steps T. camera matrix CM. decoder, renderer, and evaluator models,
2: Output: A triplet dataset D {(p). {(T,, s))
3: Initialize empty dataset D <- 0
4: for j = to N do
D Iterate over prompts Store trajectory pairs for prompt p; b Iterate over random seeds
5: Initialize empty set SJ- <- 0
6: for :ori=1 to M do
7: Set random seed r; t-
8: Generate trajectory Ti = {zy, 2i.
21} < r(p),ra)
9:
Decode final latent O t- Decoder(zi (z))
Render images I, t- Renderen( O, CM)
11:
Evaluate quality + Evaluator (I,)
12: Add pair to set S₁ < S, ((Ti, }t)}
13:
end for
14: Add prompt and pairs to dataset D <- DU (()),,))
15:
end for
16: return D
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B Experiment results
Computation Cost. Table 6 presents the ablation study the computational contributions of the proposed Trajectory Reduction (TR) and Instance Masking (IM) components. We observe that TR significantly reduces the overall FLOPs by decreasing the total number of denoising steps. However, since multiple trajectories are processed in parallel and the runtime is determined by the longest trajectory, the actual GPU memory usage and runtime show slight increase due the additional cost introduced by the selector model. In contrast, IM reduces the computational load of the denoising transformer by pruning background tokens, resulting in clear improvements in both throughput and runtime, Thus, the combined TRIM balances temporal and spatial efficiency, leading to improved overall inference performance.
Table 6: Computation resource comparison. The best performance values are shown in bold, and the second-hest values underlined. All results reported based RTX A6000 GPU.
FLOPs
Mem.
Throughput (step/s)f
Runtime (second)
Method
(T)
(GB)
SDD-3.5-Medimm
195.68
33.26
13.18
8.64
IM
165.60
32.85
18.09
5.16
+ TR
110.07
33.55
13.18
8.74
TRIM 106.31
33.13
18.09
5.24
Results PixArt backbone. Our proposed trajectory reduction strategy is designed to be applicable to most diffusion-baar based architectures, while the instance mask strategy specifically leverages the token structure of maansfomerrbaset backbones. In our main experiments, we choose Stable Diffusion 3.5, the latest diffusion model in the Stable Diffusion series, as the backbone. To further demonstrate generalization, we also apply TRIM to Pix-rt-Sigma with a different diffusion backbone, and present the results on the -Bench-Single dataset in the Table 7. These results show that TRIM improves the performance of DiffSplat with the backbone of PixArt-Sigma demonstrating that TRIM consistently improves both the generation quality and efficiency across various Transformer- backbones.
Table 7: Quantitative results on T Bench-Single
Method
CLIP Sim
ImageReward Scoret
FLOPs (T)
Runtime (second))
SD-3.5-Medium
30.95
-0.49
195.68
8.64
AD-3.5-Medium. TRIM
31.58
0.12
106.31
5.24
PixAri-Sigma
30.73
-0.30
25.18
2.76
PixArt-Sigma TRIM
31.24
-0.13
14.16
2.19
Results 3DGen-Bench We apply the CLiP-based 3DGen-Score model in 3DGen-Bende [38] as an additional metric to enhance our evaluation. The 3DGen- Score model requires multi-view RGB images, normal maps, and text prompt as input. The evaluation output contains five criteria: Geometry Plausibility, Geometry Details, Texture Quality, Geometry-Textue Coherence, and Prompt- Asset Alignment, each with different value scopes. We provide results the B*Bech-Singg dataset in the Table 8, which evaluated by the 3DGen-Score metric. The 3DGen-Score results show that TRIM achieves better performance than the baseline DifiSplat on all five criteria.
Table 3DGen-Bench results BeBench-Single
Method
Geo. Plausibility
Geo. Details
Tex. Quality
Geo.-Tex.
Alignment
DiffSplat TRIM
6462.91 6567.58
8.14
13.43
15790.92 16144.38
8544.38
9.25
13.47
8585.02
C More Visualization
We provide more visualization comparisons on text/image-to-3D generation from DiffSplat and TRIM in this section. Figure 9 shows that TRIM captures finer details, such the "splattered"
22

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colors around the easel and the "wor-oot" characteristics of the tire, demonstrating better alignment with the input text prompts compared DiffSplat. Figure 10 demonstrates that TRIM produces more structurally consistent 3D shapes with fewer distortions, leading to more realistic and coherent generation.
23

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Input
paint splatiered easel."
"A sparkling diamond tiara."
"A pirate flag with skuli."
A
"A polished grand piano."
"A womn-ut rubber tire swing."
Figure 9; Visualization comparisons Bench from DiffSplat (Rows 1.3.5,7&9) and TRIM (Rows 2,4.6,8&10 10).
24

---

Input
A
Figure 10: Visualization comparisons GSO dataset from DifI'Spaat (Rows 1.3.5.7&9) and TRIM (Rows
2,4,6,8 10).
25


Reviews:
[{"review_id": "q3z6v19C0Y", "reviewer": "Reviewer_2U8q", "summary": "This paper presents TRIM, a training-free method to improve the efficiency and scalability of 3D Gaussian diffusion models. TRIM reduces computation by selecting high-quality trajectories early and masking redundant background regions. It can be integrated with existing models without retraining, boosting both efficiency and quality in 3D generation tasks.", "questions": "1.Clarification of Definitions: Can you clarify Trajectory Scaling and Inference Step Scaling in the beginning of Ablation and Analysis? \n2.Could you redraw Figure 6 to make it clearer, or consider splitting it into multiple figures for better clarity? This would help readers better understand the presented information.\n3.More Details: Could you provide more detailed information on the experimental parameters, such as the optimal settings and the network architecture parameters? \n4.Comprehensive Metrics: The experimental metrics used (CLIP, PSNR, SSIM, etc.) seem limited. Could you consider incorporating additional evaluation metrics, such as those in 3DGen-Bench or conducting a user study for more comprehensive results?\n5.Comparison with Different Backbones: The paper mentions compatibility with various Transformer-based models, but lacks sufficient comparison across different backbones. Could you include experiments showing the impact of TRIM on various model architectures?\n6.Qualitative Comparisons: The paper mainly compares with DiffSplat. Could you provide more qualitative comparisons with other state-of-the-art methods to better demonstrate TRIM’s performance and benefits?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1.It proposes the TRIM framework, which effectively enhances the inference efficiency of 3D Gaussian diffusion models through temporal trajectory reduction and spatial instance mask denoising strategies.\n2.The TRIM framework is plug-and-play, compatible with various Transformer-based 3D diffusion models without requiring retraining, demonstrating strong adaptability.\n3.Extensive experiments on the DiffSplat backbone show that the model achieves approximately 45% reduction in computational resources (FLOPs) while improving generation quality, such as more detailed features in text-to-3D and image-to-3D tasks.\n4.Ablation experiments thoroughly analyze the impact of inference step scaling and trajectory scaling on TRIM, verifying that sampling diverse trajectories significantly boosts generation quality compared to simply increasing denoising steps.\n\nWeaknesses:\n1.The writing lacks clarity in some areas. For example, the definitions of Trajectory Scaling and Inference Step Scaling should be provided earlier in the paper. Additionally, Figure 6 is difficult to understand. There is also a lack of implementation details and specific parameters used.\n2.The experimental metrics are not sufficiently comprehensive. The paper uses CLIP similarity, CLIP R-Precision, ImageReward, PSNR, SSIM, and LPIPS to demonstrate the alignment between 3D objects and text/images, as well as human aesthetic preferences. However, these metrics are not exhaustive. A more comprehensive evaluation could be conducted by referring to the metrics used in 3DGen-Bench or by conducting a user study, which would provide a more thorough and nuanced assessment.\n3.The paper claims to be compatible with various Transformer-based 3D diffusion models without requiring retraining, but it lacks sufficient comparisons to show how the performance changes when TRIM is applied to different backbones.\n4.There is a lack of adequate qualitative comparisons; the paper only provides a direct comparison with DiffSplat, which is not enough for a thorough evaluation.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer's insightful and valuable comments. Below are our clarifications regarding your concerns. Due to the high similarity between your feedback in the weaknesses and questions, we merge some questions into the reply to the weaknesses and address them together.\n\n**Weaknesses**:\n\n> W1, Q1. The writing lacks clarity in some areas. \n> - The definitions of Trajectory Scaling and Inference Step Scaling should be provided earlier in the paper\n\nThank you for the insightful suggestion. We agree that introducing Trajectory Scaling and Inference Step Scaling earlier in the paper would improve clarity and strengthen the motivation for our method. Our approach is driven by two key observations:\n1）Increasing inference steps in 3D generation leads to diminishing gains, and even degradation when the number of steps becomes excessive. \n2）Among multiple sampled trajectories, certain ones yield significantly better results than others. \n\nThese findings demonstrate that the trajectory is a more effective axis to scale than the inference step, motivating us to propose Trajectory Scaling and the corresponding Trajectory Reduction methods. Therefore, moving the definition and analysis of these two kinds of scaling strategies to Sections 2 and 3, which are more closely associated with the core motivation, would help clarify the intuition behind our method and enhance the paper's clarity. We appreciate this helpful suggestion and will reorganize this motivation in the revised version.\n\n\n> W1, Q2. The writing lacks clarity in some areas. \n> - Figure 6 is difficult to understand.\n\nThank you for your careful examination of our experimental results. We acknowledge that Figure 6 may be unclear in its current form.\nFigure 6 compares two different scaling strategies, Trajectory Scaling and Inference Step Scaling, each varying along different axes. Specifically, all experiments start from a baseline of 10 denoising steps and 1 trajectory (the leftmost bars in Figure 6):\n\n- Trajectory Scaling: We fix the number of denoising steps at 10 and increase the number of trajectories to {2, 4, 8}.\n- Inference Step Scaling: We fix the number of trajectories at 1 and increase the number of denoising steps to {20, 40, 80}.\n\nAccording to the above scaling settings, the total inference budgets (steps) are #steps $\\times$ #trajectories, {10, 20, 40, 80}, shown as the x-axis of Figure 6.\nThis comparison reveals the two observations discussed in our response to [W1, Q1]: 1) increased inference steps do not necessarily lead to better performance, and 2) sampling more trajectories offers more potential for selecting better outputs. These two observations show that the trajectory is a more effective axis to scale rather than the inference step. In Figure 6, the trajectory scaling strategy gradually outperforms the inference step scaling strategy and has more potential for better performance as the inference budgets increase.\n\nWe acknowledge that the current legend and caption in Figure 6 are a bit confusing. In the revision, we will 1) clarify the axis labels and legends to match the original scaling strategies (TRIM $\\rightarrow$ Trajectory Scaling, DiffSplat $\\rightarrow$ Inference Step Scaling). 2) Rephrase the figure description in the paragraph for easier understanding.\nWe appreciate your feedback.\n\n\n\n>W2: The experimental metrics are not sufficiently comprehensive. The paper uses CLIP similarity, CLIP R-Precision, ImageReward, PSNR, SSIM, and LPIPS to demonstrate the alignment between 3D objects and text/images, as well as human aesthetic preferences. However, these metrics are not exhaustive. A more comprehensive evaluation could be conducted by referring to the metrics used in 3DGen-Bench or by conducting a user study, which would provide a more thorough and nuanced assessment.\n\n\nWe appreciate the reviewer's suggestion of using 3DGen-Bench and conducting a user study. Here, we use the CLIP-based 3DGen-Score model in 3DGen-Bench as an additional metric to enhance our evaluation. The 3DGen-Score model requires multi-view RGB images, normal maps, and a text prompt as input. The evaluation output contains five criteria: Geometry Plausibility, Geometry Details, Texture Quality, Geometry-Texture Coherence, and Prompt-Asset Alignment, each with different value scopes.\nWe provide results on the T$^3$-Single text-to-3D benchmark in the table below, which is evaluated by the 3DGen-Score metric. The 3DGen-Score results show that TRIM achieves better performance than the baseline DiffSplat on all five criteria.\n\n| Method    | Geo. Plausibility | Geo. Details | Tex. Quality | Geo.-Tex. | Alignment |\n| --------- | :---------------: | :----------: | :----------: | :-------: | :-------: |\n| DiffSplat |      6462.91      |     8.14     |    13.43     | 15790.92  |  8544.38  |\n| TRIM      |      6567.58      |     9.25     |    13.47     | 16144.38  |  8585.02  |\n\nFor a user study, we have included detailed visualization comparisons and utilized an aesthetic scorer to approximate human preference. As suggested, we plan to conduct a formal user study and include the results in future work.\n\n\n>W3, Q5: The paper claims to be compatible with various Transformer-based 3D diffusion models without requiring retraining, but it lacks sufficient comparisons to show how the performance changes when TRIM is applied to different backbones.\n\nWe thank the reviewer for pointing out the importance of evaluating different backbones. Our proposed trajectory reduction strategy is designed to be applicable to most diffusion-based architectures, while the instance mask strategy specifically leverages the token structure of Transformer-based backbones. In our main experiments, we choose Stable Diffusion 3.5, the latest diffusion model in the Stable Diffusion series, as the backbone. To further demonstrate generalization, we also apply TRIM to PixArt-Sigma with a different diffusion backbone, and present the results on the  T$^3$-Single text-to-3D benchmark in the table below. These results show that TRIM improves the performance of DiffSplat with the backbone of PixArt-Sigma, demonstrating that TRIM consistently improves both the generation quality and efficiency across various Transformer-based backbones.\n\n\n| Method               | CLIP Sim.% ↑ | ImageReward Score ↑ | FLOPs (T) ↓ | Runtime (second) ↓ |\n| -------------------- | :----------: | :----------------: | :---------: | :----------------: |\n| SD-3.5-Medium        |    30.95     |       -0.49        |   195.68    |        8.64        |\n| SD-3.5-Medium + TRIM |    31.58     |        0.12        |   106.31    |        5.24        |\n| PixArt-Sigma         |    30.73     |       -0.30        |    25.18    |        2.76        |\n| PixArt-Sigma + TRIM  |    31.24     |       -0.13        |    14.16    |        2.19        |\n\n\n>W4, Q6: There is a lack of adequate qualitative comparisons; the paper only provides a direct comparison with DiffSplat, which is not enough for a thorough evaluation.\n\nWe appreciate the reviewer's suggestion of providing more qualitative comparisons with other state-of-the-art methods. In the experiment section, we choose GVGEN, DIRECT-3D, LGM, InstantMesh, and DiffSplat as the baseline methods and show quantitative comparison results in Table 1 and Table 2. Since DiffSplat is the state-of-the-art method beyond the other three approaches, we present the qualitative comparisons with DiffSplat to highlight the improvement of TRIM. While the rebuttal does not allow us to insert the images, we will provide qualitative comparisons with more baselines, such as LGM and InstantMesh, in the updated submission.\n\n**Questions**:\n\n>Q3: More Details: Could you provide more detailed information on the experimental parameters, such as the optimal settings and the network architecture parameters? \n\n\nThank you for your inquiry for more implementation details. We present our main experimental parameters in Appendix A. \nThe ablation settings with custom parameter values are noted alongside each ablation experiment for clarity.\n\nFor the 3D generation inference, we follow DiffSplat's default settings to ensure a fair comparison and reproducibility. Our 3D generation pipeline is built on the Stable-Diffusion-3.5-Medium backbone and uses the Flow Matching Euler ODE solver with 28 denoising steps and 8 sampled trajectories in the main experiments. The classifier-free guidance scale is set to 7. Input images are center-cropped and resized to 256×256 resolution. \n\nFor the selector training, we adopt the Conv1-FC2 architecture for the selector, which achieves the best performance in the ablation study, shown as Table 3. We train the selector using the AdamW optimizer with a learning rate of 0.001, weight decay of 0.01, and a cosine decay scheduler. We set the training batch size to 64 and the epochs to 20.\n\nIn addition, we detail the pairwise trajectory data synthesis procedure used to generate samples for training the selector in Appendix A.\nWe appreciate your question and will provide more detailed implementation details in the revised version."}, {"review_id": "BLSgpELlBr", "reviewer": "Reviewer_sj2g", "summary": "This paper proposes a method to accelerate text-to-3D generation using 3D Gaussian diffusion by reducing inference time. Unlike existing state-of-the-art approaches that rely on lengthy denoising and post-processing, the authors introduce two key improvements. First, they generate synthetic data to train a selector model—comprising a CNN for feature extraction and an MLP for binary classification—that identifies the optimal latent trajectory early during inference. Second, they leverage the fact that image corners typically represent background, using these regions as reference to classify latent patches as foreground or background. This classification enables selective denoising, starting from outer regions and moving inward, thereby avoiding unnecessary computation. Their approach enhances both speed and quality in text-to-3D tasks using Gaussian diffusion models.", "questions": "To better evaluate the efficiency gains of the proposed method, please provide timing breakdowns for each added component—specifically the trajectory selection process and instance mask denoising steps.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe paper is clearly written and easy to follow. It addresses a practically important and timely problem in 3D generation, inference efficiency and introduces a training-free, model-agnostic solution. The proposed method is both conceptually simple and effective. The authors support their claims with comprehensive experiments and ablation studies, demonstrating consistent improvements across multiple benchmarks.\n\nWeaknesses:\n\nThe masking strategy relies on a corner-based attention heuristic, which assumes that objects are centered in the scene. This design limits generalizability to more diverse or complex layouts where foreground and background regions are not spatially separable.\n\nAlthough the selector is described as lightweight, the paper does not provide a quantitative analysis of its memory or computational overhead.\n\nThe method, as explained in the paper, is not as effective on image-to-3D to 3D scenarios.", "was_revised": true, "final_justification": "The authors have addressed my original concerns in their thorough rebuttal. However, I find the diversity issue raised by Reviewer CFPT to be very valid. The authors responded to this point and clarified that it, in fact, it does reduce diversity, and I agree that this point should also be explicitly included in the final revision.", "rebuttal": "We sincerely thank the reviewer for the thoughtful and constructive feedback. We are also pleased to see your recognition of the contributions of our work. We provide our clarifications in response to your concerns as follows. Due to the similarity between your provided Weakness 2 and Question 1, we address them together in the response to the weaknesses.\n\n**Weaknesses**:\n\n>W1: The masking strategy relies on a corner-based attention heuristic, which assumes that objects are centered in the scene. This design limits generalizability to more diverse or complex layouts where foreground and background regions are not spatially separable.\n\n\nThank you for pointing out the potential limitation of our masking strategy based on the corner-based attention heuristic in complex layouts where foreground and background regions are not spatially separable. \n\n\nWe would like to clarify that the center-object assumption is a widely adopted setting in the 3D object generation field. Specifically, 1) 3D datasets such as Objaverse and GSO (Google Scanned Objects) construct their coordinate systems with the target object centered, and both rendered and captured views are oriented towards the object center. 2) Many recent models, including LGM, InstantMesh, and DiffSplat, also train with object-centered renderings as input. Our approach follows these widely used settings for dataset preparation and model training to ensure comparability and relevance to standard benchmarks.\n\nWe clarify that our method can also be applied in scenarios where foreground and background regions are not spatially separable. Notably, the \"background\" identified by our method refers to _invalid transparent regions_, rather than _semantically meaningful backgrounds_. Specifically, our approach detects regions where the Gaussian Splatting primitive's opacity attribute is zero or close to zero, which are effectively marked as transparent and uninformative. These regions are regarded as redundant primitives and then removed during the Transformer-based denoising process.\n\n\nWe agree that generating complex layouts (e.g., multiple objects or objects with background context) is more challenging. To evaluate this, we have included evaluation results on such scenarios in Table 1 (T$^3$Bench's Single Object w/ Sur and Multiple Objects). Although performance drops slightly in these complex settings compared to single-object cases, our method still outperforms baselines, showing its effectiveness under complex layout settings. We appreciate your suggestion and will explore more robust strategies for complex layout generation in future work, especially for multi-object and background-aware scenes.\n\n>W2, Q1: Although the selector is described as lightweight, the paper does not provide a quantitative analysis of its memory or computational overhead.\n\nThank you for the suggestion. We provide the computational cost of the selector in Table 1 of the appendix, as shown in the table below. Leveraging our latent selector introduces around 0.29 GB of additional GPU memory usage and 0.1 seconds of latency during inference. Overall, the marginal cost of the temporal selector is outweighed by the substantial efficiency improvements introduced by spatial trimming, making the combined trimming strategy both effective and resource-efficient. \n\n\n| Model         | FLOPs (T) ↓ | Mem. (GB) ↓ | Throughput (step/s) ↑ | Runtime (second) ↓ |\n| ------------- | :---------: | :---------: | :-------------------: | :----------------: |\n| SD-3.5-Medium |   195.68    |    33.26    |         13.18         |        8.64        |\n| + IM          |   165.60    |    32.85    |         18.09         |        5.16        |\n| + TR          |   110.07    |    33.55    |         13.18         |        8.74        |\n| + TRIM        |   106.31    |    33.13    |         18.09         |        5.24        |\n\n>W3: The method, as explained in the paper, is not as effective on image-to-3D to 3D scenarios.\n\n\n\nThank you for the observation. Compared to text-to-3D generation, the image-to-3D task inherently exhibits lower output diversity, which reduces the gains brought by our temporal trimming. We attribute this to two main reasons: 1) Stronger supervision from input reference image: The reference image in image-to-3D provides a strong conditioning signal, making the sampled trajectories across different random seeds more consistent. This diminishes the benefit of pruning less useful trajectories since every trajectory may be similar. 2) Evaluation metric bias: The evaluation is based on accuracy compared to ground-truth views, which inherently penalizes plausible outputs that deviate from the ground-truth. Thus, the diversity encouraged by temporal trimming is under-reflected in the current reconstruction metric, since many generated details look plausible but are not considered correct under strict ground-truth comparisons.\n\nAs a potential solution, we will incorporate diversity-aware metrics or human preference studies, like CLIP and aesthetic scorer, in future benchmarks to better evaluate the image-to-3D generation task."}, {"review_id": "RSPsvKEAFE", "reviewer": "Reviewer_9KEq", "summary": "The authors propose TRIM (Trajectory Reduction and Instance Mask denoising), a training-free approach that incorporates both temporal and spatial trimming strategies. A lightweight selector model is introduced to evaluate latent Gaussian primitives derived from multiple sampled noises, enabling early trajectory reduction by selecting candidates with high-quality potential.", "questions": "1. how many trajectories do you have for TPIM? Does the Inference Step Scaling mean increasing denoising steps in inference?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "strength\n\n1. the authors demonstrate inference-time scaling in 3D diffusion models by trajectory pruning, showing that increasing the number of sampled trajectories during inference significantly improves the chance of producing high-quality results.\n2. the authors conduct comprehensive ablation studies on computation, inference scaling, and selector.\n\nweakness\n1. the latent selector seems to rely heavily on the data synthesis and metrics score. What's the off-the-shelf image metric evaluator? Does it lead to overfitting and having to re-train the selector when changing the dataset?\n2. The authors mention one of the significant improvements is the inference speed due to the Instance Masking (IM). Can the authors include the inference time comparison with other works?", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the time and effort you dedicated to reviewing our work and your valuable comments. We provide the following clarifications and responses to address your concerns in detail.\n\n**Weaknesses**:\n\n>W1： The latent selector seems to rely heavily on the data synthesis and metrics score. What's the off-the-shelf image metric evaluator? Does it lead to overfitting and having to re-train the selector when changing the dataset?\n\nThank you for the question. We provide the implementation details of selector training in Appendix A. Specifically, we construct a synthetic dataset using ChatGPT-generated prompts and 64 trajectories per prompt, and use CLIP score as the evaluation metric to supervise the selector. The well-trained selector is integrated into the TRIM pipeline to perform trajectory reduction (temporal trimming). It is noteworthy that this synthetic dataset is entirely independent of any downstream evaluation datasets, and no test sample is used during selector training, avoiding any test data leakage. \nIn our text-to-3D generation experiments, the selector is trained using ChatGPT-generated prompts describing the single object, and it generalizes effectively to single-object with surrounding and multi-object generation tasks, as shown in Table 1. This indicates that the selector does not require re-training when the evaluation dataset changes. \n\nRegarding concerns about overfitting to the CLIP score, we empirically observe that the trajectories selected by our model also gain improvements under another ImageReward metric. This suggests that the selector, although trained with a single metric, generalizes well and provides improvements among other metrics. \n\n\n\n>W2. The authors mention one of the significant improvements is the inference speed due to the Instance Masking (IM). Can the authors include the inference time comparison with other works?\n\nThank you for the suggestion. We have added inference time comparisons with two more baselines, LGM and InstantMesh, in the table below. Note that both LGM and InstantMesh adopt a different 3D generation pipeline that involves multi-view image diffusion followed by image-to-3D reconstruction. This pipeline uses a simpler U-Net architecture for image generation, resulting in faster runtimes than our Gaussian Splatting-based 3D diffusion model.\n\nSpecifically, LGM uses 30 inference steps, while InstantMesh uses 75 steps, leading to InstantMesh having the longest runtime. In contrast, our TRIM uses the default 28 denoising steps but still achieves a significantly faster runtime than DiffSplat and InstantMesh, while being only slightly slower than LGM. We also provide the major performance PSNR results in the table below, which shows that TRIM achieves the best performance among all baselines and LGM performs not well on PSNR even though it has the fastest runtime.\nWe will include the discussion of runtime differences between various 3D generation pipelines and baselines in the revised version.\n\n| Method      | Runtime (second) ↓ | PSNR ↑ |\n| ----------- | :----------------: | :---: |\n| LGM         |        4.93        | 14.90 |\n| InstantMesh |        9.78        | 15.53 |\n| DiffSplat   |        8.64        | 16.20 |\n| TRIM (Ours) |        5.24        | 16.78 |\n\n\n\n**Questions**:\n\n>Q1. How many trajectories do you have for TRIM? Does the Inference Step Scaling mean increasing denoising steps in inference?\n\nWe discuss the number of trajectories used in TRIM in Section 4.4. The ablation experiments with 2,4,8 trajectories is shown in Figure 7, and the performance gain increases with more sampled trajectories. Based on this, we adopt 8 trajectories in our main experiments in Table 1 and Table 2.\n\nRegarding inference step scaling, as analyzed in Section 3.2, denoising N trajectories over T steps leads to a total of N×T denoising steps. Our temporal trimming scheme reduces denoising steps to N×T−(N−1)×t, where t is the early-stop trimming timestep. Thus, we save (N−1)×t steps overall.\n\nIn implementation, we generate all N trajectories in parallel, so the total runtime is primarily determined by the longest (selected) trajectory, plus a small overhead from the pairwise tournament selection used in temporal trimming. As reported in Table 1 of Appendix A, this overhead is minimal and has little impact on overall runtime."}, {"review_id": "vw6p3Dcoqc", "reviewer": "Reviewer_CFPT", "summary": "This paper presents a training-free, model-agnostic framework to enhance the efficiency and quality of 3D diffusion models, through two key components: Trajectory Reduction and Instance Mask Denoising.\n\nTrajectory Reduction involves sampling multiple inference trajectories but fully denoising only those that are likely to yield high-quality results. To enable this, the authors train a latent selector on a synthesized dataset to predict latent quality. This selector approximates the relationship between intermediate latent representations (latent splats) and final rendered image scores.\n\nInstance Mask Denoising focuses on reducing computation by identifying background regions and progressively masking them out during the denoising process, thereby minimizing redundant token processing in the transformer.\n\nThe proposed method significantly improves the efficiency of text-to-3D and image-to-3D generation while maintaining or enhancing output quality.", "questions": "* Q1: Some baseline results (e.g., DiffSplat) differ notably from those in the original papers, without explanation. Could the authors clarify how these baselines were evaluated or re-implemented?\n\n* Q2: The description of the latent selector’s training is confusing—why is the initially described approach not used, and how is timestep \n$t$ sampled during training?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n* **S1:** The motivation to discard low-quality samples early in the diffusion process is well-founded and aligns effectively with the goal of improving computational efficiency.  \n* **S2:** The Spatial Trimming Scheme offers a practical way to reduce redundant computation by concentrating on foreground regions, which is a sensible strategy.  \n* **S3:** The method demonstrates consistent improvements in both efficiency and generation quality across several benchmarks, supporting its practical value.\n\n## Weaknesses and Suggestions for Improvement\n* **W1:** The paper frequently describes the method as training-free (e.g., lines 5, 31, 51), but this characterization is somewhat misleading since a latent selector is trained to guide trajectory pruning. It would strengthen the paper to acknowledge this training component more transparently and clarify its role.  \n* **W2:** The explanation of the latent selector’s training process is occasionally unclear. For instance, lines 115–120 describe a training approach that the authors later state is not used, without sufficient explanation for its inclusion. Providing clarity here would help reduce ambiguity for readers.  \n* **W3:** Equation (1) is introduced but does not appear to be used in the final method, which makes its inclusion somewhat confusing. Meanwhile, Equation (2) is employed for training the latent selector, but key details—such as how the timestep \\( t \\) is sampled—are missing. Clarifying whether the selector is trained across all timesteps or at a fixed one would improve both the interpretability and reproducibility of the method.  \n* **W4:** Pruning all but one trajectory during inference may limit output diversity, as it commits early to a single generation path. Addressing how this approach impacts diversity, particularly in multi-modal generation tasks, would be valuable.  \n* **W5:** Some baseline results reported differ noticeably from those in the original papers, without any explanation or discussion. For example, in Table 2, the DiffSplat baseline shows PSNR, SSIM, and LPIPS scores of 16.20, 0.79, and 0.19, respectively, while the original DiffSplat paper reports 22.91, 0.892, and 0.107 for these metrics. Providing clarification on how baseline results were obtained and discussing these discrepancies would enhance the credibility of the comparisons.  \n* **W6:** The ablation study focuses primarily on variations in the latent selector architecture. It would be beneficial to also analyze the impact of individual trimming components—such as the Temporal Trimming Scheme, Progressive Mask Expansion Scheduler, Token Merging and Padding, and Post-denoising Correction—to better understand their contributions to overall performance.", "was_revised": true, "final_justification": "The rebuttal has address most of my concerns except for the diversity problem. \nIf the authors include the discussion of this problem to the revision version of the paper, I think it is okay to have a score of 4 (borderline accept)", "rebuttal": "We sincerely appreciate the reviewer's thoughtful and detailed feedback. Your constructive suggestions are invaluable to improving our work, and we are encouraged by your recognition of our contributions. Below, we provide detailed responses to your concerns. As some of the weaknesses and questions are closely related, we merge their responses and address them together for coherence.\n\n\n**Weaknesses**:\n\n>W1: The paper frequently describes the method as training-free, but this characterization is somewhat misleading since a latent selector is trained to guide trajectory pruning. \n\nOur claim of \"training-free\" refers specifically to that we do not require any re-training or fine-tuning on diffusion models, meaning that weights of the off-the-shelf DiffSplat model remain completely unchanged in our framework. We acknowledge that our method introduces a lightweight latent selector, which is trained to adapt to the DiffSplat model and guide trajectory pruning.\nThe selector is the only trainable component in our framework, and training it on our synthetic dataset takes less than 10 minutes, incurring only minimal training computational cost, as shown in training details in Appendix A. We will revise the paper to clarify this statement more rigorously in the next version.\n\n\n> W2, W3.1, Q2: The explanation of the latent selector's training process is occasionally unclear. For instance, lines 115–120 and Equation (1) describe a training approach that the authors later state is not used, without sufficient explanation for its inclusion. \n\n\nThanks for your attention to our latent selector training process. \nWe introduce the distillation objective in Equation (1) to set up the training goal, which is to align the selector's output with the Decoder-Renderer-Evaluator's score, which is the ground truth of the synthetic dataset. \nHowever, in the experiments of directly distiling the Decoder-Renderer-Evaluator joint model into the selector, we notice that the selector training is not stable and hard to converge. We attribute this training challenge to two reasons: 1) The variance of score distribution is very small, which makes it difficult for the selector to learn the slight score difference among multiple trajectories. 2) The lightweight selector is too weak to distill the complex knowledge in the Decoder-Renderer-Evaluator. \n\nConsidering the above challenges on the direct distillation, we further propose a pairwise comparison training strategy to perform an indirect distillation, as shown in Line 143-158. The selector is changed to predict the relative performance among trajectories, which is more robust and stable to train. Compared to the direct distillation, the pairwise comparison training strategy is under the same offline distillation setting with the same training data and a similar training objective.\n\nWe acknowledge that the explanation of the latent selector's training process is not clear in the submission. We appreciate your suggestion and will improve the method description to make the transition from direct model distillation to indirect pairwise comparison distillation smoother in the revised version.\n\n\n\n> W3.2, Q2: Equation (2) is employed for training the latent selector, but key details—such as how the timestep ( t ) is sampled—are missing. Clarifying whether the selector is trained across all timesteps or at a fixed one would improve both the interpretability and reproducibility of the method.\n\n\nWe thank the reviewer for pointing out that Equation (2) does not mention the timestep. We clarify that Equation (2) reflects the final training objective used in our experiments, and the corresponding timestep setting is earlier described in Lines 144–145. Specifically, we use  $\\mathbf{f}= \\mathrm{CNN}(\\mathbf{z}_t)$, the CNN embeddings of the intermediate latent features at timestep $t$,  as inputs to the MLP, and train the selector with the loss in Equation (2).\n\nWe train the selector at a fixed timestep, and our ablation study in Figure 8 investigates the impact of different $t$ values. The performance gain plateaus after $t = 50\\% T$, so we adopt this as the default timestep in our main experiments.\n\nWe appreciate the reviewer's suggestion of training the selector across all timesteps. We agree that incorporating timestep as an additional input for the selector could turn to a more elegant and robust architecture without manually tuning the hyperparameter $t$. The timestep-aware selector might enable more flexible temporal trimming during inference. We plan to explore this advanced selector model in the future work.\n\n\n> W4: Pruning all but one trajectory during inference may limit output diversity, as it commits early to a single generation path. Addressing how this approach impacts diversity, particularly in multi-modal generation tasks, would be valuable.\n\nThank you for your comment regarding output diversity. It is generally accepted that the diversity of diffusion models is primarily driven by the initial noise, i.e., different random seeds result in diverse generations. However, the quality of these diverse outputs can vary significantly.\n\nOur method is motivated by the observation that among multiple sampled trajectories, some yield substantially better results than others. As shown in the left subfigure of Figure 3, instead of choosing a random path, we employ a pairwise tournament selection strategy to identify and retain the trajectory with the most promising potential. This does not reduce the inherent diversity from random noise, but selects a higher-quality sample from the diverse pool. Thus, our approach balances maintaining diversity and improving output quality.\n\nMoreover, we can invert the usage of the selector to identify the \"loser\" trajectories, those predicted to have lower performance potential, offering a way to analyze the failure cases and lower-bound quality of the model's outputs. \nThis dual capability of the well-trained selector enables more effective investigation of output diversity from both best-case and worst-case perspectives. We plan to explore this direction in future work.\n\n\n> W5, Q1: Some baseline results in Table 2 reported differ noticeably from those in the original papers, without any explanation or discussion. \n\nThank you for pointing out the discrepancies. There are two main reasons for the differences between our reported baseline results and those in the original papers: 1）Data selection inconsistency: DiffSplat reports results on a randomly selected subset of 300 images from the GSO dataset, but does not disclose the its selection list. In our experiments, we randomly sample 300 GSO objects, matching the number used in DiffSplat, but it is not an identical subset to DiffSplat's. 2） Viewpoint rendering mismatch: Both DiffSplat and InstantMesh do not provide multi-view rendering camera parameters of the GSO evaluation dataset. Therefore, we follow the official GSO dataset's rendering protocol as an alternative, using four orthogonal views (front, back, left, right) rendered at a fixed elevation angle of 0°. This setting offers a standard and reproducible test set that is beneficial for future research. However, these four orthogonal views have lower overlap compared to six- or eight-view settings used in other works, making the reconstruction task more challenging. This explains why our reproduced baseline results are mismatched and lower than those reported in the original papers.\n\nWe have included detailed settings about the GSO benchmark in Appendix A to ensure reproducibility. The results in Table 2 are conducted under these consistent settings for a fair comparison. We will provide additional explanation to discuss the differences in the GSO dataset settings in the revised version.\n\n> W6: The ablation study focuses primarily on variations in the latent selector architecture. It would be beneficial to also analyze the impact of individual trimming components—such as the Temporal Trimming Scheme, Progressive Mask Expansion Scheduler, Token Merging and Padding, and Post-denoising Correction—to better understand their contributions to overall performance.\n\n\nThank you for the valuable suggestion to clarify the contribution of individual components. We provide an ablation study analyzing the contributions of Trajectory Reduction and Instance Masking in Figure 1 and Figure 2 of Appendix B. For the internal components of Instance Masking, we offer detailed discussion on their design and function in Section 3.3. Specifically, Instance Mask Detection and Token Merging & Padding are core components for spatial trimming and are indispensable to the overall pipeline. And we conducted additional ablations on the remaining two components: Progressive Mask Expansion Scheduler and Post-denoising Correction. The results in the table below show that removing these two components hurts the model output quality, demonstrating that both components contribute positively to the model's performance.\n\nSpecifically, Progressive Mask Expansion Scheduler introduces a smooth and gradual transition from unmasked to masked regions, facilitating more stable and effective denoising. Post-denoising Correction removes Gaussian primitives located in transparent background regions, which are typically associated with [BG] tokens that are not optimally denoised, thereby reducing visual artifacts in the final rendered images.\nWe will add visual comparisons of rendered outputs with and without Post-denoising Correction in the revised version to better illustrate its effect.\n\n\n| Method                                   | CLIP Sim.（%） | CLIP R-Precision（%） | ImageReward |\n| - | :-: | :-: | :-: |\n| TRIM                                     | 31.58          | 81.42                 | 0.12        |\n| w/o Progressive Mask Expansion Scheduler | 31.24          | 81.12                 | 0.03        |\n| w/o Post-denoising Correction            | 30.98          | 79.80                 | -0.28       |"}]

Author rebuttal:

Meta-review: The submission studies 3D Gaussian diffusion models. Specifically, the submission argues that the large number of Gaussians leads to a slow denoising, i.e., slow generation. To address, the authors incorporate temporal and spatial trimming strategies. Initially, the reviewers raised concerns among others about clarity (2U8q, CFPT), missing comparison/incomprehensive evaluation (2U8q, sj2g, 9KEq, CFPT), unjustified/misleading claims (2U8q, CFPT), generalizability (sj2g), effectiveness (sj2g), differences to numbers reported in prior work (CFPT). The rebuttal was able to answer many of these concerns. A remaining concerns was the reduction in diversity due to pruning. The author discussion showed that the proposed approach indeed reduces diversity. Two reviewers strongly encouraged the authors to include this discussion in the revised submission.
Decision: Accept (poster)