Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching
Authors: Weimin Bai
Yubo Li
Wenzheng Chen
Weijian Luo
He Sun
Venue: Accepted by TMLR ()

Abstract: Distilling pre-trained 2D diffusion models into 3D assets has driven remarkable advances in text-to-3D synthesis. However, existing methods typically rely on Score Distillation Sampling (SDS) loss, which involves asymmetric KL divergence—a formulation that inherently favors mode-seeking behavior and limits generation diversity. In this paper, we introduce Dive3D, a novel text-to-3D generation framework that replaces KL-based objectives with Score Implicit Matching (SIM) loss, a score-based objective that effectively mitigates mode collapse. Furthermore, Dive3D integrates both diffusion distillation and reward-guided optimization under a unified divergence perspective. Such reformulation, together with SIM loss, yields significantly more diverse 3D outputs while improving text alignment, human preference, and overall visual fidelity. We validate Dive3D across various 2D-to-3D prompts and find that it consistently outperforms prior methods in qualitative assessments, including diversity, photorealism, and aesthetic appeal. We further evaluate its performance on the GPTEval3D benchmark, comparing against nine state-of-the-art baselines. Dive3D also achieves strong results on quantitative metrics, including text–asset alignment, 3D plausibility, text–geometry consistency, texture quality, and geometric detail.

Published in Transactions on Machine Learning Research (12/2025)
Dive3D: Diverse Distillation-based Text-to-3D via Score Implicit Matching
Generation
Weimin Bai
weiminbbaf@stt..pu...uuu
Academy for Advonced Interdisciplinary Studies, Peking University National Biomedical Imaging Center, Peking University
Yubo Li
gbli25@stu.pku.cdu edu.crm
School Software and Microclectronics, Peking University National Biomedical Imaging Center, Peking University
Wenzheng Chen
wencherngchenn@yku.cuu.co
Wongruan Institute of Computer Technology, Peking University
Weijian Luo hi-lab, Xiaohongshu Inc
pkuluj1994 994 @icloud.ccooo com
He Sun"
hesun DessunOpku.oo edu.cn
Future of Technology, Peking University.
National Biomedical Imaging Center, Peking University
Reviewed on OpenReview: https: // openreveew. net/ formm? id- OUYMueHLMf
Abstract
Distilling pre-trained 2D diffusion models into 3D assets has driven remarkable advances in text-to-3D synthesis. However, existing methods typically rely Score Distillation Sam- pling (SDS) loss, which involves asymmetric KL divergence-a formulation that inherently favors mode-seeking behavior and limits generation diversity. In this paper, we introduce Dive3D, a novel text-to-3D generation framework that replaces KL-based objectives with Score Implicit Matching (SIM) loss, score-based objective that effectivoly mitigatos mode collapse. Furthermore, Dive3D integrates both diffusson distillation and reward-guided op- timization under a unified divergence perspective. Such reformulation, together with STM loss, yields significantly more diverse 3D outputs while improving text alignment, human preference, and overall visual fidelity. We validate Dive3D across various 2D-to-3D prompts and find that it consistently outperforms prior methods in qualitative assessments, includ- ing diversity, photorealism, and acsthetic appeal. We further evaluate its performance on the GPTEval3D benchmark, comparing against nine state-of-the-art baselines. Dive3D also achieves strong results on quantitative metrics, including text asset alignment, 3D plansi- bility, text goommeryy consistency, texture quality, and geometric detail.
1 Introduction
Text-to-3D generation the task of creating 3D contents from natural language descriptions has attracted enormous interest Poolle et al. (2022): : Shi et al. (2023) Lin et al. 2023) due to its broad applications in vision and graphics. Recent advances, such as 3D representations MMildehhall et al. 2021 Kerbl et al. 2023
large-scale pre-trained vision-language models RRdford et. al.) 2021), advanced texl-to-image diffusion and flow models MoodelsRoombach et al. 2022 and differentiable rendering techniques, have further accelerated progress
in this field. In particular, powerful text-to-image diffusion models such as Stable Diffusion scrics pericsRoobaach
Corresponding author.

---

Published in Transactions on Machine Learning Research (12/2025)
ProfificDreamer (4 samples)
Durs (4 samples)
(a) Dive3D generates more (e-g., background, lighting, geometry) models using a score-based divergence. Prompt: pirate ship sky.
baby bunny sitting top of stack of pancokes.
A model of house Tuslor style.
full and frogrant bundle of fresh, calorful blooms.
A Chimponzee dressed like Henny VIII king of England.
(b) Dive3D unilies SDS and reword guidance, producing higher-quality and human-preferred 3D models.
Figure We propose Dive3D, a novel text-to-3D generation framework that enables both diverse and high-fidelity 3D content creation. Top: Compared to ProlificDreamer, which exhibits mode collapse under the same prompt, Dive3D generates diverse set of realistic and semantically aligned 3D outputs. Bottom: Dive3D unifies SDS and human-preference rewards within divergence-based framework, effectively com- bining diffusion priors and reward signals to boost both visual quality and prompt alignment. All results are represented as textured mesh assets.
et al. 2022 Ramesh al. 2022 Saharia al. 2022). lay a strong foundation for text-driven 3D synthesis:
by leveraging pre-trained 2D diffusion priors and multi-view rendering, one can optimize 3D asset so that its renderings align with given text prompt. This capability opens new avenues for 3D content creation, enabling even non-experts to "describe and create" novel 3D assets in freestyles.
Several paradigms have emerged to tackle text-to-3D generation. Diffusion distillation-based meth- ods cxempiillliiied by Score Distillation Sampling (SDS) in DreamFusion Poolc et al. (2022) opptimize 3D representations by aligning multi-view renderings with pre-trained lexi-to-image dillusion priors Rombach et al. (2022). Reward-guided approaches Mohammad Khalid al. (2022 Jain et al. (2022) further refine these approaches by directly incorporating human-preference CLiP-based rewards, boosting both seman- tic alignment and perceived quality. Despite their impressive fidelity and text alignment, both diffusion- distillation and reward-guided methods suffer from a critical limitation: limiled generalive diversily. Even when prompted with intentionally vague or open-ended descriptions, current models tend to converge on a narrow set of similar outputs.
We analyze this limitation and trace its root to the utilization of Kullback-Leibhe (KT.) divergence. Specif- ically, the objectives optimized by both SDS and reward-based methods can be reformulated as minimizing an asymmetric KL divergence, which shares a fundamental limitation: KL divergence inherently encourages mode-seeking behavior by penalizing samples that deviate from high-density regions of the target distribu- tion. As result, the generative model tends to collapse to few dominant modes, severely suppressing output diversity.
In this paper, we present Dive3D, a novel framework that replaces KL-based objectives with Score Implicit Matching (SIM)-a score-based divergence loss that directly matches the gradient fields of the probability density of generated contents and the diffusion prior. This formulation avoids the mode-sceking tendencies of KI. and encourages exploration of multiple high-probability regions, thereby promoting diversity without
2

---

Published in Transactions on Machine Learning Research (12/2025)
sacrificing fidelity or alignment. Furthermore, Dive3D unifies both diffusion distillation and reward-guided optimization under a divergence-based perspective. Combined with SIM loss, this formulation enables a principled integration of diffusion priors, human preferences, and diversity-promoting objectives within a single framework. As a result, Dive3D generates 3D assets that are not only realistic and well-aligned with text prompts, but also significantly more diverse.
Through extensive experiments on standard text-to-3D benchmarks, we demonstrate that Dive3I) achieves state-of-the-ar performance, substantially outperforming existing SDS- and reward-based approaches across visual fidelity, prompt adherence, and generative diversity.
2 Related Works
Diffusion Distillation Based Methods Diffusion distillation methods (Luo 2023) leverage pre-trained text-to-image diffusion models (Rombach et al.] 2022 Saharia et al. 2022 Balaji et al. 2022) to guide the optimization of 3D representations by aligning rendered views with diffusion priors. This line of work was pionccred by Score Distillation Sampling (SDS) in DreamFusion (Poole et al.) 2022). which initiated an cra of 3D synthesis by transferring the knowledge embedded in 2D diffuusion priors. However, these diffusionn driven optimization techniques typically rely on minimizing KL divergences (Poole et al. 2022 Wang et al, 2024 Luo ct al.] 2024a), often resulting in modo-sccking behavior where generated 3D objects collapse into a single plausible solution with limited diversily. Moreover, the straightforward use of 2D diffusion priors can introduce visual artifacts such as over-saturated colors, overly smooth geometry, and even Jamus
artifacts (Jain et al. 2022 Chen et al. 2023 Wang et al. 2024). To address these challenges, recent studies
have explored various improvements, including timestep annealing (Huang et al.] 2024a] Wang et al. 2024
Zhu et al., 20244, coarse-to-fine training (Lin et al., 2023 Wang et al.
2024
Chen et al.
2023
component
analysis
(Kazzir et al.
2024). and formulation refinements (Zhu et al.
2024
Wang et a.l.
2024
Liang et al.
2023
Tang et al. 2023a
Wang et al. [023b] Yu et al.) 2024; Armandpour et al. 2023
Wu et al.,
2024b
Yan
et al.
2024).
Additional efforts have focused on geometry- disentanglement (Chen et al.
2023
Ma
ct al.
2023a
Wang et al.]
2024) and mitigating the multi-face (Janus) problem by replacing text- to fext-to-image image
diffusion with novel view senthesis or multi-view diffusion (Liu et al., 2023 Long et al. 2023 Liu et al.
2024
Weng et al.]
2023:
Ye et al.)
2023 Wang & Shi] 20233 Shi et. al., 2023). Notably, diffusion distilaation
has
also seen rapid progress in other domains, such as one-step diffusion models (Luo al.) 2023a
2024b
Yin
ct al.
2024
Zhou ct al.]
2024b Geng ct al.] 2024 Huang et al. 2024b) and various related approaches
Nic
et al.
2022
Zhang et al.
2023a Luo el a.. 2023b
Reward Optimization based Methods. Another calegory of approaches optimizes 3D outpuls directly human- using reward models, such as visual-language alignment losses human- preference reward models instead
of (or in addition to) diffiusion prior. Early methods like CLIP-Mesh (Mohammad Khalid et al. 2022 and DreamFields (Jain et al. 2022) directly maximize the CLIP score (Radford et al. 2021) between rendered images and the text prompt, enabling zero-shot text-to-3D without 3D datasets. While conceptually simple, these CLIP-guided approaches often yielded suboptimal geometry or texture (e.g. unrealistic shapes) and required expensive optimization. More recently, DreamReward (Ye et al., 20244 uses a learned internal 3D preference-reward model (Reward3D) trained on internally collected human feedback data guide generation. DreamReward improves alignment of generated shapes with user intent, achieving better text relevance as judged by the reward function. Reward-based methods explicitly push for semantic or aesthetic alignment, but relying solely on them can compromise visual fidelity if the reward is not perfectly aligned with 3D realism (e.g. CLIP might encourage implausible textures). They may also require costly human data collection to train the internal 3D reward model.
Feed-forward Methods. Feed-forward methods train neural networks to directly generate 3D content from text using large synthetic 3D datasets or cross-modal supervision. For example, CLIP-Forge (Sanghi et al. 2022) and CLIP-Sculptor (Sanghi et al. 2023) leverage CLIP embeddings for zero-shot text-to-shape gencration. More recently, advances large reconstruction models (LRMs) (Hong et al. 2023) have enabled rapid 3D model prediction from single or sparse-viet view images, inspiring these developments of methods like Instant3D [i et al. 2023) and Turbo3D (Hu et al. 2024 that first. generate multi-view images from text and
3

---

Published in Transactions on Machine Learning Research (12/2025)
then feod-forward 3D reconstructor (trained on synthetic data) to instantly produce representations such as NeRF or 3D Gaussian Splatting. However, the quality of these approaches depends heavily on the underlying text-to-multi-vie generator, often recasting the challenge as one of diffusion distillation or reward-based optimization.
3 Preliminary
In this section, we review the key concepts and mathematical formulations underlying our work. We first describe text-to-image diffusion models, then explain how these models are adapted for text-to-3D generation via diffusion distillation, and finally review reward-guided text-to-3D methods.
3.1 Text-to-lmage Diffusion Models
Diffusion models Sohl-Dickstein et al. (2015): Ho al. (2020); Song al. (2020b) are class of generative models that iteratively transform noise into data using stochastic process. Lel. atn qdata (ar) denote a data
sample. The forward diffusion process corrupts 2o by gradually adding noise described by the stochastic differential equation (SDE):
dæ; F(x,,t) t) dt + G(t) dwr. E [0,
(1)
where F is a drift function, G(t) is a scalar-valued diffusion coefficient, and w, denotes standard Wiener process. To generate samples, the reverse diffusion process is used to progressively denoise initial
noise sample (Song et al. 2020b a Zhang & Chen 2022 Liu et al. 2022 Lu et al. 2022 Xue al. 2023
The marginal core function V,, (xx)) is typically approximated by contimuuus- indexed neural network so (a t). This score network is trained using the weighted denoising score matching objective:
((Ø) Ex, A(t) ( + toe,tt ) ae
(2)
where e ~~ (O, I), and the functions a and are determined by the noise schedule.
By conditioning on text inputs, these diffusion models can be extended to text-to-image synthesis. In this setting, a conditional score network s5(2,, y, t) Va, dogp:(as y) used, where is the text prompt describ- ing the image content. Popular models such as Stable Diffusion (Rombach al. 2022] and MVDiffusion (hi
et al. 2023 have demonstrated that this approach yields high-quality, semantically aligned images.
3.2 Text-to-3D Generation by Diffusion Distillation
A prevalent paradigm for text-to-3D synthesis leverages pretrained text-to-image diffusion models to guide the optimization of a 3D representation. Let g(0, c) be differentiable renderer that maps the 3D parameters i to 2D image under camera pose qo (z)(ec) be the distribution of rendered images diffusion time t, and m(x.)y") be the target conditional distribution given view-dependent text prompt ye defined by a pretrained diffusion model. The loss that aligns each rendered view of the 3D model with the conditional diffusion prior can be formulated as:
LCDP(0) Er, (u(t) DKL ( (xx(c) p(
(3)
where wt) a weighting function. In practice, the gradient of loss (2024a) and Wang et al. (2023a) for comprehensive derivation):
3 writes (please refer to Luo et al.
VeLeDP(O) ~ R,,,. (n(() (co( c) 0g(0,c)
where (0 fft -0, - f,t)) is the noise prediction of the diffusion model.
The Score Distillation Sampling (SDS) loss, introduced in DreamFusion Poole (002)), improves gen-
eration quality by employing classifier-free guidance (CFG) (oo & Salimans 2022 Ahn et al. 2024 Karras
4

---

Published in Transactions on Machine Learning Research (12/2025)
Prompt y carton-style house with vibrant colors and unique design.
Noise
Text-to-lmage
lcop Diom Djor|(00(((0))))x(xxx)))))
x x-p(xlze.c)
Diffusion
Noise
Image Diffusion
luop P
Pze
Reward
Human
LER = (
Preference
d[a+
3D Objects Pzg
Rendered images
Figure 2: Overview of Dive3D. Dive3D reformulates both SDS loss and human-preference rewards within unified divergence-based framework, revealing that both rely on KI. divergence, D an asymmetric objective that inherently favors mode-seeking and leads to model collapse and limited diversity. Dive3D replaces this with a Score Implicit Matching-bass based divergence, Dio.r] (::-), which aligns score fields rather than probability densities, effectively mitigating mode collapse and enabling more diverse and faithful 3D generation.
ct al. 2024
Li 2024). which replaces the original conditional score Eq with weighted difference between the conditional and unconditional score estimates,
Cs(2,, sf 1) = + c f,,) - co (o(())t))
= cs (a,, / .) + ? Esl (x 3. t) c,(
(5)
= - ot S00 (3 y, + 8 (2,1 v. s so 1))]
VoLSDS()) x R, w(() (Colare, ,0) 0g(0.c)
(6)
00
This adjustment is equivalent to incorporating an additional regularization term into the Score Distillation Sampling (SDS) loss the so-called CFG-reward as introduced by (Luo al. 2024c) (CsDS LCDP + <CFR)) effectively acting an implicit likelihood term that better aligns the generated image with the text prompt and enforces pose constraints. Increasing the weighting factor strengthens this alignment, thereby improving the semantic calibration of the 3D renderings.
3.3 Text-to-3D Generation via Reward Guidance
An alternative approach leverages reward signals to steer the generation of 3D content. Pioneering works such as DreamFiele Jain el al. (2022), CLIP-Mesh Mohammad Khalid et al. (2022), and X-Mesh Ma el al. (203bb) leverage CLIP scores Radford et al. [2021 to align 3D representations with text prompts. In these mothods, reward function is defined as:
f((().)) Myy")
(7)
where f(-) and ((-)) are embedding functions for images and text, respectively, and g(0, c) is the rendered image. Maximizing this reward encourages the 3D model to generate outputs that are semantically aligned with the text.
Recent methods, such as Ye al. (2024), combine the SDS loss with reward-based signals to further enhance semantic alignment and cumman-preferenc consistency. For example, DreamReward modifies the SDS loss as:
LRwward (0) LsDs(0) - A AE,,, ( ()(())))
(8)
where o; qo (c) in 1 x orCo(ar; y, t)] is an estimate of the denoised image, and balances the influence of the reward. Similar Eq. 5 the reward function acts an additional regularization term in SDS-based 3D generation.
a
5

---

Published in Transactions on Machine Learning Research (12/2025)
4 Method
In this section, we introduce Dive3D, a principled framework that boosts both diversity and fidelity in text-to-3D synthesis by replacing KL-divergence guidance with score-based divergence optimization (see Fig In 4.1 we demonstrate that existing SDS and reward losses are both linear combinations of KL divergences and thus prone to mode collapse and mode-seeking, Then, Sec. 4.2 we present our core-basce divergence formulation, which overcomes these limitations and delivers significantly more varied and higher-quality 3D outputs.
4.1
SDS and Reward Guidance are Both KL Divergences
The SDS Loss.
The classifier free guidance in the SDS loss (Eqs. 6 can rewritten as
(a, y, t) - So (2t, t) ~ Va, log logp(>;ll) Va, log p(x:).
(9)
Substituting Eq. 9 into Eq. and integrating, the SDS loss can be expressed as the difference between two KL divergence terms:
(SDs(0) = (1 7) (C) - (0)
= (1 (1+1)) 7) Be,e wt)) DKL (go(xelc) || p(aalv))
(10)
-7 Br,e w(l) DKL (o(2))))) plo.i))
This formulation makes explicit that the SDS loss balances two KL divergences one that promotes prompt fidelity (LCDP) and that modulates diversity via the unconditional prior (CuDp). Increasing ? strengthens text wext-image alignment but narrows diversity by shrinking the effective entropy.
The Explicit Reward Loss.
Assuming the reward defines exponential distribution,
PER xx) x exp (r(1f fo(xx)))
(11)
the explicit reward loss in Eq. S can likewise be interpreted as a KL divergence.
LER (0) Ex,c (()) DKL. (g0 (z,())) ( (f))..)))) =
Ex,,, (w((1) (oggo((;;lc) log (v m.))
(12)
constant Er,,, [w(() $o())))))))))))))))))))))))))))
where the first term a constant because the distribution (o((cc) typically uniformly-distributtedd collection of N particles (i.e., (o(x)cc) 1/N). Serving a measure of the joint distribution of prompts and
images, the explicit reward loss not only enhances text alignment during 3D generation but also provides
the flexibility to incorporate additional criteria, such as human preference Xu et al. (202)); Murray et al.
2012), photorcalism Kirstain et al. (002)), and geometric consistency Ye ct al. (2024).
Unified KL Divergence Framework. Collecting these components, we can unify all loss terms in the diffusion- or reward-bar based text-to-3D genoration framework by defining three core KL-bbaedd terms:
(CD = Er, DK) (go(2= ||
LUDP(0) = E,,c (KL (90(a)l0) ||
(13)
CER(O) = Er,e (I) (0 | xx)

---

Published in Transactions on Machine Learning Research (12/2025)
Both SDS and reward-guided objectives simply linear combinations of these divergences:
LSDS (1 CCDP YLUDP,
(14)
Reward LSDS + ALER-
This unified view permits flexible tuning of the weights on each term (see Appendix), yielding higher-fidelity generations. However, both theory and experiments Luo et al. (2025]: Zhou et al. (2024b a show that relying
on the inherently asymmetric KL divergence (qlp( + DKL (pl)) destabilizes training and induces mode-
seeking, thereby constraining the diversity of generated 3D assets.
DreamFusion Poole
2022
Fantasia3D Chen
2023
ProlificDreamer Wang al. 2024
Dive3D (Ours)
A chimpanzee dreszed like Henry VIII King England.
A baby bunny sitting or top of stack of pancakes.
Figure 3: Comparison with Basclines based Stable Diffusion Rombach et al. (2022). Dive3D exhibits higher quality, richer texture details, and superior aligmment with human preferences, such accurate clothing styles, and vivid fur texture.
MVDream Shi et 2023
DreamReward et. al. 2024
Dive3D (Ours)
guitar resting against
Various hollow,, asymmetrical, textured snasholls, collected in sand-hited, clear glass with swine-tied neck, displayed a windowslll
sequeece street lamps, casting pools fight on cobbiestone paths as twilight descends.
Figure 4: Comparison with Basclines based on MVDiffusion Shi et al. (2023) and reward model Ye et al. (2024). Dive3D exhibits more detailed and realistic 3D generation, capturing fine- grained structures such as accurate guitar geometry and transparent glass materials.
4.2 From KL to Score-based Divergence
To mitigate these issues, in Dive3D we propose to replace the KL divergence with a score-based divergence, named score implict matching (SIM) loss(Lun et al. 2024b), which has shown significant improvements generation diversity in one-step diffusion and flow models et al. 2024b Zhou et al. 2024b Huang et al. 2024b). Specifically,, the scoro-basod divergence definod between two distributions p and q as
DIo 9) = 1- w(()) BRewte (a(sp(az)) - sy(ax)) dt.
(15)

---

Published in Transactions on Machine Learning Research (12/2025)
TIE
Score Divergence (ours) KL Divergence
The blacksmith NPC the game.
A realistic Japanese building.
Figure 5: based divergence vs. KL divergence in 2D space sampling. The proposed score- based divergence significantly enhances the diversity of generated 2D samples, yielding more varied back- grounds and clothing in "game character" generation, as well as a broader range of environments, lighting conditions, and architectural features "Japanese building" generation.
where the score functions of these two distributions are given by sy(2))) Va, log logp( and (x())) V.. log (x;,, d: Rd + IR is distance function, मं is a sampling distribution whose support exceeds that of Pr and gt, and wt) is weighting function. If we set p(-) = (t,ell))) PER (v² æ.) and q(-) = (x( can be updated to
then the KL-based losses in Eqs. 13 14
LScore (DP (0) w(t) Exiws (d ((ppxxl1l))))) syo (201 de,
T
Lscore (U)) =
w(ll Em Em moy d (pp(ar)) - 800 (s0slc)) dl,
(16)
/
T
Cscore-ER =
w(t) Eaeenn
(d(Vaz) Vo. (00,)()))))))))))))))))))))))))) - Sep (2r dt,
LDivED = - - + XLscore ER
This formulation offers more elfective similarity metric between the generated content and diffusion- reward-hased image distributions, yielding 3D outputs that are both more diverse and higher fidelity than those produced using traditional KL divergence.
Although this divergence may initially seem intractable, recent work (Luo et al. 2025 shows that the gradient of this divergence with respect to 0 can be efficiently computed without directly differentiating the score functions by introducing a separate approximation network. For full derivation and implementation details, please refer to Appendix.
4.3 Analysis of Score-based Divergence
Our methodology, which leverages Score-based divergence (SIM), offers significant advantages over conven- tional generative objectives basod on KT-divergence, such as those used SDS. The primary limitation of KL-divergence-bl based methods their inherent "modo-sceking" nature, which frequently leads mode collapse. This results in generated samples that, while often high in quality, lack diversity.
8

---

Published in Transactions on Machine Learning Research (12/2025)
This phenomenon can be intuitively understood by analogizing the target data distribution to complex mountain range with multiple peaks, where each peak represents data mode. A KT.-based objective function akin to a GPS system that identifies the most efficient path to the single highest peak, consequently ignoring all other valid destinations. In contrast, our SIM-based approach constructs a complete topographical map of the entire landscape. By matching the score function tte gradient of the log-probability density- -a every point, our model illuminates the complete topology of the data distribution. This enables the generation of truly diverse results that cover all modes of the distribution.
From theoretical standpoint, the mode collapse issue in the KL-divergence stems from its mathematical formulation, which soeks to minimize the following objective:
DKL(PI1Q) = Ez~p log P(x)
(17)
Q(x)
The objective's reliance on the likelihood ratio P(x)/Q(x)) renders it ill-defined and unstable, particularly when the support of the generated distribution (P) and the target distribution (Q) are misaligned. This instability compels the model to converge onto single, "safe" mode of where the ratio is well-behaved, thereby discarding other modes.
Our SIM-based approach circumvents this fundamental flaw by minimizing a score- based divergence instead:
DSIM (PllQ) = log
V log Q(x))
(18)
Rather than comparing an unstable ratio of densities, this objective directly aligns the vector fields of the score functions aV log P(x) and v log Q(x)). This score-matching process is inherently robust to mis- aligned support between distributions. Consequently, our model is empowered to learn the entire topological landscape of the target distribution, thereby preserving all of its modes and fostering superior generative diversity.
5 Experiment
In this section, we evaluate how our proposed ccore-based divergence optimization enhances both quality and diversity in text-to-3D synthesis. We perform comprehensive experiments on the CPTEval3D benchmark [Wu] et al. (2024a), supplemented by additional 2D and 3D assessments that demonstrate the effectiveness and diversity of the method.
5.1
Evaluation on the GPTEval3D Benchmark
Sctup.
We first evaluate Dive3D on 110 creative and complex prompts from the GPTEva13D benchmark Wu
et
al.
(024a)) comparing against 9 state-of-the-art methods, including DreamFusion Poole et al. (2022)
Dream
Gaussian Tang et al. (2023b) Instant.3D Li et al. (2023). Fantasia3D Chen et al. 2023) ) Latent-
NeRF Metzer et al. (0022), Magic3D Lin et al. (2023), ProlifccDreamer Wang al. (2023c) MVDream Shi
et al.
2023), and Rewadd Ye et al. (2024) All experiments use PyTorch and the ThreeStudio frame-
work Guo et al. 20023 testing both MVDream Shi et al. (2023) and Stable Diffusion Rombach et al. (0222
as diffusion backbones, and PickScore Kirstain et al. (2023) as the reward model. Optimization takes about
one hour per object on a single NVIDIA A100 GPU.
Quantitative Results. Table reports performance of our method across six metrics, including text-asset alignment (+53.5), 3D plausibility (+49), text-geometry alignment (+68.2), texture dctails (+67.5), geom- etry details (+35.3), and overall performance (+50.0), where "4" indicates improvement and indicates degradation relative to the state of the art. Dive3D achieves the top rank on every metric, demonstrating that score-based divergence quidance-ospecinn gains over both diffusion-only and reward-angunnteed baselines.
when combined with reward models-vicld substantial
Qualative Results. Figure g compares Dive3D against methods built on Stable Diffusion (e.g., Dream- Fusion, Fantasia3D, ProlifieDreamer), which often struggle with fine details or prompt adherence. By op- timizing a score-based divergence that unifies text-conditioned diffusion priors with a differentiable reward model, Dive3D consistently produces high-fidelity, semantically precise 3D assets.
9

---

Published in Transactions on Machine Learning Research (12/2025)
Table 1: Quantitative Results on 110 Prompts from the GPTEval3D Benchmark Wu et al. (2024a). We compute all six GPTEvaI3D metrics-tor text alignment, 3D plausibility, texture- -oomurey co- herence, geometry details, texture details, and overall score comprehensively evaluate 3D generation quality. Dive3D achieves the highest score every metric, demonstrating its superior performance.
Prompts from GPTEva13D
Wu et al.
2024a
Method
Aligmment
Plausibility
T-G Coherency.
Geo Details
Tex Details
Overall
Dream Poole et 2022
1000.0
1000.0
1000.0
1000.0
1000.0
1000.0
DreamGausiia Tang et al. 2023b
1100.6
953.6
1158.6
1126.2
1130.8
951.4
Fantasia3DCChnn al. 2023
1067.9
891.9
1006.0
1109.3
1027.5
933.5
Instant3 Li et al. 2023
1200.0
1087.6 6
1152.7
1152.0
1181.3
1097.8
Latent- SeattNeRRKMinn Metzer a..
2022
1222.3
1144.8
1156.7
1180.5
1160.8
1178.7
Magic3I Lin et [2023
1152.3
1000.8
1084.4
1178.1
1084.6
961.7
ProlifiDrreame Wang et al.
[2023c
1261.8
1058.7
1152.0
1246.4
1180.6
1012.5
SyncDreame Liu al.
2024
1041.2
968.8
1083.1
1064.2
1045.7
963.5
MVDream Shi ot al. 2023
1270.5
1147.5
1250.6
1324.9
1255.5
1097.7
DreamReward Ye et al.
[2024]
1287.5
1195.0
1254.4
1295.5
1261.6
1193.3
DIVE3D (Ours)
1341.0
1249.0
1322.6
1360.2
1329.1
1243.3
Our metrics differ from those reported in the original DreamReward because GPT-4V has been deprecated GPTEval3D, instead use GPT-4o-mini.
Additional examples in Figures and TC compare Dive3D with MVDream and DreamReward. While MV- Dream preserves geometric consistency, it sometimes deviates from the prompt content (missing keywords highlighted in red). DreamReward improves aligmment but remains constrained by its KL-based formulation and associated mode collapse. In contrast, Dive3D faithfully follows the prompt, delivers rich detail and appcaling aesthetics, and maintains strong visual coherence.
Table 2: Quantitative comparison of diversity and quality, Arrows indicate whether higher (t) or lower (L)
values are better.
Method
CLIP-D t
LPIPS-D t Chamfer-D f
FID +
Prolific Dreamer (KL-based)
0.3908
0.485
0.072
207.34
Dive3D (Ours, Score-based)
0.4483
0.551
0.115
168.59
5.2 Analysis Generation Diversity
Setup. We then show that score-based divergences produce more diverse, information-rich outputs than traditional KL-based losses. To evaluate this, test method in both 2D and 3D settings-usse -sing Stable Diffusion Rombach
al. (2022) as the backbone. In 2D, represent scenes with 2D Neural Radiance Fields; in 3D, we use full 3D NeRFs. We primarily compare against ProlificDreamer Wang et al. (2023c). the leading KL-divergence based method that leverages variational score distillation (VSD) to maximize diversity in text-to-3D generation. On a single NVIDIA A100 GPU, our 2D experiments complete in roughly minutes, while the 3D evaluations take about 9 hours.
2D Results. We begin by evaluating 2D generation, where we distill 2D neural field from text-to-image diffusion model. This task shares the same mathematical formulation computationally less demanding because it does not involve camera poses. As shown in Fig, 5 for both game character and realistic architecture generation tasks, the score-based divergence consistently produces more diverse samples Uhan KL divergence. For instance, when generating "a realistic Japanese building," " the KL-based method consistently generates towers with standard color schemes (predominantly red and blue), uniform backgrounds (lush green treos), and similar weather and time conditions (sunny daytime). In contrast, the cosee-based approach generates outputs with varied lighting (c.g., night scenes, snowy settings) and diverse architectural features (e.g., towers, pavilions, and residential houses). A similar trend is observed
text-to-3D problem but is
10

---

Published in Transactions on Machine Learning Research (12/2025)
in the game character generation task: while the KL-based SDS loss tends to produce similar archetypes, the score-based loss reveals a wider range of characters, clothing styles, and backgrounds.
3D Results. These diversity gains naturally and effectively generalize to 3D synthesis. Figure compares the output for "a pirate ship in the sky" under the KL-based VSD loss versus our score- based divergence. As expected, our approach produces a far wider range of geometric shapes, surface textures, and background scenes from bright sunny skies to dark thunderous clouds. Figure [1] offers additional examples across diverse prompts to reinforce this finding, illustrating how score-based divergence yields richer variation in colors, object styles, material properties, and environmental details.
To quantitatively validate that our method enhances diversity, we have also conducted a large-scale evalua- tion. We generated 12,800 images from 50 diverse prompts and assessed them using three standard metries: Pairwise CLit-Distance (CLIP-D) for inter-sample diversity, average pairwise LPIPS for perceptual diversity, and Fréchet Inception Distance (FID) to evaluate overall set quality. As summarizod in Table 2 our method significantly perperforms the KL-based baseline (ProlifieDreamer on all three metrics. These improvements are statistically significant, as all p 0.01 with paired t-tests (tClır(49) 5.21 and tFID -8883)
providing strong quantitative evidence that our method effectively mitigates mode collapse.
5.3 Computational Cost
We analyze the runtime and memory overhead of Dive3D in Table 3 The cost depends on the diffu- sion backbone. When using MVDiffusion, our method is comparable to other reward-guided methods like DreamReward. When using Stable Diffusion, our het is slightly slower than ProlificDreamer (NeRF) and requires more memory due to the reward guidance. In both scenarios, the computational cost is justified by the significant gains in generation quality and diversity. All experiments were run on a single NVIDIA A100 GPU.
Table 3: Computational cost and performance comparison, broken down by diffusion backbone.
Diffuusion Backbone
Method
Generation Time
Peak Memory (GB)
MVDream
0.7 hours
14
MVDiffusion
DreamReward
1.0 hours
~2
Dive3D (Ours)
1.0 hours
22
PreaifiDDreamer
7.9 hours
27
Stable Diffusion
Dive3D (Ours)
9.0 hours
35
6 Conclusion
In this paper, present Dive3D, novel framework that boosts both diffusion-based distillation and reward- guided optimization by substituting their asymmetric KT-divergence objectives with powerful score-based divergence. Evaluated on the GPTEval3D and other benchmarks, Dive3D effectively alleviates mode collapse, yielding substantially greater diversity while simultaneously improving text alignment, geometric plausibility, and visual fidelity.
Limitations and Future Work. Although Dive3D delivers compelling results, its runtime remains slower than recent LRM- based methods. In future work, we plan to integrate our score-based divergence with latent reconstruction models by first distilling diverse, text-driven multi-view generators and then combining them with LRM techniques to achieve rapid, high-fidelity, and diverse 3D synthesis.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (63371007)) the National Key Research and Development Program of China (2024YFC3406400) and Biomedical Computing Platform of National Biomedical Imaging Center, Peking University.
11

---

Published in Transactions on Machine Learning Research (12/2025)
References
Donghoon Ahn, Hyoungwon Cho, Jacwon Min, Wooscok Jang, Jungwoo Kim, SoonHwa Kim, Hyun Hee Park, Kyong Hwan Jin, and Seungryong Kim. Self-rectifying diffussin sampling with perturbed-attention guidance. In European Conference on Computer Vision, pp. 1-17. Springer, 2024.
Mohammadreza Armandpour, Huangjie Zheng, Ali Sadeghian, Amir Sadeghian, and Mingyuan Zhou. Re- imagine the negative prompt algorithm: Transform 2d diffusion into 3d, alleviate jamus problem and beyond. arXiv preprint or urXiv:2304.0420 2023.
Yogesh Balaji, Seungjun Nah, Xum Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, al. ediff-i: Text to-too---mm to-image diffusion models with an ensemble of expert denoisers. Xiv preprint arXiv:2211.0132, 2022.
Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In Proceedings of the IEEE/C /CF international conference on computer vision, pp. 22246 22256, 2023.
Zhengyang Geng, Ashwini Pokle, William Luo, Justin Lin, and J Zico Kolter. Consistency models made easy. Xiv preprint Kiv:222006. 14548, 2024.
Y'aan-Chen Guo, Ying- Tian Liu, Ruizhi Shao, Christian Laforte, Vikram Voleti, Guan Luo, Chia-Hao Chen, Zi-Xin Zou, Chen Wang, Yan-Pei Cao, and Song-Hai Zhang. threestudio: A unified framework for 3d content generation. https://g/ ht//gtthub.ceo http://///.tteeeeeettooooooo/ee/ http///ttt/teeeuuiioooo 2023.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint ar Xrc:22200.1150 2022.
Jonathan Ho, Ajay Jain, and Picter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-688 6851, 2020.
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400 2023.
Hanzhe Hu, Tianwei Yin, Fujun Luan, Yiwei Hu, Hao Tan, Zexiang Xu, Sai Bi, Shubham Tulsiani, and Kai Zhang. Turbo3d: Ultra-fast text-to-3d generation. ar preprint arXiv:24 12.24222.44 12.04470 2024.
Yukun Huang, Jianan Wang, Yukai Shi, Boshi Tang, Xianbiao Qi, and Lei Zhang. Dreamtime: An improved optimization strategy for diffusion-guided 3d generation. In The Twelfth International Conference on Learning Representations, 2024a. URL https:///penreriion http:/oormm/dddddllyyyaa.
Zemin Huang, Zhengyang Geng, Weijian Luo, and Guo-jun Qi. Flow generator matching. arXio preprint arXiv:2410.1900 19310, 2024b.
Ajay Jain, Ben Mildenhall, Jonathan T Barron, Picter Abbocl, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
2022.
Tero Karras, Miia Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine. Guiding a diffusion model with bad version of itsclf. Advances in Neural Information Processing Systems, 37: 53091, 2024.
Oren Katzir, Or Patashnik, Daniel Cohen-Or, and Dani Lischinski. Noise-free score distillation. In The Twelfth International Conference on Learning Representations, 2024. URL httpe://openrevvie: net/ Tormm
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42( 42(4):139-1 2023.
12

---

Published in Transactions on Machine Learning Research (12/2025)
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick-a-pic: An open dataset of user preferences for texi-to-image generation. Advances in Neural Information Processing Systems, 36:36652 00066522-666,, 2023.
Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXi22311.0021, 2023.
Tiancheng Li, Weijian Luo, Zhiyang Chen, Liyuan Ma, and Guo-Jun Qi. Self-guidance Boosting flow and diffusion generation on their own. arXiv preprint arx: 12.002277 2024.
Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. arXiv preprint arXiv:2311. 112884 2023.
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming- Yu Liu, and Tsung-Y Yi 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.
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao. Pseudo numerical methods for diffusion models on manifolds. arXiv preprint or Xiv:2200 09778, 2022.
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero- 1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVP International Conference on 2023.
Computer Vision, pp.
Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syne- dreamer: Generating
images from a single-view image. In The Twelfth International Conference on Learning Representations, 2024. URL
Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song- Zhang, Mare Habermann, Christian Theobalt, al. Wonder3d: Single image to 3d using cross-domain diffusion. Xi preprint ar (rXii:2310.15000 2023.
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775- 5787, 2022.
Weijian Luo. A comprehensive survey on knowledge distillation of diffusion models. ar preprinl orXivv2304.0420 2023.
Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhihua Zhang. Diff-instruct: A universal approach for transferring knowledge from prentrdined diffusson models. Advances in Newral Information Processing Systems, 7776525-76546 2023a.
Weijian Luo, Boya Zhang, and Zhihua Zhang. Entropy-based training methods for scalable neural implicit samplers. Advances in Newral Information Processing Systems, 36:7137-711 2023b.
Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhihua Zhang, Diff-instruct: A universal approach for transferring knowledge from pro-trained diffusion models. Advances in Neural Information Processing Systems, 36, 2024a.
Weijian Luo, Zemin Huang, Zhengyang Geng, J Zico Kolter, and Guo-jun Qi. One-step diffusion distillation through score implicit matching. Advances in Neural Information Processing Systems, 37:115377-115408, 2024b.
Weijian Luo, Colin Zhang, Debing Zhang, and Zhengyang Geng. Diff-instruct* Towards human- preferreee one-step text-to-image generative models. Xixiv preprint or arxiv 10..0888, 2024c.
13

---

Published in Transactions on Machine Learning Research (12/2025)
Weijian Luo, Zemin Huang, Zhengyang Geng, J Zico Kolter, and Guo-jun Qi. One-step diffusion distillation through score implicit matching. Advances in Neural Information Processing Systems, 37:115377-115400, 2025.
Baorui Ma, Haoge Deng, Junsheng Zhou, Yu- Shen Liu, Tiejun Huang, and Xinlong Wang. Geodream: Discntangling 2d and geometric priors for high-fidelity and consistent 3d generation. arXiv preprint arXiv:2311.19971, 2023a.
Yiwei Ma, Xiaoqing Zhang, Xiaoshuai Sun, Jiayi Ji, Haowei Wang, Guanman Jiang, Weilin Zhuang, and Rongrong Ji. X-mesh: Towards fast and accurale text-driven 3d stylization via dynamic textual guidance. In Proceedings of the IEEE/CVE International Conference on Computer Vision, pp. 2749-2760, 2023b.
Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or Latent-nerf for shape- guided generation of 3d shapes and textures. arXiv preprint or arxiv:2211 Shw:2211.0700 1.07600, 2022.
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.
Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In SIGGRAPH Asia 2022 conference popers, pp. 1 1-8, 2022.
Naila Murray, Luea Marchesotti, and Florent Perronnin. Ava: A large-scale database for aesthetic visual analysis. In 2012 IEEE conference computer vision and pattern recognition, pp.
IEEE, 2012.
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar. Diffusion models for adversarial purification. arXiv preprint arXiv:2205. 004402200000440, 2022.
Ben Poole, Ajay Jain, Jonathan Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXio:2209.1488. 14988, 2022.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkinn Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748 8763. PMLR, 2021.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.066 201,10 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esscr, and Björn Ommer. High-rcsolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on compuler vision and pattern recognition, pp. 10684 10695, 2022.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, et al. Photorealistic text- to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479 36494, 2022.
Aditya Sanghi, Hang Chu, Joseph G Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, and Ka- mal Rahimi Malekshan. Clip-forge: Towards zero-shot text-to-shape generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognation, pp. 18603-186) 18613, 2022.
Aditya Sanghi, Rao Fu, Vivian Liu, Karl DD Willis, Hooman Shayani, Amir H Khasahmadi, Srinath Sridhar, and Daniel Ritchie. Clip-sculptor: Zero-shot generation of high-fidelity and diverse shapes from natural language. In Proceedings of the IEEE/CVF Conference on Compuler Vision and Pattern Recognition, pp. 18339-18348, 2023.
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. Xie preprint or 2r0 16512, 2023.
14

---

Published in Transactions on Machine Learning Research (12/2025)
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pp- 2256-2263 PMLR, 2015.
Jiaming Song, Chenlin Mong, and Stefano Ermon, Denoising diffusion implicit models. ar preprint wwXiv:2010.02500 2020a.
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishck Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprinl arXiv:2011.1945,, 2020b.
Boshi Tang, Jianan Wang, Zhiyong Wu, and Lei Zhang. Stable score distillation for high-quality 3d genera- tion. arXiv preprint or Xiv:23112 09305, 2023a.
Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arx:::2309. 16653, 2023b.
Pascal Vincent. A connection between score matching and denoising autoencoders. Neural computation, 23 (7):1661- 1674, 2011.
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pp. 12619 12629, 2023a.
Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest landola, Rakesh Ranjan, Yilci Li, Qiang Liu, Zhangyang Wang, et al. Taming mode collapse in score distillation for text-to-3d generation. arXiv preprint vxXi:22401.000 00909, 2023b.
Peng Wang and Yichun Shi. Imagedream: Image- sage-prompt multi-view diffusion for 3d generation. arXiv preprint 2rXi::2312.02201, 2023.
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolific- dreamer: High-fidelity and diverse text-to-3d gencration with variational score distillation. arXiv preprint arXiv:2305. 16213, 2023c.
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolifiedreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36, 2024.
Haohan Weng, Tianyu Yang, Jianan Wang, Yu Li, Tong Zhang, CL Chen, and Lei Zhang. Consistent123: Improve consistency for one image to 3d object synthesis. arXiv preprinl arXiv:2310. 0800809, 2023.
Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. Tn CVPR, pp. 22227- 22238, 2024a.
Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, and Hanwang Zhang. Consistent3d:: Towards consistent high-fidelity text-to-3d generation with deterministic sampling prior. In Proceedings of the IEEE/CVP Conference Compuler Vision and Pallern Recognition, pp. 9892 9902, 2024b.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong, Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Newral Information Processing Systems, 36:15903 15935, 2023.
Shuchen Xue, Mingyang Yi, Weijian Luo, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhi-Ming Ma. Sa-solver: Stochastic adans solver for fast sampling of diffusion models. Advances in Neural Information Processing Systems, 96:77632-77674, 2023.
Runjie Yan, Kailu Wu, and Kaisheng Ma. Flow score distillation for diverse text-to-3d generation, 2024.
15

---

Published in Transactions on Machine Learning Research (12/2025)
Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-L-to-3: Consistent image to 3d view synthesis via geomelry-aware diffusion models. arXie preprint wrXiv23300003020 2023.
Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference, 2024.
Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6796 6807, 2024.
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William Freeman, and Taesung Park. One-step diffusion with distribution matching distillation, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6613-6623, 2024.
Xin Yu, Yuan- Guo, Yangguang Li, Ding Liang, Song- Hai Zhang, and XIAOJUAN QI. Text-to-3d with classifier score distillation. In The Twelfth International Conference on Learning Representations, 2024. URL https::/ http:/iewinnntteeeeeeeeeeeeeeeee net/f htte/forrmmaa -dttnnndt
Boya Zhang, Weijian Luo, and Zhihua Zhang. Enhancing adversarial robust via score-based optimization. Advances in Newral Information Processing Systems, 11.51800-5182)) 2023a.
Boya Zhang, Weijian Luo, and Zhihua Zhang. Purify++: Improving diffusion critusiio-purffiaattio with advanced diffusion models and control of randomness. arXiv preprint arXin:2310.18700 2023b.
Qinsheng Zhang and Yongxin Chen. Fast sampling of diffusion models with exponential integrator. arXiv preprint 2rXiv:2204.33900 2022.
Mingyuan Zhou, Zhendong Wang, Huangjie Zheng, and Hai Huang. Long and short guidance in score identity distillation for one-step text-to-image generation. arXiv preprint arXiv:2406.01550 2024a.
Mingyuan Zhou, Huangjic Zheng, Zhendong Wang, Mingzhang Yin, and Hai Huang. Score identity distilla- tion: Exponentially fast distillation of pretrained diffusion models for one-step generation. In Forty-first International Conference on Machine Learning, 2024b.
Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. HIFA: High-fidelity text-to-3d generation with advanced diffusion guidance. In The Twelfth International Conference Learning Representations, 2024. URL
16

---

Published in Transactions on Machine Learning Research (12/2025)
A Derivation of the Score-based Loss Function
The dcrivation is based on the so-called score-projection identity which was first found in Vincent (201 1] to bridge denoising score matching and denoising auto-encoders. Later, the identity is applied by Zhou el al. 20244b;; Luo et al. (2025) for deriving distillation methods for image generation. We appreciate the efforts of Zhou et al. (2024b] and introduce the conditional score-projectio identity for inverse problems here:
Theorem A.1. (Conditional scon--rrojecton identity). Let u(ū, 0) be a vector-valued function, under mild conditions, the identily holds:
E (aoll)) u(x, y, 0)T {spo.. (2rc9) Vas log logg(())))0)) O, 49. m (maa mog (m, (mo)
(19)
Next, we turn to derive the loss function.
Proof. We prove a more general result. Let uaū) be a vector-valued function, the so-called score-projection
identity Vincent 2011 holds,
E woropo (woll) (wall)) u(x,, y, 0)T {spa (arel)) - V an log (ar)) 0, 40.
(20)
Taking 0 gradient both sides of identity
20 have:
0 E stropa (xoll)
{ u (zr, 0) (Spo.. (20.1)) Van, log ge (aex 220)
doms
an (m (nn)
Owg u (Ie, y. ²)²² {- (-V,, log de (aea m0)3 }
sampp (ato (y) in (mon-n (mt (mo)
Owo
u D1,, y, T)T T 0o Spa, (xali)} + do (xx,, y, 9)T Spo.e (xe (
E
mampp (ato (y) (
u (Ee, y. ²)² 00 Spo. (xel))}
mompo(oool w) ae (aonn (30a (ea)
(Ie, y, 0)" {spo. (xily) Va. a. log de (x,, (mo) } dzl
+E E mamppu (mto (v)
an ( (200)
u (Ft, y, 0)T { (- log ai (xr (20))
Axg 20 - (xx, y. 0)T spo., (2o (20:11)
Amo
E Exi u ( y, 0)T 00 {sp.. (0 +
a
Exoxpo, (x,, y, 0)" peotoo (30, (3) - Vx, log (20))
50
(21)
Therefore, we have the following identity:
Enir-po (xi, y, 0)r δ
8
00 {spe.. (axll))
Exo-po (xt, y, 0) ( (wr:y) V.,, log ge (30a (20))
00
(22)
which holds for arbitrary function u(ū 0) and parameter 0. If we set
u( u(x,, y. d'(kss)
(23)
k. Spoio),1 (2r13)- - Sg. (xx) - Sge (3/(z))
Then we formally have the loss function as:
loss Exxerrpo --d' (ks))² {Smagor (xr/v) Van. ae, log do (x)
(24)

---

Published in Transactions on Machine Learning Research (12/2025)
B Theoretical Proof of SIM's Robustness Against Mode Collapse
This section provides formal mathematical proof that the score-based loss (SIM) function is theoretically more robust against mode collapse than the KL divergence-base loss function. The proof is structured by first. formally defining a mode-seeking loss function, then proving that Kī-divergence satisfies this definition while SIM does not. This culminates in the main theorem establishing SLM's theoretical advantage.
B.1 Definitions
Definition B.1 (Mode Collapse). Let Qxx) be target probability distribution on a space X' S R pos- sessing a set of distinct modes (high-density regions) {M, Let Pe(x) be the distribution generated by a model with parameters 0. The model is said to suffer from mode collapse the support of its distribution, supp(Po), fails to cover the union of the significant modes of Q, i.e,
R
(Po) S M. 1=1
M,
(25)
Definition B.2 (Mode-Seeking Loss Function). A loss function L(Po, Q) is defined mode-seeking if it satisfies the following property: For any region R c X' of extremely low target density (i.e., suPreR Q(x) < for some + 0), if the generated distribution assigns non-vanishing probability mass to this region (i.e., J Po(x)dx > c for some constant с > 0), then the loss must diverge,
L(Ps, Q) + co.
(26)
Intuitively, a mode-sceking loss function infinitely penalizes the generator for exploring low-density regions of the target distribution.
B.2 Lemmas
Lemma B.1. The Kullback-Leible (KL) divergence, DKE(PellQ, is mode-secking loss function.
Proof. The KL-divergence is defined as:
LKL(Po)= (Ko(Pe)))) / Po(z) log
Po(x) dr ((z)
(27)
Pa(x) log Po(x)dx / Po(x) log Q(z)dr.
(28)
Let us analyze the second term, the negative cross-entropy T T= Sx Po(x) log Q(x)da.
Consider a region R that satisfies the condition in Definition 2, such that Vo E R, Q(x) < e with - 0. As consequence, log Q(x) + ooo and thus log Qxx) + too for all x E R.
Now, assume the generator assigns a non-vanishing probability mass to this region, JR Po(x)dx c >
The contribution to the loss from this region is:
/ Po(x) log Q(x)dr.
TR --
(29)
Since (o(x)) is probability density function and its integral over R is positive constant, while log Q(z) diverges to +00 over this entire region, the value of the integral must also diverge:
TR
(30)
As the total loss (Po) is the sum of terms including TR, it follows that L (Po) + co. Thus, DK (Pol satisfies the property of mode-seeking loss function per Definition 2.
2

---

Published in Transactions on Machine Learning Research (12/2025)
Lemma B.2. The SIM loss function, LSIM, is not mode-sccking loss function, under the mild assumption that. (xx is continuuusly differentiable.
Proof. The SIM loss is defined by matching the score functions of the two distributions, sp(x) Vz log P(z)
and sQ(z) = V log (x)):
LITM (Pa) Bewx(H) [I log Po(x)) Va log Q(x))l+)
(31)
where (22) is sampling distribution. We assume that the target distribution Q(x) is continuously dif- ferentiable (Ca smooth), which is a standard assumption for distributions defined by stochastic differential equations in diffusion models.
Consider again the region R where SuP,ER Q(z) + 0. The critical term to analyze is the target score function, (Q(x) = v log (z). By the chain rule, this is sQ(x)= v. Qu.( Although the denominator Q(z) 0, the numerator V (=Q(z) also approaches zero if in a region a local minimum the probability density. For smooth distributions like Gaussian mixtures, the score function is well-defined and bounded across the entire space. Even low-density regions between modes, the score vector V, log Q(x) is finite vector pointing
Q(x)
towards the nearest mode; it does not diverge to infinity.
Therefore, on any compact subset of X, the score function sa(x) is bounded, i.e., there exists constant K such that [z)]l < K. Consequently, the loss contribution from any point E R is:
| log Pa(x) - s0()]1?
(32)
which remains finite value. The loss LSIM does not diverge to infinity merely because the generator samples from a low-density region of the target. Thus, LsrM does not satisfy the property of mode-sccking loss function as per Definition 2.
B.3 Main Theorem
Theorem B.3. An oplimization process guided by mode-seeking loss function is theorelically susceptible to mode collapse. Conversely, a process guided by loss function that is not mode-seeking is theoretically more robust against mode collapse.
Proof. From Lemma 1, we have proven that the KI-divergence is a mode-sceking loss function. Any opti- mization algorithm (e.g, gradient descent) atlempting to minimize Lkt. will receive an unbounded penalty for placing probability mass outside the modes of Q. The optimizer is therefore strongly incentivized to adjust such that the support of Pd bocomes a strict subset of the high-density regions of Q. This directly leads the condition of mode collapse as defined in Definition 1.
From Lemma we have proven that the SIM loss is not mode-seeking. An optimizer minimizing LSIM receives finite and meaningful gradient information across the entire data space, including low-density regions. There is no infinite penalty that forces the optimizer to "escape" these regions. Instead, it is guided to match the vector field score functions globally. This allows the learned distribution Po to capture the full topology of the target distribution Q, making the optimization process inherently more robust to mode collapse.
Therefore, the SIM framework provides a stronger theoretical foundation for generating diverse samples compared to frameworks based on KL-ddieegence.
C Implementation Details
Pseudo-code for Dive3D detailed pseudo-code for Dive3D is presented in algorithm
In this paper, we conduct experiments primarily using a NVIDIA A100 Tensor Core GPU, with the former mainly employed for NeRF and 3D Gaussian Splatting generation. For our quantitative evaluations, we maintain consistent experimental setups across all methods, employing similar pipelines that include 3D
3

---

Published in Transactions on Machine Learning Research (12/2025)
Algorithm
Pseudo-code for Dive3D
Input: Text-to-image diffusion score network s and its LoRA parameters y: Reward function R(:,.)
2: Input: Text prompt y, Differentiable 3D renderer g(0, v)
3: Input: Learning rate for 3D parameters 8, Timestop weighting function w(t)
4:
Input: Hyperparameters: y (guidance scale coefficient), À (reward weight)
Input: Noise schedule parameters at, dt
Initialization: Randomly initialize 3D representation parameters 0
while not converged do Sample camera viewpoint v
10:
11:
Render 2D image from current 3D model: xo = g(0, v) Sample timestep t Uniform(O, T) and noisc NO
12:
13:
Compute noisy image: I; 0r + dhE
14:
Compute score of the current rendered data distribution: Svendered 6/0
15:
16:
Score-based Conditional Diffusion Prior (S-CDP) Gradient
17:
Estimate conditional target score: Scond "cond_ttaret = ( V, Compute S-CDP gradient: VoLs-CDP Rs w(t)dscore (Scond targets 8 (3 V. t))
18:
19:
20:
Score-based Unconditional Diffusion Prior (S-UDP) Gradient
21:
Estimate unconditional target score: Suncond target = S (ate, 0, (using null text 0)
22:
Compute S-UDP gradient: VoLs-UDP R w(f)d (Buncond targets S
23:
24:
Score-based Explicit Reward (S-ER) Gradient
25:
Estimate denoised image (x) (c.g., using It, So. C1,
26:
Estimate largel reward score: Sreward treward_target V., R(y, (t)()))
27:
Compute S-ER gradient: VoCs EN w(t)dscorell (8reward targets S. (J y, y.t))
29:
Total Loss Gradient and Parameter Update
30:
Compute total Dive3D loss gradient (based on Eq. 16):
31:
VgLDive3D = (1 + - -VoLS- + AVLs-EER
32:
Update parameters: 0 + 0 a
33:
Update parameters: < DenoissinSSoreeMtcccittt..
34:
end while
reprosentation selection, training procedures, shape initialization, and the use of teacher diffusion model. We uniformly apply identical negative prompts during the assessments. The primary distinction bet ween the methods lies in their respective loss functions. In our experiments, we use CFG scale of 7.5 for mesh generation and 20 for Gaussian Splatting generation. We observed that a high CFG scale tends to oversaturate colors, while a low CFG scale hinders object convergence. With algorithmic improvements, we found that a smaller CFG scale can produce high-quality 3D objects with smooth colors.
For lext-to-3D generation, we utilize Stable Diffusion v2.1 and the MVDream model sd-v2.1-base-4viww To produce diverse 3D representations, geometry and mosh refinement stages as outlined in ProlificDreamer Wang et al. (2024). Additionally, we employ the Gaussian Splatting generation pipeline from DreasianDDeamell NeRF, mesh, and Gaussian Splatting outputs. To align the generated 3D content. with human preferences, we integrate two human RLHE models: ImageReward Xu et al. 2023 and PickScore Kirstain et al. (022))
adapt algorithm to generate NeRF outputs and follow the
Dreamen Yi et al. (2024), resulting in 3D
For PickScore, we use a scale of 100 for mesh generation and 10,000 for Gaussian Splatting generation. Empirically, found that high PickScore scale enhances visual richness but may lead to color oversatura-
4

---

Published in Transactions on Machine Learning Research (12/2025)
tion. The optimal PickScore scale ranges between 50-200 for mesh generation and 5,000-10,000 for Gaussian
Splatting generation.
Score-based Divergence Implementation For the score-based loss in Equation 15 we use the squared 22 norm as the distance function d, where d(pp: 8,) = ||sp - s,112 The sampling distribution m. simply the marginal distribution p(2) from the forward diffuuson process applied the current rendered image æ0 matching occurs in the data-rclevant regions of the diffusion trajectory.
c(,,c). This ensures that the It is important to note that our method does not require training separate score approximation network. Instead, it comprises the frozen weights of the original score network 86 with trainable LoRA layers injected into its attention blocks, which is exactly the same as ProlificDreamer [aang et al. (2023c). The LoRA parameters & are trained concurrentyy with the 3D representation parameters at each optimization step. The objective function for training y the standard denoising score matching (DSM) loss. At each step, using the rendered image 2g = g(0,c), we compute the noisy image 24 Qt + dtc. The DSM loss then minimizes the difference between the network's score prediction and the true score of the noisy data distribution, which is known to be -e/oi- This training step corresponds to Line 33 in our pseudo-code (Algorithm 1 and ensures that &p provides an accurate, up-to-date estimate of the score of the generator's s current output distribution, which is essential for the SIM loss calculation.
DreamGaussial Tang al. 2023b
Dreamer
Yi ei al. 2024
Dive3 (Ours). Particle
Dive3D (Ours), Particle
Flamethrower with frre, cyberpunk...
Shiba sitring ground.
Figure 6: Comparison with baselines on Gaussian Splatting. Dive3D can generate diverse and human- preferred 3D Gaussian splattings.
D Ablation Study
D.1 SIM vs. Reward Guidance
To disentangle the contributions of our score-based divergence (SIM) and reward guidance, conduct an ablation study. As shown in Table the results demonstrate that switching from a KL-based objective to SIM is the primary driver for increased diversity (CLIP-D). Subsequently, adding the reward term is key achieving state-of-the-arl quality and texi-alignment (ImageReward score), clarifying the distinct role of each component.
Table 4: Ablation study on the components of Dive3D.
Method
Loss Formulation Diversity (CLIP-D)
Quality (ImageReward)
Baseline (PraaieicDreamme)) Dive3D (No Reward)
LkL
0.3908
0.802
LSTM
0.4412
0.839
Dive3D (Full)
CSIM + Reward
0.4483
0.977
D.2 Impact of Divergence Loss Combinations
In this section, we investigate how different combinations of text-to-3D divergence losses affect generation performance. Our framework involves three fundamental divergence losses: the CDP loss, the UDP loss.
5

---

Published in Transactions on Machine Learning Research (12/2025)
3DGS
NeRF
Mesh
3DG3
NeRF
Mesh
Figure 7: Dive3D can generate 3D objects across different 3D representations. We show the results Gaussian splitting, NeRF and Mesh. The prompts are A 3D model of an adorable cottage with thatched roof and A cauldron full of gold coins.
and the ER loss.
LDiveaD CyCC agLUDP aggLER
(33)
We systematically explore the effects of different loss weights by analyzing all possible paired combinations.
CDP and UDP As shown in Fig. g(),, assigning large weight to the CDP loss preserves generation diversity but results in blurred outputs, while a large weight on the UDP loss introduces significant artifacts, preventing the 3D generation from capturing basic image features. Empirically, the optimal performance is achieved when the weights for CDP and UDP are balanced, ie., when the ratio az/22 is approxinately one. This finding aligns with the observations for tuning the CFG loss (oss(Wang& et al.] 2024), which typically attains the best performance when the key component, balanced linear combination of CDP and UDP losses, is strongly emphasized.

---

Published in Transactions on Machine Learning Research (12/2025)
Magic gun, game mystery.
Jedi duck holding lightsaber.
Magic dagger, mystery, ancient.
An airplane made of wood.
Figure 8: Dive3D 3D Gaussian Splattings. Dive3D can generate anime-slyle and game-sttle 3D Gaussian Splattings.
CDP and ER We employ two human-pre preeeer-cceeencee reward modcls, ImageReward Xuu et al.) 2023) and PickScore (Kirstain al. 2023), in our experiments. As shown in Fig. [b), in both cases, higher ER scores lead to improved 3D generation by enhancing text alignment and revealing finer textual details. Different reward models can guide generation in distinct ways; for instance, in our test case, ImageReward and PickScore exhibit different preferences for color combinations that evoke sense of mystery in the generated game assets.
UDP and ER We further explore the combination UDP and ER losses a configuration not previously investigated text-to-3D generation. As shown Fig- g( c), this novel combination also produces reasonable 3D objects, demonstrating that text-to-3D generation possible without relying on toxt-to-mmao model. However, we observe (hat its performance highly sensitive to the initialization of the 3D representation; poor biased initialization trap the algorithm in local minima in the absence of text-to-image guidance, Consequently, although the ER+UDP combination is promising, integrating all three basic losses yields the best overall results.
D.3 Hyperparameter Design and Selection
The weights for the core divergence terms in Equation 33 (an, 022, 0a) were determined empirically on validation subset of 10 prompts. Our goal was to balance text-alignment, realism, and diversity. The systematic exploration shown in Figure 9 of the main paper provides a detailed visualization this process. For the final model, we found that setting the ratio 01/02 close to (consistent with the (1+r) and -y formulation) and reward weight (corresponding to 03) that enhances detail without causing oversaturation yielded the best results. The final values used are consistent with the principles illustrated in that analysis.
E
More Results
We provide more results in Figure D
and

---

Published in Transactions on Machine Learning Research (12/2025)
aa)
02/a
(a) C = auu The prompt is: An amigurumi matorcycle.
03/01 =0 0
03/0 1e5
BaageRRward
PirkSeore
(b) L = CcDP + agLER- The prompt is: Magic game asset, mystery.
a3/ag
1c0
az/ag le4
PirkScure
(c) C= CC==opCuD + onCER. The prompt is: airplane
Figure Exploration of the design space of the unified divergence loss. We investigate their influence of different text-to-3D divergence losses on generation performance by adjusting their weights. 3D Gaussian splitting are used representations in these experiments for computational efficiency.

---

Published in Transactions on Machine Learning Research (12/2025)
MVDream
Ours
Oronge monarch butterfly resting dandelion
torn hot
KETO
MVDream
Ours
Several large, solid, shapes parcels, wrapped in brown paper and tied with string, each labeled with a different destination, awaiting dispatch post office
MVDream
Ours
pondering mysteries of Figure 10: More comparison results of Dive3D, showing improved quality over prior methods. All results are based on the MVDream pipeline Shi et al. (2023) using Multi-view Diffission Shi et al. 22023

---

Published in Transactions on Machine Learning Research (12/2025)
ProlificDreamer
Ours
refined vase with artistic pottems
ProlificDreamer
A rubbery cactus
ProlificDreamer
Ours
lomp casting
Figure 11: More comparison results of Dive3D, showing improved diversity over prior hods. All results are based on Stage 1 of the PreaieecDreamer pipeline Wang et al. (023ch using Stable Diffusion Rombach ct al. 2022), without further refinement.
10


Reviews:
[{"review_id": "wwnAO7U6TF", "reviewer": "Reviewer_DvKe", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes to tackle the problem of diversity in text-to-3D generation. They propose to use Score Implicit Matching instead of KL divergence to avoid model collapse. This allows for a better diversity since the loss isn't stuck in a high-density area of the KL divergence.\nThey first propose a unified KL divergence framework, which is a linear combination of three losses: one for prompt fidelity, one for diversity, and one for user reward. Then they change to score-based divergence instead of KL divergence. \nTo test their new method called Dive3D, they did a benchmark on GPTEval3D compared to 10 other methods. Then they propose a analysis of generation diversity.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper clearly explains the fundamental problem in text-to-3D generation. Most of the methods struggle to have high diversity in their generated images. This problem is clearly explained in the paper, and the authors proposed to use score-based divergence instead of Kl divergence to be able to have more flexibility in the generation. The claim of having better variability is proven in the experimental part where Dive3D outperforms 10 competitors on 5 different scores. In addition, the authors propose an experiment that focuses on diversity analysis. This study shows that Dive3D generated more diverse images.", "audience_yn": "Yes", "audience_explanation": "Dealing with diversity in text-to-3D generation is a problem that has been raised and studied in many papers in the field. The proposed method improves diversity, making this solution a good finding for the community.", "requested_changes": "- Dive3D proposes two improvements compared to the literature: expression of the loss in three terms: prompt quality, diversity, and reward, and use of the Score-based divergence. It could be nice to have an ablation study to see what helps the best for diversity and/or other scores of the benchmark.\n\n- One limit raised by the authors is the time computation of their method. For transparency, adding a comparison of the runtime would be a good addition. It can allow user to know if Dive3D is scalable for their application.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "aKLdN06fiM", "reviewer": "Reviewer_xLMJ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents Dive3D, a novel text-to-3D generation framework designed to address the limited diversity in existing diffusion distillation and reward-based text-to-3D methods. The key innovation lies in replacing the conventional Kullback–Leibler (KL) divergence objectives (used in Score Distillation Sampling, SDS, and related methods) with a Score Implicit Matching (SIM) loss. This score-based divergence encourages exploration of multiple high-probability regions, alleviating the mode-seeking tendencies inherent to KL minimization. The authors further unify diffusion-based and reward-based paradigms under a single divergence-based framework, enabling principled integration of diffusion priors, human preference signals, and diversity-promoting objectives. Experimental results demonstrate that Dive3D achieves state-of-the-art performance.\n\nStrengths:\n- This paper is well-motivated. The limited diversity and the asymmetric nature KL divergence are effectively recognized.\n- The proposed theoretical validation further supports the effectiveness and rationale of the proposed method.\n- This paper unifies diffusion distillation and proposes a reward-based optimization into a single divergence-based formulation, which can further simplify the training paradigm.\n- The experimental evaluation is quite extensive, and various visualizations under different scenarios are presented, which validates the effectiveness of the proposed method.\n\nWeaknesses:\n- Lack of ablation analyses of the reward and Dive3D objectives. There are multiple losses proposed to achieve the final performance, but they are not theoretically nor empirically understood. At the same time, it is unclear how the hyperparameters are chosen. Thus, the reproducibility of the paper would be difficult.\n- Lack of computational efficiency justification. Since SIM may require additional sampling or gradient estimation steps, it would be useful to report training time and computational overhead relative to SDS-based methods.\n- The paper claimed the diversity of the generation is one vital problem; however, it does not specify how to quantify whether the proposed method can indeed enhance generation diversity. Thus, it would be beneficial to incorporate various diversity metrics, such as feature variance, LPIPS across samples, or FID diversity, to justify the contribution.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Most of the claims are well-supported.", "audience_yn": "Yes", "audience_explanation": "The research topic is attention-drawing, and the performance is quite promising.", "requested_changes": "Please see the weaknesses part.", "broader_impact_concerns": "No ethical issues.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "2rOrxpMiO8", "reviewer": "Reviewer_oNEN", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper reformulates both Score Distillation Sampling (SDS) and reward-based text-to-3D methods as linear combinations of three KL divergence terms, revealing their shared mathematical foundation and common limitations in mode-seeking. To address this, the authors replace KL objectives with Score Implicit Matching (SIM) loss, which matches score functions rather than probability densities, thereby claiming to avoid mode collapse. The method achieves state-of-the-art performance on the GPTEval3D benchmark (top scores across all six metrics). It demonstrates qualitative improvements in both diversity and quality against nine baselines across multiple 3D representations.\n\nStrengths:\n- The KL divergence unification (Equations 10-14) provides valuable conceptual clarity, showing that different methods share underlying objectives.\n- Comprehensive evaluation showing consistent improvements in quality, text alignment, and diversity.\n- Mode collapse in text-to-3D is a fair limitation that impacts practical applications.\n- Works across multiple 3D representations (NeRF, mesh, Gaussian Splatting) and diffusion backbones.\n\nWeaknesses:\n- Claims SIM \"avoids mode collapse\" without formal proof. Section 4.3 provides intuition rather than mathematical justification.\n- Only 16 prompts for quantitative diversity assessment. This paper lacks geometric diversity metrics.\n- No runtime or memory comparisons with baselines, despite this being critical for practical adoption.\n- Distance function d, sampling distribution \\pi_t, and score approximation network training are unspecified.", "claims_and_evidence_yn": "No", "claims_explanation": "The central claim that SIM \"mitigates mode collapse\" lacks formal proof. Section 4.3 provides intuition rather than mathematical justification. The diversity claim is evaluated on only 16 prompts with two metrics (CLIP-D, FID), which is insufficient for claims of \"significantly more diverse\" outputs. Critical information is missing: no computational cost comparison and unspecified implementation details (distance function d, sampling distribution \\pi_t). In the meantime, GPTEval3D results and qualitative comparisons convincingly demonstrate quality improvements.", "audience_yn": "Yes", "audience_explanation": "The paper addresses mode collapse in text-to-3D generation, which is a timely problem for the ML and CV communities. The unified framework, revealing that SDS and reward methods share KL-based foundations, provides valuable conceptual insight. The empirical finding that score-based divergence improves both diversity and quality is relevant to researchers in diffusion models, 3D generation, and generative modeling.", "requested_changes": "- Either provide formal proofs that SIM avoids mode collapse, or reframe all claims as empirical observations rather than theoretical guarantees. Throughout the paper (abstract, introduction, Section 4.3, conclusion), replace phrases like \"effectively mitigates mode collapse\" and \"avoids mode-seeking tendencies\" with \"empirically demonstrates reduced mode collapse\" and \"shows less mode-seeking behavior in experiments.\"\n- Increase quantitative diversity assessment to at least 25-50 prompts across diverse categories. Add geometric diversity metrics specific to 3D (e.g., Chamfer distance between meshes for the same prompt). Include statistical significance testing (t-tests, p-values) for CLIP-D and FID improvements. Report complete user study protocol.\n- Add runtime comparisons with baselines, memory overhead measurements, and a computational cost table showing: Method | Time (hours) | GPU Memory (GB) | alongside quality/diversity scores.\n- Specify the distance function d used in Equation 15, the sampling distribution \\pi_t, and the score approximation network architecture and training procedure.", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)