DreamCS: Geometry-Aware Text-to-3D Generation with Unpaired 3D Reward Supervision
Authors: Xiandong Zou
Ruihao Xia
Hongsong Wang
Pan Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Preference Alignment in 3D
Preference Alignment
Human Preference Alignment
3D Generation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose DreamCS, a text-to-3D generation framework that aligns with human preferences using a 3D reward model trained on unpaired preference data.

Abstract: While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation — leading to geometric artifacts due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the first reward model trained directly on unpaired 3D-MeshPref data using a novel Cauchy-Schwarz divergence objective, enabling effective learning of human-aligned 3D geometric preferences without requiring paired comparisons. Building on this, we propose DreamCS, a unified framework that integrates RewardCS into text-to-3D pipelines — enhancing both implicit and explicit 3D generation with human preference feedback. Extensive experiments show DreamCS outperforms prior methods, producing 3D assets that are both geometrically faithful and human-preferred.

Published as a conference paper at ICLR 2026
DREAMCS: GEOMETRY-AWARE TEXT-TO-3D GENER- ATION WITH UNPAIRED 3D REWARD SUPERVISION
Xiandong Zou', Ruihao Xia², Hongsong Wang', Pan Zhou "Singapore Management University "East China University of Science and Technology
3 Southeeatt University
ABSTRACT
While text-to-3D generation has attracted growing interest, existing methods of- ten struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely hardly-collected preference-puiie -piired multi-view 2D images to train 2D reward models, when then guide 3D) generation leading geometric artifacts, such as the Janus face problem and geometric incompleteness, due to their inherent 2D bias. To ad- dress these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the first re- ward model trained directly on unpaired 3D-MeshPref data using a novel Cauchy- Schwarz divergence objective, enabling effective learning of human-aligned 3D geometric preferences without requiring paired comparisons. Building on this, we propose DreamCS, unified framework that integrates RewardCS into text-to- 3D pipelines enhancing both implicit and explicit 3D generation with human preference feedback. Extensive experiments show DreamCS outperforms prior methods, producing 3D assets that are geometrically faithful and preferede
INTRODUCTION
Text-to-3D generation has emerged as a key technique for automating 3D asset creation in do- mains such as gaming, film, digital comics, and virtual reality. Recent advances include one-stage optimization-base 2D lifting methods (Shi et al., 2023b; Wang et al., 2023b; Chen et al., 2023; Lin al.. 2023; Wang et al., 2023a; Liu et al., 2023a; Shi al., 2023a; Qian et al., 2023; Wu et al., 2024c; Zhu et al., 2023b; Kwak et al., 2024), two-stage approaches that separately model geometry and appearance (Lin al., 2023; Chen et al., 2023), and end-to-end methods (Tsalicoglou et al., 2024; Jun & Nichol, 2023). Despite these impressive advancements, generated 3D assets often mis- align with human preferences, highlighting the need for preference- generation frameworks.
To address this gap, reinforcement learning from human feedback (RLHF) (Christiano et al., 2017; Yang et al., 2024) has recently been incorporated into text-to-3D pipelines (Xie et al., 2024; Ye et al., 2024; Zhou et al., 2025), showing improvements in fidelity and realism. However, cur- rent approaches face two key challenges. First, they rely on costly paired preference-labeled 2D data: for each prompt, multiple 3D assets must be rendered from diverse viewpoints, and pre- ferred/dispreferred image pairs must be manually annotated. This process is computationally in- tensive, time-consuming and difficult to scale, especially since large preference gaps are rare in practice. Second, these methods provide only 2D view-dependent supervision. Rewards derived from rendered images (e.g., RewaeReward (Xu al., 2023)) may favor assets that appear plausi- ble from some viewpoints while ignoring structural flaws from others, leading to artifacts such as Janus faces or incomplete geometry (Fig. 1). The root cause lies in the absence of 3D-aware reward signals: existing diffusion- based 2D lifting pipelines excel at semantic alignment but lack explicit global geometric supervision, limiting their ability to ensure consistent and plausible 3D structure.
Contributions. In this work, we take a step forward by proposing novel 3D-reward guided frame- work for text-to-3D generation. To eliminate paired training data and provide geometry-leve feed- back during generation, we respectively resolve three associated key challenges by proposing the
"Correspondngg author.

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Published as a conference paper at ICLR 2026
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Figure Comparison of 2D- vs. 3D-based reward models. 2D reward model ImageRe- ward (Xu et al., 2023) assigns high scores to geometrically flawed 3D assets, while our 3D reward model RewardCS better aligns with human preference. b) while DreamFusion (Poole et al., 2022) guided by 2D Reward3D (Ye et al., 2024) produces 3D assets with geometric defects and the Janus problem, while DreamFusion with our RewardCS yields geometrically consistent 3D content.
first large-scale preference-unpaired 3D dataset, the first 3D reward model trained on our dataset with theoretical guarantees, and the first 3D-reward guided text-to-3D generative framework.
First, collecting paired preference-labeled 3D data is prohibitively expensive due complex gen- eration and annotation. To overcome this, we build 3D-MeshPref, the first large-scale preference- unpaired 3D mesh dataset with over 30,000 samples, each containing text prompt, 3D asset, and preference reward score. We curate meshes from Cap3D (Luo et al., 2023), evaluate them with Llaha-Mesh (Wang et al., 2024) on geometric fidelity, semantic alignment, and structural plau- sibility, and refine scores with human verification. Assets with higher rewards are designated as preferred, while lower ones serve as dispreferred examples.
Second, prior preference alignment frameworks (Ouyang et al., 2022; Yang et al., 2024; Ye et al., 2024; Zhou et al., 2025) rely on paired comparisons and thus cannot operate in our unpaired set- tings. Accordingly. on unpaired data. To enable this, we propose a distribution-level training objective based on Cauchy-Schwart (CS) divergence (Jenssen et al., 2006), treating preferred and dispreferred assets as samples from two distributions. Optimizing their CS divergence encourages higher rewards for geo- metrically and semantically superior assets. We also prove the equivalence between CS-divergence learning on unpaired data and vanilla paired preference supervision, providing theoretical guarantee.
introduce RewardCS, the first 3D geometry-aware reward model trained
Finally, existing text-to-31) frameworks lack native leometry-level feedback and face challenges in mesh representation. differentiability. and reward compatibility. So we develop Dream the first 3D-reward guided text-to-3D framework. DreamCS integrates RewardCS into both implicit and explicit pipelines through three innovations: (I) differentiable meshization for end-to-end gradient flow, (2) adaptive mesh fusion for reward compatibility without sacrificing detail, and (3) progressive reward guidance balancing coarse structure refinement with fine-graines optimization.
Extensive experiments on the GPTEva13D benchmark (Wu et al., 2024a) show that DreamCS con- sistently improves geometric alignment and mesh quality for one-stage and two-stage pipelines like MVDream (Shi et al., 2023b), DreamFusion (Poole et al., 2022), and Magic3D (Lin et al., 2023). Moreover, our 3D reward guidance complements 2D-based methods, and can yield further gains.
RELATED WORK
Text-to-3D Generation. Recent advances in text-to-3D generation have largely been driven by the use of pretrained 2D diffusion models, given the scarcity and limited diversity of high-quality 3D datasets. A common strategy in works like DreamFusion (Poole et al., 2022) and SJC (Wang et al., 2023a) involves using Score Distillation Sampling (SDS) to optimize 3D representations like NeRF (Mildenhall et al., 2021) by using gradients from a 2D diffusion model's 's denoising process as supervision to refine the 3D scene. Unfortunately, SDS suffers from issues like view inconsis- tency, the Janus problem, and poor structural fidelity (Wang et al., 2023b). To mitigate these, new methods adopt either one-stage pipelines (Shi et al., 2023b; Zhu et al., 2023b; Wang et al., 2023a; Wu et al., 2024c) that jointly optimize geometry and uppearance, 2023; Chen et al., 2023; Wang et al., 2023b) that decouple them. However, most of them still strug- gle with alignment human preferences, particularly terms of global 3D geometry.
two-stage pipelines (Lin et al.,
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Learning from Human Feedback. Human feedback is increasingly vital for aligning generative models with user intent. In language, RLHF (Ouyang et al., 2022) and DPO (Rafailov et al., 2023) have successfully aligned LLMs to human values, and similar strategies have extended to images (Xu et al., 2023; Liang et al., 2024) and videos (Wu et al., 2024b; Liu et al., 2025a). In 3D generation, however, feedback has only recently been explored. DreamReward (Ye et al., 2024) trains a multi- view image-based reward model from annotated renderings, while DreamDPO (Zhou et al., 2025) applies DPO using human preferences from multi-view comparisons. Yet both approaches remain constrained to 2D supervision, aligning view-specific appearance rather than global 3D structure and often yielding inconsistent geometry. To address this gap, we introduce RewardCS, 3D reward model for direct geometrie supervision, and further design DreamCS to integrate this feedback into optimization, ensuring more consistent, plausible, and human-aligned 3D content.
METHODOLOGY
We first construct laage-scale preference-uppairee 3D mesh dataset, 3D-MeshPref, in Section 3. 1. Building on this, we develop the first 3D HDD-gemmetry-awain reward model, RewardCS, trained on the ID-MeshPrel dataset, detailed in Section 3.2. To effectively integrate the 3D reward model, Re- wardCS, into text-to-3D generation pipelines, we propose Dream in Section 3.3 - a framework that aligns generated 3D content with human preferences through geometry-awand 3D supervision.
3.1 3D-MESHPREF: LARGE-SCALE
CEFFERENCE-UNPAIEEE 3D MESH DATASET
major challenge in 3D preference alignment the lack of labeled datasets, as generating and an- notating high-quality 3D assets is resource-intenste resentations hinders the construction of consistent paired preference data, limiting the development of effective 3D reward models. To overcome this, we propose 3D-MeshPref, the first large-scale preference-unpaired 3D mesh dataset. It includes 30,000+ samples, each with text prompt, 3D asset, and a preference reward score. Our pipeline combines automated LLM-basee scoring with human refinement, enabling scalable annotation while preserving alignment with human judgment.
Moreover, the complexity of explicit 3D rep-
We build our dataset from annotated point clouds in Cap3D (Luo et al., 2023), sourced from 3D datasets like Objaverse (Deitke et al., 2023) and ABO (Collins et al.,
15000
Mesb
Beeand Margin (3.3, 4.10)
12000
2022). ABO contains high-resolution 3D household ob- jects across 63 categories; Objaverse offers large-scale and semantically rich 3D collection of over 21,000 cat- egories. For diversity, we respectively collect 8,000 and 15,000 samples from ABO and Objaverse, which are then converted into high-quality mesh representations using Me- shAnything (Chen et al., 2024), which consistently out- performs other meshilization methods in both mesh quality and computational efficiency. To ensure mesh quality and compatibility with our 3D reward model, we apply QEM mesh simplification algorithm (Wu et al., 2004) to ensure the output with maximum of 16,384 triangular faces. After filtering for mesh quality and completeness, we obtain 20,000+ 3D meshes. To adapt the 3D reward model to interme- diate assets generated during SDS optimization, we augment the dataset with over 10,000+ meshes optimized using SDS- based methods, such as DreamFusion (Poole et al., 2022) and MVDream (Shi al., 2023b), under text prompts in Objaverse. These meshes are sampled at early stages of the optimization process. (see Appendix B) This data augmentation exposes the reward model to both early- and late-stage geometries, improving robustness and generalization across the optimization.
Count
9000
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3000
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Figure 2: Annotated score distribu- tion of meshes in 3D-MeshPref.
We use Llama-Mesh (Wang et al., 2024), a capable open-source LLM fine-tuned for 3D evaluation, to score each mesh on a 0-5 Likert scale (Joshi et al., 2015) based on prompt alignment, structural realism, and visual fidelity. We observe that LLM-based ratings tend to overestimate quality relative to human perception. Accordingly, human raters verify and re-evaluate them. See annotation guide- lines and human refinement process in Appendix E. We then partition the dataset using a reward threshold (3.5-4.0): meshes scoring > 4.0 show strong geometric integrity and semantic fidelity and are labeled as preferred (47% of all 3D asserts), while those <3.5 typically indicate structural defects or poor alignment with the prompt and are dispreferred (53%). This clear margin excludes ambiguous cases, ensuring reliable partitioning a distribution visualized in Fig. 2. This separation provides a strong supervisory signal for learning to distinguish relative quality in 3D meshes, and also provides the training of RewardCS balanced preference pairs- that is, each training instance
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3D-MeshPref
Class Tokiena Neeh Emboediing
Encoder To,
RewardCS
Token
Proferred Samplea
Dispraferred Samplea!
Fuskon Embbetting
Projection Head
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Figure 3: RewardCS is trained on 3D-MeshPref using Schwarz objective.
contains one preferred and one dispreferred mesh sample, regardless of their score values, en- suring that the learning signal remains unbiased toward either class and avoids reward skewing due class imbalance. We provide examples and detailed rationale for 3D-MeshPref in Appendix B.
3.2 REWARDCS: 3D GEOMETRY-AWABE REWARD MODEL
Learning reward models for 3D assets is crucial for guiding high-quality text-to-3D generation (Xie et al., 2024; Ye et al., 2024), but it is hindered by the scarcity of paired preference annotations, which are costly and hard to scale due to 3D complexity. Prior preference alignment methods - both general (e.g.. DPO (Rafailov et al., 2023) and its variants (Meng et al., 2024; Lai et al., 2024)) and 3D-specific (Zhou et al., 2025; Ye et al., 2024) - rely on paired data of the form {m, m c; /i:1 c where each tuple includes a prompt c, drawn from prompt distribution pc, and pair of 3D meshes: a preferred one m. and a dispreferred one m, These samples are drawn from prompt-dependem. distributions: m ~ (=(()))) and m ~ p (+(c)). The goal is to train reward models to assign higher rewards to preferred assets and guide text-to-3D) generative models. However, this paradigm fails when such paired data are unavailable, which is common in real-world 3D settings.
3.2.1 REWARDCS: A NOVEL FRAMEWORK FOR UNPAIRED 3D PREFERENCE LEARNING
To overcome this limitation, we introduce RewardCS, the first geometry-ave aware 3D reward model that learns from unpaired preference data. Rather than relying on explicit preferred dispreferred pairs, RewardCS leverages distributional training objective based on Cauchy-Schwarr (CS) di- vergence (Jenssen et al., 2006), enabling effective preference modeling at scale. Our method built upon the newly introduced 3D-MeshPref dataset, which contains only unpaired samples: high- quality (preferred) and low-quality (dispreferred) 3D meshes associated with diverse prompts.
Let {n, )m p+ (-lc; and {n, 1リ= ~ p (-le;) denote two sets of unpaired preferred and dispre- ferred 3D meshes, each conditioned independently sampled prompts {c J and {c,} from a distribution Pc- Standard preference alignment methods (Ouyang et al., 2022; Christiano et al., 2017; Ye et al., 2024) are inapplicable in this unpaired setting because they require joint comparisons across samples from the same prompt. Instead, we formulate preference learning as a distribution matching problem. We define 3D reward model as function re . + IR, parameterized by O, which maps 3D mesh m E .M and the text condition с E C to a scalar reward. Our reward model, RewardCS, consists of an encoder re, that transforms the input pair into a unified embedding E Z and a projection head to generate a scalar reward. Crucially, the model is designed to learn the reward conditioned on the text prompt (see Section 3.2.2). To train the 3D reward model, we encode unpaired preferred and dispreferred samples into latent embeddings via the encoder ro.:
) re. (n, c.) ~ p(x), {y = re. (nj ,c, ~ p(y).
(1)
We treat these embeddings as samples from two distinct distributions p(x) and p(y), representing the overall structure of high- and low-quality 3D meshes the latent space. Then rather than comparing individual mesh pairs, we aim to separate the distributions p(x) and p(y) in the embedding space. It allows the reward model to generalize over the global statistical properties of high- and low-quality 3D assets, enabling robust learning even when direct pairwise supervision is unavailable. To this end, we introduce a distribution-lewe loss based on CS divergence (Yin et al., 2025), which quantifies the dissimilarity between two probability density functions p and q over the support w:
Dcs(p lI q) -log (/ p(w))(w)) du) )u)² (ʃ p(w)2 2 du [ q(w)² dw) ((w))dw))
(frlw)
Compared to the Dallback-Leibler divergence (Van Erven & Harremos, 2014), the CS divergence offers a tighter generalization bound (Yin et al., 2024) and is more robust than divergence, which lacks closed-form for Gaussians (Fuglede & Topsoe, 2004; Nielsen, 2019).
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15
15
Dispreferred
Dispreferred
10
Preferred
10
Preferred
5
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-10
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(a) RewardCS trained using Eq. (4) with = 0.
(b) RewardCS trained using Eq. (4) with == 1.
Figure 4: t-SNE visualizations of class token embeddings based on different RewardCS in 3D- MeshPref data. The addition Laiv enables RewardCS to separate the underlying data distributions of preferred and dispreferred mesh samples (p(x) and p(y)) in the learned embedding space.
This makes CS divergence well-suited for unpaired 3D reward learning, as maximizing helps the model capture semantic and geometric cues that differentiate preferred from dispreferred assets without explicit supervision for each prompt.
Estimation of CS-Divergence. Since true data distribution densities p(x) and ply) are unknown, estimate them via kernel density estimation (KDE) (Weglarczyk, 2018). Let {x;} and {y,}ī=ı denote the embeddings of preferred and dispreferred samples. The empirical CS divergence is:
Des (x(x) p())) = log
x(xx,x;)) + +og
m m(y..,))
21og
ΣΣ M(x6,))) (3)
m
cJ=1
2 1=1==1
where k(::)) is a kernel function. This estimator possesses desirable properties: it is symmetric and differentiable. Importantly, supports unequal numbers preferred and dispreferred unpaired samples (m & rt), enabling distributional alignment without requiring pairwise annotations.
Overall Training Objective RewardCS. train our RewardCS model re. we combine a re- gression loss with the CS divergence loss. The final training objective is given by:
LRewantcs (0) LMSB(0) + XCaiv (0),
(4)
where LMSE is the mean squared error for reward prediction, Laiv (xC(((()):(()) encourages separation between preferred and dispreferred mesh embeddings, and a hyperparameter.
Fig. 4 shows 2D t-SNE visualizations of mesh embeddings from RewardCS trained with À = 0 and 1. Adding Laiv helps the reward model distinguishing preferred and dispreferred meshes in the embedding space, and enables RewardCS to align with human preferences from unpaired data. To further validate the CS divergence objective, we conduct an ablation study to examine its impact on clustering embedding quality and downstream performance (see Appendix C.2).
Theoretical Justification. We analyze CS divergence for training the 3D reward model on unpaired preference data by demonstrating its asymptotic equivalence to paired preference supervision. Op- timizing the CS divergence equivalent to optimizing a quantity in a kemel feature space (Jenssen al., 2006; Yin et al., 2025):
Dcs(p(x)) p(y)) log || [Alllll
with
P..
Σ x(x)):
m
My 1
n ply.). (5)
where a characteristic kernel s(x,y) ) is defined as x(x, (x(x), (oy)) and maps samples to a Reproducing Kernel Hilbert Space (RKHS) H. (Smola et al., 2007). our case, we need to compute the means
paired Mr p(((m]) ,c)) My paired -(((mm,c))}m))
(6)
ampaired
p(((ntt o.)) py P
umpaireed p(((n) (
where p({(n (m; +)))) = Σ x (ro. (m;,c:) Then we can define the CS divergence loss on unpaired data and paired data:
parred
(1 umpaired unpaired
i paired
-22 log
D Des
umpaired
-22 log
1u umpaired ||
(7)
pairod
paired
paired
| !
unpaired
H pz
Then we pose necessary assumptions widely used in RLHF and divergence analysis (Zhu et al., 2023a; Zou et al., 2025; Kim et al., 2021).
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Multi-view Images
DreamCS
Training Objective
Differentiable Randering
Add Noise
SDS Loss Csss (1)
Mulliview Diffusion
Promoet Sand hourglass sanid glirners like
3D Mesh d(())
Deep Marching
3D Guidance
Tetrahedra
Reward
RewardCS To
To ro(d())).c)
Figure 5: Framework of DreamCS: integrate RewardCS into the SDS model for NeRF optimization.
k(x, y) S K, and characteristic, meaning the kernel mean embedding is injective. b) The population mean embeddings of the pre- ferred and dispreferred samples satisfy: ||
Assumption 1. a) The kernel -) is bounded, i.e. supx.y
paired]]]] ||x, |1 umpaired H4 Ippaared ||7,, [1paaprardddd || >> 0.
Assumption (a) and (b) are standard assumptions in kernel methods (Hofmann et al., 2008; Gretton et al., 2006; Muandet et al., 2017), and hold in our case. We the Gaussian kernel No (x, exp(-l|x - yll//(20)) which is bounded and characteristic, and our data distributions are well- separated, satisfying the non-zero mean embedding condition.
Theorem (Asymptotic Equivalence). Suppose Assumption holds. With a constant C > 0, the empirical CS divergences Dpaired and Dumpaired computed from paired and unpaired data satisfy:
Dpaired umpparrdd
< C ((c.(2to) +
2,0 as Vn
(8)
See proof in Appendix A. Thm. shows that the CS divergence computed from paired and un- paired preference data converges asymptotically RewardCS can learn a latent embedding z such that maximizing CS divergence between embeddings of preferred and dispreferred samples induces reward separation even without paired data.
convergence rate of O ( + ) Thus,
3.2.2 NETWORK ARCIITECCURE OF REWARDCS
As shown in Fig. 3. RewardCS contains two key components: an encoder ra, to produce a unified representations of 3D geometry and text, and prediction head to generate scalar reward score. The encoder re, integrates three modules: 3D mesh encoder for geometric feature extraction, text encoder for prompt representation, and cross-modal fusion module to integrate the two. 3D mesh encoder uses MeshMAE (Liang al., 2022), masked Vision Transformer (ViT) autoencoder for 3D meshes, which shows strong performance in capturing global geometric structure and its robustness partial inputs and structural noise. Each input mesh is divided into 256 non-overlapping patches, with each patch consisting of 64 triangular faces. Each face is represented by 10-dimensional feature vector capturing geometric attributes like area, angles, surface normals. This strategy en- ables 3D mesh encoder to capture both localized surface details and global geometric context. For text encoder, it adopts MeshCLIP (Song et al., 2023) to map input text prompt into sequence of semantic embeddings. To fuse mesh and text information, mesh tokens with learnable 128-dimensional class token act as queries, while text tokens serve as keys and values. During training, the pretrained text encoder is frozen. This design ensures that semantic information from the text condition is injected directly into the mesh representation, so that the resulting embeddings contextually modulated by the prompt. The class token, which captures the global fused context, then passed to an MLP-based projection head to produce the final reward score. More rationale of the network architecture is in Appendix C.I.
apply cross-attention mechanism:
3.3 DREAMCS: 3D REWARD GUIDANCE FRAMEWORK
Integrating 3D reward model into text-to-3D frameworks presents two key challenges. Firstly, isting reward methods are designed for 2D paired preferences and cannot directly handle preference- unpaired explicit 3D mesh representations, requiring differentiable mechanism to convert implicit

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fields into mesh form. Secondly, meshes generated during training may not meet the reward model's structural requirements due to face count constraints, necessitating adaptive topology aligmment. To address these, we propose DreamCS, the first geometry- aawre 3D reward guidance framework, in- tegrating our RewardCS model into existing text-to-3D pipelines. As shown in Fig. 5, DreamCS enables end-to-end optimization via: (1) differentiable meshization module for reward supervi- sion, (2) an adaptive mesh fusion algorithm for topology alignment, and (3) a progressive reward guidance scheme balancing exploration and reward optimization.
Differentiable Meshization, Text-to-3D models often operate on implicit representations such as NeRF or SDFs, which must be converted into explicit mesh geometry to be compatible with our RewardCS model. We introduce a differentiable meshization module that enables smooth con- version from these fields to triangle meshes without breaking the optimization flow. For SDS pipelines (Poole et al., 2022), we use DMTet (Shen et al., 2021) to extract high-quality isosur- faces differentially. This preserves the gradient flow from the reward signal back to the implicit 31) parameters. Compared to Marching Cubes, DMTet enables high-fidelity geometry extraction. For pipelines that optimize explicit meshes, this conversion step is bypassed.
Adaptive Mesh Fusion. Meshes produced above are often structurally incompatible with the in- put requirements of our reward model, RewardCS which expects meshes partitioned into 256 non- overlapping patches, each with 64 faces. To address this, we propose an adaptive mesh fusion algorithm that simplifies and reorganizes mesh topology while preserving geometric fidelity.
The algorithm iteratively merges adjacent faces using two criteria: (1) similarity in face normals and (2) topological adjacency (shared vertex). When two faces share vertex and exhibit high normal similarity, we construct a new face using the shared vertex and one additional vertex from each original face. When two faces share an edge and have highly aligned normals, new face is formed using the edge and a third vertex from either face. This fusion process reduces mesh complexity while maintaining fine structural details. Importantly, the entire fusion algorithm is differentiable and integrated into the training loop, maintaining gradient flow and supporting end-to-end learning. We validate our proposed adaptive mesh fusion method on mesh geometric fidelity in Appendix D.2.
Progressive Reward Guidance. To ensure faithful 3D generation, DreamCS integrates RewardCS through progressive guidance. At each optimization step. the current differentiable mesh produced via our differentiable meshization module is evaluated by the RewardCS model rg. which provides gradient-based signal reflecting its geometric plausibility and alignment with input prompt.
Let the 3D generation be parameterized by an implicit representation t/. At each optimization step 1, the mesh d( derived from 1/, via DMTet is evaluated by the reward model re to assign scalar score that reflects mesh's 's quality w.r.t. the input prompt c, which provides a gradient guidance signal based the geometry of mesh vertices and flows back to the implicit field via mesh structure. Because RewardCS guidance derives from the high-level geometric properties of the mesh, it acts as structural prior that promotes global geometric coherence and semantic alignment.
The optimization objective at optimization step is defined as:
((1))) LsDs (1h,) o(t) -re (d(1))))c)
where LsDS is vanilla score distillation loss (Poole et al., 2022), and a(I) is weighting function.
To balance the SDS loss and the reward guidance, we adopt a progressive reward guidance sched- ule (Ye et al., 2024; Liu et al., 2025b). At the beginning of optimization, is kept low so that the process driven primarily by the SDS loss, enabling broad exploration of shape space. As optimization progresses, increases linearly from Omin to O'ax:
(10) This schedule delays strong reward influence until the geometry stabilizes, balancing the SDS loss and the reward guidance and promoting coarse-to-fine refinement.
att Oman + (Amax - amin) 7.
4 EXPERIMENT
Setup. We test our DreamCS by integrating our reward model RewardCS into one-stage SDS-based text-to-3D generative models like MVDream (Shi et al., 2023b) and DreamFusion (Poole et al., 2022), and two-stage models like Magic3D (Lin et al., 2023) and Fantasia3D (Chen et al., 2023). Moreover, we compare with other 3D preference alignment approaches. DreamReward (Ye et al., 2024), and DreamDPO (Zhou al., 2025). All baselines use their official implementations where
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available, otherwise Threestudio's 's implementation (Liu al., 2023b). Our DreamCS is initialized by pretrained ShapeNet (Chang et al., 2015) and fine-tuned on 3D-MeshPref by minimizing loss in Eq. (4). All methods run for 20,000 steps, with additional 10,000 steps for refinement in eextre/geomeery-avalt
baselines. Rendering resolution follows a coarse-to-fing schedule (64 x 64 for the first 5,000 steps, then 256 256), O'min 10 and Omax 20. We show that the trained MeshMAE and RewardCS can achieve strong alignment with the performance of LLaMA-Mesh, showing superiority in generalization and robustness (see details in Appendix C.3).
We use all 110 prompts in GPTEva13D (Wu et al., 2024a) for evaluation. We assess texture and geometry quality using CLIP (CP) (Radford et al., 2021) and VisionReward (VR) (Xu et al., 2024) on rendered 2D images. These image-text similarity metrics are computed by averaging the similarities between each view and the given prompt. We proposed a 3D Geometry-Asset Alignment Reward (GA) metric based on RewardCS trained with different training configuration. It serves as an independent evaluator for 3D geometry cenntttr--tetteeee alignment. MiniCPM-o (Yao et al., 2024), a multimodal vision vision-language model, rates text-asset alignment, 3D plausibility, and geometry on a 0-5 Likert scale. In addition, a user study with 30 participants on 60 prompts assesses these criteria. Finally, we report the proportion of 3D assets suffering from Janus artifacts (Proportion) by conducting an user study with 60 participants. See more details in Appendix E.
consistency
Table 1: Comparison of baselines using 1 11 GPTEval 3D prompts under 12 multi-view images. Metrics: CP = CLIP, VR = VisionReward, GA = 3D Geometry-Asset Alignment Reward.
Method
CP
VR
GA
Method
CP
VR 1
GA
DreamFusion
0.22
-3.21
2.53
Magic3D Stagel
0.20
-3,26
2.48
+Reward3D
0.23
-3.11
2.77
+Magic3D Slage2
0.21
-3.03
2.65
+DreamDPO
0.23
-2.98
2.79
Magic3D Stagel +DreamDOO
0.22
-3.05
2.56
+RewardCS
0.25
-2.11
2.96
+Magic3D Stage2=DreamDPO
0.23
-2.94
2.74
+RewandSD-RewanICS
0,25
-277
3.22
Magic3D Stagel +Rewand3D
0.23
-3.09
2.63
MVDream
0.24
-3.31
2.79
+Magic3D Stage2-Reward3D
0.23
-3.32
2.55
+Reward3D
0.27
-3.12
2.87
Magic3D StageI+Rewarde +RewardCS
0.23
-1.90
3.00
+PreamDPO
0.23
-2.13
2.87
+ +Magie3D Stage2+Rewandor
0.24
-1.58
3.22
RewardC'S
0,29
-2.11
2.96
Fantasia3D
0.22
-3.03
2.95
+Rewand1D-RewanICS
0.29
-1.92
3.08
+RewardCS
0.25
-1.01
3.33
Quantitative Comparison. Tab. presents a comparative analysis of one-stage (DreamFusion, MVDream) and two-stage (Magic3D, Fantasia3D) (Massse)..aattsiaS))) pipelines, along with their respective 2D-guided (Reward3ID, DreamDPO) and our proposed 3D-guided (RewardCS) variants. As Reward3D (Ye et al., 2024) is finetuned to optimize ImageReward score, methods with Reward31) guidance often overfit the ImageReward (IR) metric, but do not generate high-quality 3D assets as shown in Fig. 6.
Three key findings emerge from these results. 1) RewardCSS consistently improves upon all vanilla baselines and outperforms 2D-guided variants in CP, GA and VR scores. In Magic3D, RewardCS achieves the highest GA (3.22), significantly outperforming the baseline (2.65) and the variant with 2D guidance (2.55). while achieving the highest CP score (0.24) and VR score (-1.58). Similarly, in DreamFusion, RewardCS leads with a GA score of 2.96, surpassing both bascline (2.53) and the variant with 2D guidance (2.79), and the best CP (0.25) and VR score (-2.11). 2) RewardCS exhibits strong compatibility with diverse text-to-3D backbones, including both implicit 3D repre- sentation optimization and explicit mesh-based rendering pipelines. In MVDream, it improves the vanilla baseline from -3.31 to -2.1 11 in VR score and from 2.79 to 2.96 in GA score, outperforming the Reward3D variant (-3.12 VR. 2.87 GA), Furthermore, in Fantasia3D's s mesh-rendering-baie pipeline, RewardCS achieves the highest CP and GA score and significantly improves the VR from -3.03 to -1.11. 3) 3D and 2D guidance prove complementary: integrated guidance in DreamFusion achieves the peak GA (3.22) score while improving VR to -2.77, demonstrating that Reward3D en- hances vuew consistency while RewardCS optimizes geometric alignment. discussion on inference computational cost is provided in Appendix F.4. Extended results comparison with 3D controllable generation methods (Li et al., 2023; Huang et al., 2024) and advanced 3D generative backbones (Sun et al., 2023; Xiang al., 2025) provided in Appendix F.I and F.2.
Visualization Comparison. Fig. compares visual generation results across methods. Vanilla base- lines such frequently exhibit textural misalignment and the Janus face problem, as exemplified in Fantasia3D's example, where the baseline produces a two-headed geometry. Baselines augmented with 2D guidance via Reward3I DPe improve aesthetics but still display artifacts like ge-
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DreamPusion(DP) DF-Roward3D
+RewardCS
DF+DreamDPO
+RewardCS
DF+RewadCCS
An assortment of vinttage fragrant
vinttagefragrrant p perfames on display
MVDream(MM)
MV+Reward3D
*RewardCS
MV-DreamDPO
"RewardCS
MV-Rawdd
A thorry rose
Magic3DDStngeel
Magle3DDStagee2
Magic31) -Reward3D
Magic3D +DrcamDPO
Mtgae3D-Stagef
RRwardCC
Mugic3D-Stage2
RewardCS
Tockue
Toxtui
A víntage table lamp casting soft, warm glow in quist, dark room
Funtasin3D-Stuap
Fantasia3D
Fantasia3D- Stagol- Fantasia3l-SSan Stagenttttte-tttt R-RwardSS
Textene
TENAR
A lone, ancient tree stands the middle of full
Figure 6: Comparisons with I-stage generation pipelines (DreamFusion and MVDream) and 2-stage generation pipelines (Magic3D and Fantasia3D). More visualizations are provided in Appendix F.3.
Table Comparison of methods on MiniCPM-o evaluation (left) and user study (right) using (a) 110 and (b) 30 representative GPTEval3D prompts, respectively. Metrics: T-A Text-Asset Alignment,
3DP = 3D Plausibilityy, G-T Geometry- Texture Alignment.
(a) MiniCPM-o evaluation on 110 prompts
(b) User study on 60 prompts
Method
T-A 1
3DP
G-T
Method
T-A 1
3DP 1
G-T
MVdream
2.97
3.12
3.08
MVdream
2.90
2.87
2.91
+Rewand3D
3.38
3.35
3,22
+RewarD3D
3.04
3.15
3.07
+PreamDPO
3.30
DreemmDPO
3.15
3,00
+RewardC'S
3,59
4.05
3.95
+RewardCSS
3.21
3.72
3,59
+Rewand3D+RewanICS
3.61
3.98
3.81
+RewandDD+RewanIC
3.41
3.51
3.54
Table 3: Proportion of 3D assets suffering Janus artifacts using 60 GPTEval3D prompts.
Evaluation MVDream
(b) Evaluation on DreamFusion
Method
Proportion
Method
Proportion
MVDream
0.52
DreamFusion
+Reward3D
0.44
+Reward3D
0.53
+DreamDPO
0.43
+DreamDPO
0.50
+RewardCS
0.30
+RewardCSS
0.41
+Reward3D+Roward
0.28
+Rcward3DDRewaarCSS
0,39
ometric incompleteness and floaters, as seen in DreamFusion, Magic3D and MVDream's 's examples. In contrast, DreamCS framework, which combines baseline with RewardCS, demonstrates robust compatibility across all 3D generation backbones, consistently producing assets with supe- rior text alignment, geometric completeness, and texture fidelity. Moreover, RewardCS is also orthogonal to 2D guidance like Reward3D to further enhance text-to-3D generation.
Evaluation via MiniCPM-o and Human. Tab. 2 (a) reports evaluation results using MiniCPM- o, and Tab. (b) reports findings from a user study. One can observe that RewardCS guidance outperforms 2D preference-based methods geometry-related criteria while maintaining compet-

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Published as a conference paper at ICLR 2026
Dynamic
ado-55 008ST
sde15 68082
A OSLRRphott of 6lue jay standing large basket of rainbow macarons
Figure 7: Ablation study on the weight of 3D guidance a in MVDream at 15000 and 20000 steps.
itive performance in text-asset alignment. In MiniCPM-o evaluations, MVDream with RewardCS achieves the highest scores 3D Plausibility (4.05) and G-T Alignment (3.95), significantly exceed- ing both MVDream and MVDream with Reward3D.. User studies confirm this trend, with RewardCS again leading in 3D Plausibility (3.72) and G-T Alignment (3.59).
Evaluation via Janus-Artifact Artifact Proportion. Regarding to the Janus problem, to the best of our knowledge, no established benchmark or quantitative metric currently exists for evaluating multi- view consistency the Janus problem in text-to-3D generation. Consequently,
compare text-to- 3D baselines and DreamCS using the proportion of generated 3D assets exhibiting Janus artifacts. As shown in Tab. 3, both MVDream and DreamFusion with RewardCS yields lower rate of Janus artifacts (0.30 and 0.41) compared to both the vanilla baseline and 21)guided variants.
Ablation Study. Fig. 7 shows the effect of varying guidance weight in Eq. (9), balancing the 2D diffusion distillation score and DreamCS 3D guidance, on the quality of generated 3D assets using the MVDream backbone. Low values of a0, 10) cause geometric ambiguity, while high a550, 100) results in structural distortions. Our progressive strategy yields can improved results.
5 CONCLUSION
In this paper, we present DreamCS, the first 3D-guided text-to-3D generation framework explicitly designed for human preference alignment. Specifically, we construct preference dataset on mesh data, 3D-MeshPref. Then we train a 3D reward model, RewardCS, on this dataset to encode human- aligned geometric priors. By incorporating RewardCS into the 3D generation pipeline, DreamCS produces geometrically plausible and texture-consistent 3D) assets that are faithfully aligned with human preferences. Extensive experiments demonstrate that DreamCS consistently outperforms both vanilla and 2D-guided baselines across 3D geometry-oriinted metrics and human evaluations.
Limitations. Due to limited budget, our evaluation of 3D generation relies on open-source LLMs like MiniCPM-o. Incorporating stronger models like GPT-4 (Wu et al., 2024a) may yield more reli- able and fine-grained feedback, and will be considered when budget becomes available. In addition, the current absence of established benchmarks for assessing multi-view consistency and Janus arti- facts limits the comprehensiveness of our evaluation. Developing standardized metrics related to the Janus problem would enable more robust comparisons in future work. Moreover, while DreamCS shows promising results, its performance limited by text-to-3D generative backbone models like DreamFusion. In future work, we plan to explore advanced text-to-3D backbones.
DECLARATION OF LLM USAGE
We Llama-Mesh score each mesh in our 3D mesh dataset. Additionally, employ MiniCPM-o to conduct the MLLM evaluation. All research ideas, methods, and experimental results presented are original contributions of the authors.
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ACKNOWLEDGEMENTS
This work was supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant (Proposal ID: 23-SIS-SMU-0770) the National Natural Science Foundation of China (62302093), and Jiangsu Province Natural Science Fund (BK20230833), Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Ministry of Education, Singapore.
ETHICS STATEMENT
The text-to-3D base models employed in this research may reflect biases or generate sensitive or potentially offensive 3D content, intended solely for academic and scientific purposes. The opinions expressed within generated outputs do not represent the views the authors. We remain commit- ted to fostering the development of Al technologies which align with ethical standards and reflect societal values.
REPRODUCIBILITY STATEMENT
We detail our work in the Methodology section (Section 3) and describe implementation details in Section and Appendix E.
REFERENCES
Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cláudio Silva, and Gabriel Taubin. The ball-pivoting algorithm for surface reconstruction. IEEE transactions on visualization and com- puter graphics, 5(4):349-35, 2002.
Tadeusz Calinski and Jerzy Harabasz. A dendrite method for cluster analysis. Communications in Statistics-theon and Methods, 3(1):1-27, 1974.
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, al. Shapenet: An information-rich 3d model repository. arXiv preprint
2015.
Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high high-quality text-to-3d content creation. In Proceedings of the IEEE/CVF inter- national conference on computer vision, pp. 19246-22256 2023.
Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, and Gu- osheng Lin. Meshanything v2: Artist-created mesh generation with adjacent mesh tokenization. arXiv preprint arXiv: arXiv:2408.02555, 2024.
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing sys- tems, 30, 2017.
Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F Yago Vicente, Thomas Dideriksen, Himanshu Arora, et al. Abo: Dataset and benchmarks for real-world 3d object understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 21 21126-20 21 1136, 2022.
David L Davies and Donald W Bouldin. cluster separationn measure. IEEE transactions on pattern analysis and machine intelligence, (2):224-227, 2009.
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of anno- tated 3d objects. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 131 13142-13153, 2023.

---

Published as a conference paper at ICLR 2026
Herbert Edelsbrunner and Ernst P Mücke. Three-dimensional alpha shapes. ACM Transactions On Graphics (TOG), 13(1):43-7,, 1994.
Bent Fuglede and Flemming Topsoe. Jensen-shannon divergence and hilbert space embedding. In International symposium onInformation theory, 2004. ISIT 2004. Proceedings., pp. 31. IEEE, 2004.
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex Smola. A kernel method for the two-sample-problene Advances in neural information processing systems, 19, 2006.
Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola. Kernel methods in machine learn- ing. 2008.
Tianyu Huang, Yihan Zeng, Zhilu Zhang. Wan Xu, Hang Xu, Songcen Xu, Rynson WH Lau, and Wangmeng Zuo. Dreamcontrol: Control-based text-to-3d generation with 3d self-prior. In Pro- ceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 5364- 5373, 2024.
Robert Jenssen, Jose C Principe, Deniz Erdogmus, and Torbjørn Eltoft. The cauchy-schwarz diver- gence and parzen windowing: Connections to graph theory and mercer kernels. Journal of the Franklin Institute, 343(6):614-62)) 2006.
Ankur Joshi, Saket Kale, Satish Chandel, and D Kumar Pal. Likert scale: Explored and explained. British journal of applied science & technology, 7(4):396, 2015.
Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023.
Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Pro- ceedings of the fourth Eurographics symposium Geometry processing, volume 7, 2006.
Tachyeon Kim, Jachoon Oh, Nak Kim, Sangwook Cho, and Se- Se-Young Yun. Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation. arXiv preprint arXiv:2105.089/9, 2021.
Min-Seop Kwak, Donghoon Ahn, Inès Hyeonsu Kim, Jin-Hwa Kim, and Seungryong Kim. Geometry-aware score distillation via 3d consistent noising and gradient consistency modeling. arXiv preprint arXiv:2406. 200.446666665, 2024.
Xin Lai, Zhuotao Tian, Yukang Chen, Senqiao Yang, Xiangru Peng. and Jiaya Jia. Step-dpo: Step- wise preference optimization for long-chain reasoning 1lms. arXiv preprint arXiv:2406. 2024.
Zhiqi Li, Yiming Chen, Lingzhe Zhao, and Peidong Liu. Mvcontrol: Adding conditional control to multi-view diffusion for controllable text-to-3d generation. arXiv preprint arXiv:2311.14494 2023.
Yaqian Liang, Shanshan Zhao, Baosheng Yu. Jing Zhang, and Fazhi He. Meshmae: Masked au- toencoders for 3d mesh data analysis. In European conference on computer vision, pp. 37-54. Springer, 2022.
Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, et al. Rich human feedback for text-to-image genera- tion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19401-19411, 2024.
Then-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming- Yu Liu, and Tsung- Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 300-309, 2023.
12

---

Published as a conference paper at ICLR 2026
Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Wenyu Qin, Menghan Xia, et al. Improving video generation with human feedback. arXiv preprint ar arXiv:2501.13918, 2025a.
Jie Liu, Gongye Liu, Jiajun Liang. Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Wenyu Qin, Menghan Xia, et al. Improving video generation with human feedback. arXiv preprint arXiv:2501.1 13918, 2025b.
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-2 sero one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 0298-9309, 2023a.
Ying-Tian Liu,
Guo, Vikram Voleti, Ruizhi Shao, Chia-Hao Chen, Guan Luo, Zixin Zou, Chen Wang. Christian Laforte, Yan-Pei Cao, al. threestudio: a modular framework for diffusion-guided 3d generation. cg. Isinghua. edu. cn, 2023b.
Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pre- trained models. Advances in Neural Information Processing Systems, 36:75307-75337, 2023.
Yu Meng, Mengzhou Xia, and Danqi Chen. Simpo: Simple preference optimization with reference-free reward. Advances in Neural Information Processing Systems, 37:124198-124235. 2024.
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.
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Bernhard Schölkopf, et al. Kernel mean embedding of distributions: A review and beyond. Foundations and Trends@ in Machine Learning, 10(1-2):1-141 2017.
Utkarsh Nath, Rajeev Goel, Fun Som Jeon, Changhoon Kim, Kyle Min, Yezhou Yang, Yingzhen Yang, and Pavan Turaga. Deep geometric moments promote shape consistency in text-to-3d generation. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4331-4341. IEEE, 2025.
Frank Nielsen. the jensen-shannon symmetrization of distances relying on abstract means. Entropy, 21(5):485, 2019.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to fol- low instructions with human feedback. Advances in neural information processing systems, 35: 27730-27744, 2022.
Ben Poole,
Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffuuion. arXiv preprint arXiv:2209. 14988, 2022.
Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin- Ying Lee, Ivan Skorokhodov, Peter Wonka, Sergey Tulyakow. al. Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors. arXiv preprint arXiv:2306. 17843, 2023.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp- PmLR, 2021.
Rafael Rafailov, Archit Sharma. Eric Mitchell, Christopher Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly reward model. Advances in Neural Information Processing Systems, 36:53728-55 53741, 2023.
Peter Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analy- sis. Journal of computational and applied mathematics, 20:53-65, 1987.
13

---

Published as a conference paper at ICLR 2026
Tianchang Shen, Jun Gao, Kangxue Yin, Ming- Yu Liu, and Sanja Fidler. Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems, 34:6087-6101 2021.
Ruoxi Shi, Hansheng Chen. Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zerol23++: a single image consistent multi-view diffusion base model. arXiv preprint arXiv:2310.155 13/.31000///00 10, 2023a.
Yichun Shi, Peng Wang. Jianglong Ye, Mai Long. Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308. 16512, 2023b.
Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf. A hilbert space embedding for distributions. In International conference on algorithmic learning theory, pp. 13-31. Springer, 2007.
Yupeng Song, Naifu Liang, Qing Guo, Jicheng Dai, Junwei Bai, and Fazhi He. Meshclip: Efficient cross-modal information processing for 3d mesh data in zero/few-shot learning. Information Processing & Management, 60(6):103497, 2023.
Jingxiang Sun, Bo Zhang, Ruizhi Shao, Lizhen Wang. Wen Liu, Zhenda Xie, and Yebin Liu. Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior. arXiv preprint arXiv:2310.16818. 2023.
Christina Tsalicoglon, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, and Federico Tombari. Textmesh: Generation of realistic 3d meshes from text prompts. In 2024 International Conference on 3D Vision (3DV), pp. 1564-1563. IEEE, 2024.
Tim Van Erven and Peter Harremos. Rényi divergence and kullback-leibler divergence. IEEE Transactions on Information Theory, 60(7):3797-3820, 2014.
Haochen Wang. Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich, Score jaco- bian chaining: Lifting pretrained 2d diffusion models for 3d generation. Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 12619-12629, 2023a.
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Pro- lifiedreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36:8406-8441, 2023b.
Zhengyi Wang, Jonathan Lorraine, Yikai Wang, Hang Su, Jun Zhu, Sanja Fidler, and Xiaohui Zeng. Llama-mesh: Unifying 3d mesh generation with language models. arXiv preprint LrXiv:2411.09500 2024.
Stanislaw Weglarezyk. Kernel density estimation and its application. In ITM web of conferences, volume 23, pp. 00037. EDP Sciences, 2018
Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is human-aligned evaluator for text-to-3d generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 22227- 22238, 2024a.
Xun Wu, Shaohan Huang, Guolong Wang, Jing Xiong, and Furu Wei. Boosting text-to-video gener- ative model with milms feedback. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024b.
Yong Wu, Yuanjun He, and Hongming Cai. Qem-based mesh simplification with global geometry features preserved. In Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia, pp. 50-57, 2004.
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 IEFE/CVF Conference Computer Vision and Pattern Recognition, pp. 9892-9902, 2024c.
14

---

Published as a conference paper at ICLR 2026
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang. Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 21469-21480, 2025.
Desai Xie, Jiahao Li, Hao Tan, Xin Sun, Zhixin Shu, Yi Zhou, Sai Bi, Sören Pirk, and Arie E Kaufman. Carve3d: Improving multi- reconstruction consistency for diffusion models with rl finetuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6369 6379, 2024.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 96:15903-15935, 2023.
Dong. Imagereward:
Jiazheng Xu, Yu Huang, Jiale Cheng, Yuanming Yang, Jiajun Xu, Yuan Wang, Wenbo Duan, Shen Yang. Qunlin Jin, Shurun Li, et al. Visionreward: fine-grained multi-dimensional human prefer- ence learning for image and video generation. arXiv preprint arXiv:2412.21059, 2024.
Kai Yang. Jian Tao, Jiafei Lyu, Chunjiang Ge, Jiaxin Chen, Weihan Shen, Xiaolong Zhu, and Xiu Li. Using human feedback to fine-tune diffusion models without any reward model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8941-8951, 2024.
Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: A gpt-4v level milm on your phone. arXiv preprint arXiv:2408. 01000, 2024.
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. In European Conference on Computer Vision, pp. 259 276. Springer, 2024.
Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Liu, Jan-Jakob Sonke, and Efstratios Gavves. Domain adaptation with cauchy-schwarz divergence. arXiv preprint arXiv:2405.19977 2024.
Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, and Efstratios Gavves. Distributional vision-language alignment by cauchy-schwar divergence. arXiv preprint arXiv:2502.17028, 2025.
Youcai Zhang. Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, et al. Recognize anything: A strong image tagging model. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp- 1724-1732, 2024.
Ruowen Zhao, Junliang Yc, Zhengyi Wang, Guangee Liu, Yiwen Chen, Yikai Wang, and Jun Zhu. Deepmesh: Auto-regressive artist-mesh creation with reinforcement learning. arXiv preprint arXiv:2503. 15265, 2025.
Zhenglin Zhou, Xiaobo Xia, Fan Ma, Hehe Fan, Yi Yang. and Tat-Seng Chua. Dreamdpo: Aligning text-to-3d generation with human preferences via direct preference optimization. arXiv preprint 043300 2025.
Banghua Zhu, Michael Jordan, and Jiantao Jiao. Principled reinforcement learning with human feed- back from pairwise or k-wise comparisons. In International Conference on Machine Learning. pp. 43037-43067. PMLR, 2023a.
Junzhe Zhu, Peiye Zhuang, and Sanmi Koyejo. Hifa: High-fidelity text-to-3d generation with ad- vanced diffusion guidance. arXiv preprint MxXiv:3055.18666 2023b.
Xiandong Zou, Wanyu Lin, Yuchen Li, and Pan Zhou. Hps: Hard preference sampling for human preference alignment. arXiv preprint arXiv:2502. 14400, 2025.
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CONTENTS OF APPENDIX
A Theoretical Analysis of CS divergence
16
Details about 3D-MeshPref'
19
B.1 Dataset Sample Visualization
19
B.2 Dataset Construction Rationale
Details about RewardCS
22
C.1 Design Rationale for RewardCS Architecture
22
C.2 Ablation Study Latent-Space Separation Loss
22
C.3 Ablation Study on RewardCS and MeshMAE
23
Details about RewardCS
24
D.1 SDS Algorithm
24
D.2 Discussion on Adaptive Mesh Fusion
Implementation Details
25
E.1 Experimental Setup
25
E,2 Annotation Pipelines
25
................................
E,3
Evaluation Pipelines
25
Extended Empirical Results
28
F.1
Comparison with 3D Controllable Generation Methods
28
F.2
Comparison with Advanced Text-to-3D Generation Pipeline
28
F3
Qualitative Visualization
29
F.4
Additional Computational Cost from Meshization Guidance.
44
A
THEORETICAL ANALYSIS OF CS DIVERGENCE
Theorem (Asymptotic Equivalence). Suppose Assumption holds. With a constant C 0, the empirical CS divergences D and Dun Dumpaired computed from paired and unpaired data satisfy:
Dppaieed Dumpaired SC
20
as m, + oc,
(11)
Vm
VT
Here provide our proof for Theorem 1.
Proof. By Assumption 1a)), know that the kernel is bounded and have supx x(xxx) x) S K < co, so the canonical feature map satisties [|<p() 1 < K for all x.
Next, we define the empirical mean embeddings for the paired and unpaired cases:
paired
((ro, (m c;)),,
p
paired
(m),
(12)
1 m
umpaired
Σ m
(re,, ,
unpaired
p(re, (n, (c)))
(13)
7
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By the law of large numbers in Hilbert spaces, for i..d. samples, obtain the weak convergence:
(re. (m c)),, Σ p(ro ,.)) &
(m ,)]],
(14)
Σ proo, [mj c,)), 1
1
16 n
Σ (n, 4 Bowne_mr=-- (m ,))]
(15)
Thus, have the convergence of the empirical mean embeddings to their respective population mean embeddings in the RKHS:
paired
f
gupaareed E pe: paired umpaired Eg pyy
(16)
where the population mean embeddings are defined as:
(mmeemmmm_o_ c))], Fy = = Ee-ro.m m- (1e)li((r< ,c)] E Pe m mtopt (11))p((r (
(17)
Applying the triangle inequality for the distance between the paired and unpaired empirical mean embeddings, we obtain:
unpaired
paired
1xx122 + |
uupaired
-xe
(18)
and similarly for py.
Therefore, the sample means from the paired and unpaired cases converge to the same population mean RKHS norm. Explicitly, we have:
paired
unpaired
E 0,
umpaired
P 0.
(19)
To quantify the rate of convergence, we apply standard results from the theory of Hilbert space- valued random variables (Smola et al., 2007). For all E
m=²
P [
paired
uz 2 E) S 2exp
(20)
2K
and similarly for p umpaired
Therefore, the convergence rate of the difference between the paired and unpaired empirical mean embeddings is given by:
1
paired
umpaired
11
=0p
=
(21)
/m
1
|
pairred
unpaired
11
Op
(22)
We now consider the empirical CS divergence functional defined as:
(plzy Myy 9
s(p pby) -2 log
(23)
which is continuous on 7. \, {0} x \ {0). This condition is satisfied under Assumption 1, which ensures that the population mean embeddings non-degenerate due to the boundedness and uni- versality of the kernel.
Hence, based on the continuous mapping theorem, yield:
[f paired
f
paired)
umpaired
uupaired)
B 0
(24)
That is,
D
D
uupaaredd
P
0
(25)
CS
m, 71 Co.
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Furthermore, under Assumption 1(b), the CS divergence functional f(..-) is Lipschitz continuous, which allows us to quantify the rate of convergence. Using the previously established rates, we can conclude:
[( paired
f(
nupaired hripairod)
My
)| Op
+ vn.
(26)
vm
Thus, the convergence rate is bounded by:
D
Dpaired
cs
jumpaired
1 O,
(
SC.
1
(27)
Vn
Vn.
where C' is constant that depends on the properties of the kernel function and the Lipschitz constant of the empirical CS divergence.
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B DETAILS ABOUT 3D-MESHPREF
B.1 DATASET SAMPLE VISUALIZATION
3D model of two-seater sofa with slanted backrest, curved armrests, and varyiny backrest heiiffts
zD model of two-seater sofa with backrest and armrests. Reward: 0.51
Reward:
L-shaped sectional sofa with backrest and two armrests. Reward: 1.61
talf, thin rectangular prism a smooth surfacc, made of light gray material. Rowark 4.31
3D model of daybed armrest, and footrest. Reward: 2.13
with with slatted base, curved backrest, seat,
3/D rectangular table with rectangular top and base.
Reward 4.88
Figure 8: Example instances from our large-scale preference-unpaired dataset 3D-MeshPref.
yD object fuuturing cubold with crapezoidal bases. alat, rectangularr surface with skighthy roursled adyes, made of Reward: 4.81
smooth material. Reward
A cylinder with spherical base and top. Reward 2.04
3D model of three tier cart with sheff on each rier. Rewardi
King size platform with slatred base. Reward 3.63
model of cutord footstool with slanted roy and bottom, royafty-free Reward: 1.33
Figure 9: Example instances from large-scale preference-unpaire, dataset 3D-MeshPref.
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Abstract goometric sculpture composed of intersecting planes and sharp angles. Reward: 4.41
Nintendo logo sign Reward: 2.90
A cluster of whitte sylhes resembling twigs or arrows. 'Remand' 4.42
& sphere- shaped ceiling light with doms. Reward: 3.46
Munochromatic scale model of large, four-engine turboproy miltary Abstract layerod architectural model with comfinaatinn of planes transport afrcraft with high T-tail.
and taxtured blacks. Reward: 3.10
Reward:
Figure 10: Example instances from our large-scale preference-unpaired dataset 3D--MehhPref.
dress aborrad with flawers. Remard 2.17
Fusurisii chainaww with a bons-cchoral grip Raward n-97
A city fuaturrnn buildings and trees. Rascard:: 1.7
3D modé of Christmas tree. Roward:
A small Chinase houss withi curved, vilef, black roef. Reward
A two-cylinder engine withi cyfindrical chambers, prominent ciradar
fianges, coumal piplng, and fixaagoaa side mufflars. oirmal nemari
Figure 11: Example instances from large-scale preference-unpairee dataset PeeMes.
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As presented in Figs. 8, 10, 11, we provide diverse 3D mesh instances in curated 3D- D--MesPrref. 3D-MeshPref 3D assets are semantically diverse, high-quality. Each example instance contains a text prompt, 3D asset, and preference reward score.
B.2 DATASET CONSTRUCTION RATIONALE
We detail the rationale behind our design choices for each component and describe the measures we took to ensure data quality.
(a) Meshilization Method. We explored both traditional point cloud-to-mesh methods, such as Alpha Shapes (Edelsbrunner & Mücke, 1994), Ball Pivoting Algorithm (Bernardini et al., 2002), Poisson Reconstruction (Kazhdan et al., 2006), and deep learning-based approaches, including Me- shAnything (Chen et al., 2024) and DeepMesh (Zhao et al., 2025). Through systematic compar- isons, we found that MeshAnything particularly with its adjacent mesh tokenization design, con- sistently outperforms other alternatives both mesh quality and computational efficiency. Based on these observations, selected MeshAnythingV2 as it offers the best trade-off among current methods.
(b) Scoring. Given the current scarcity of 3D reward models, adopted Llama-Mesh (Wang al., 2024) a large language model fine-tuned for 3D mesh evaluation - which, to our knowledge, the most capable open-source solution for mesh scoring. Its strong mesh understanding and generalization ability derive from its large-scale supervised training data and its alignment with natural language instruction, making well-suited for scoring prompt-conditioned meshes in our setting.
To further validate the scoring capability of Llama-Mesh, we follow the DeepMesh (Zhao et al., 2025) preference pair construction pipeline and construct a preference mesh dataset with 300 prefer- ence paired 3D meshes. Each pair includes a human-verified positive and negative example. Llama- Mesh evaluates all samples using the annotation criteria described in Appendix F.2 and produces a ranking for each pair. Comparing these rankings with the ground-truth preferences, we find that Llama-Mesh correctly identifies the preferred mesh in 93.6% of cases, demonstrating strong con- sistency with human judgment and supporting its suitability as an initial scorer for constructing 3D-MeshPref.
Due to the scarcity of large-scale human- aannaan-n 3D data, prior work in 3D reward modeling (Ye et al., 2024) and 3D generation (Zhao et al., 2025) often relies on automated 3D asset pipelines with human verification. 3D-MeshPref adopts this practical approach, enabling scalable and effective training of RewardCS while maintaining alignment with human preference.
(c) Human Refinement. We recognize that based ratings may sometimes overestimate mesh quality relative to human perception. To address this, incorporate manual verification and cor- rection into the data pipeline to ensure the final dataset reflects human preferences more faithfully.
In addition, to adapt the 3D reward model intermediate assets generated during SDS optimization, we augment the dataset with over 10,000+ meshes optimized using SDS-based methods, such as DreamFusion (Poole et al., 2022) and MVDream (Shi et al., 2023b), under text prompts in ABO and Objaverse. These meshes are sampled at early stages of the optimization process. Thus, we sample meshes not only at the final stages SDS optimization but also at early stages of the process. By including these intermediate 3D geometries in DD-MesPPref, the reward model is exposed to wider spectrum possible outputs, from coarse to refined structures.
In summary, we carefully selected the most capable mesh generation and scoring models currently available, and augmented them with human refinement to construct 3D-MeshPref. In addition, we include the intermediate 3D meshes across the SDS optimization in 3D-MeshPref to further extend domain of our proposed RewardCS.
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C DETAILS ABOUT REWARDCS
C.I DESIGN RATIONALE FOR REWARDCSS ARCHITECTURE
Existing mesh encoders typically fall into two categories: graph-based and transformer-based. Transformer- bassoddde encoders like MeshMAE offer superior geometric representations given the mesh input compared to the graph-based encoders, while there is currently no mesh encoder that jointly models both high-resolution geometry and texture semantics due lack of large-scale 3D mesh data. Thus, we adopt MeshMAE, transformer-bast formance in capturing global geometric structure and its robustness to partial inputs and structural noise. MeshMAF. is pretrained using masked autoencoding on large-scale 3D mesh data, making it well-suited for downstream tasks requiring shape understanding.
encoder, in RewardCS due to its strong per-
C.2 ABLATION STUDY ON LATENT-SPACE SEPARATION LOsS
We provide empirical evidence demonstrating the necessity of the separation loss Laiv in Equation. in RewardCS, Our findings validate that this component improves embedding quality and text-to-3D performance.
Table Clustering performance under different À values.
A
SCI
CHI f
DBI
0
-0.21
0.52
13.65
0.5
0.38
50.23
6,89
1
0.64
212.92
0.97
10
0.58
340.30
0.86
We conduct ablations with four different X values: 0, 0.5,, 1, and 10. For each setting, we train a separate RewardCS model and evaluate the quality of the learned embeddings using three standard clustering metrics across 10 test batches from the 3D-MeshPref dataset: Silhouette Coefficient In- Index (CHI) (Caliñski & Harabasz, 1974), and Index (DBI) (Davies & Bouldin, 2009). Across all three metrics shown in Table 4, we observe consistent improvements in cluster separability between preferred and dispreferred sam- ples as À increases, confirming that introducing Laie effectively enhances the structure of the latent space for reward learning.
dex (SCI) (Rousseeuw, 1987),
Table 5: Performance 3D Generation using RewardCS trained under different À values.
IR
VR
GA
T-A(M)
3DP(M)
G-T(M)
T-A(U)
3DP(U)
G-T(U)
-0.61
-4.10
1.97
2.91
2.80
2.62
2.25
2.48
2.23
0.5
-0.56
-3.28
2.88
3.18
3.31
3.72
3.17
3.23
3.01
-0.43
-2.10
2.95
3.21
4.09
4.02
3.27
3.69
3.65
10
-1.48
-4.28
1.26
2.38
2.23
2.07
2.21
2.06
2.07
Then We incorporate RewardCS trained with different À values into MVDream and evaluate differ- ent settings on GPTEva13D. We report results across the same metrics in our Tab. and Tab. 2, where MILMM evaluation is denoted M and user study is denoted as trained with À 0.5 and 1 consistently outperforms the X 0 variant across all metrics, with yielding the strongest performance overall. These results demonstrate the positive impact of on practical 3D asset quality.
shown in Tab. 5, RewardCS
These empirical findings support our theoretical results (Theorem 1), which show that optimizing CS divergence between unpaired preferred and dispreferred embeddings induces meaningful reward separation. Our experiments confirm that this theoretical formulation can enhance embedding qual- ity and improve generation fidelity in practice.
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C.3 ABLATION STUDY ON REWARDCS AND MESHMAE
Generalization Ability of RewardCS. Our evaluation (Table 2) is based on the GPTEva13D bench- mark, which consists of 110 test prompts that are out-of-distribution relative the 3D-MeshPref dataset. These experiments demonstrate that RewardCS variants consistently outperform vanilla text-to-3D generation basclines, producing 3D assets with better 3DP and G-T, and thus it can gen- eralize well.
we curated novel evaluation setup to measure generalization of RewardCS. We sampled 200 dis- tinct noun tags from the Recognize Anything (Zhang et al., 2024) tag set, and refined them into descriptive prompts using DeepSeek-Ri. These prompts do not overlap with those in Cap3D, en- suring zero prompt-level data leakage. We used these prompts to generate 3D assets using two text-to-3D backbones: MVDream and DreamFusion, each generating 100 assets. These outputs are distinct from Cap3D assets and represent unseen real-world inference settings.
Then to assess how well RewardCS scores align with numan-perceived quality, we used LLaMA- Mesh, a strong baseline scorer, to evaluate the generated assets based three criteria E: (1) prompt alignment, (2) structural plausibility, and (3) geometric completeness. We then compared RewardCS scores against LLaMA-Mesh scores using: Mean Squared Error (MSE) quantify absolute align- ment between RewardCSS and LLaMA-Mesh outputs; and Classification Accuracy (Acc) by dis- cretizing the score interval [0,5] into 20 equal bins. A prediction is counted correct if the Re- wardCS and --Msh scores fall in the same bin.
RewardCS achieves 0.06 and 83.10 in MSE and Acc, respectively, and thus show strong alignment with LLaMA-Mesh across both metrics, even on generated samples that lie far outside the training distribution. These results demonstrate that RewardCS effectively generalizes to high-quality 3D assets produced by text-to-3D pipelines like MVDream and DreamFusion, despite being trained on preference data from a different source.
Robustness of MeshMAE. Our evaluation is conducted on the GPTEva13D benchmark. The results show that RewardCS variants consistently outperform vanilla text-to-3D generation baselines, pro- ducing high-quality 3D assets. This performance indicates that MeshMAE- the mesh encoder used in RewardCS is robust to geometric variation and is capable of effectively guiding the generation framework.
To further evaluate whether MeshMAE can effectively capture mesh quality, we constructed 100 paired prompts from 100 unique noun tags in the Recognize Anything (Zhang et al., 2024) dataset. Each pair consists of a positive prompt with detailed object descriptions and a negative prompt, derived from the positive one, but modified to include geometry-related defects (c.g.. holes, cracks, asymmetry, and surface degradation). Then, using MVDream, we generated 3D assets from these prompt pairs, producing meshes with topology differences.
To establish reference scores, we used LLaMA-Mesh, a strong mesh-level quality assessor, to score each asset on three criteria: (1) prompt alignment, (2) realism and structural plausibility, and (3) geometric completeness. This evaluation process reflects the same preference criteria used in con- structing the 3D-MeshPref dataset.
Finally, we used RewardCS (with McshMAE as the encoder) to score the same 3D assets and com- pared the outputs to those from LLaMA-Mesh using: Mean Squared Error (MSE) between Re- wardCS and LLaMA-Mesh scores, and Classification Accuracy (Acc) by dividing the score range into 20 bins and checking score agreement within bins.
Reward CS achieves the MSE of 00 and Acc of 80.98. The results show strong aligmment between RewardCS and LLaMA-Mest across both metries, indicating that MeshMAE captures meaningful distinctions in geometric quality and structural attributes across meshes with varying topology.
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D DETAILS ABOUT REWARDCS
D.1 SDS ALGORITHM
We consider a differentiable rendering function g(v, m) which maps 3D scene representation parameterized by v/ E Yp and camera parameter m E M to a 2D image x.
Let Φ(x; c) denote pretrained 2D conditional diffusion model, where 2; is the noisy image diffusion timestep t and c is a conditioning text prompt. The corresponding noise prediction network 4))
Following the SDS framework (Poole et al., 2022), we can compute the gradient of the SDS loss with respect to the 3D parameters v via the chain rule at step t:
V CSDs((sssss (1h;m Em,,, ()) -
(28)
Our
where the latent 2e is obtained by
Ft = vo IVv, (/ vá
(29)
with ae the cumulative product of the diffusion schedule, N(O, f). and w(t) timestep- dependent weighting factor.
D.2 DISCUSSION ON ADAPTIVE MESH FUSION
To assess the impact of the Adaptive Mesh Fusion (AMF) step on geometric fidelity, we evaluate two standard surface-level metrics that quantify the deviation between the mesh before (A) and after (B) applying AMF. All meshes are normalized by the diagonal length of their bounding box for scale-invarian evaluation.
Chamfer Distance (CD) measures the average distance from points on one surface to the closest points the other surface, computed
CD B)
min x Σ ÆEA
all +
Σ yEB
min ZEA
lly
æ|11,
(30)
A
VEB
B
where |A| and [B] denote the cardinality of points on meshes A and B, respectively.
95% Hausdorff Distance (HD) captures the worst-case deviation between the two surfaces while remaining robust outliers:
HD(A, B) max Quantilegs vEB
min | yll2
Quantilegs
min [ly x112
(31)
DEA
.EA
MEB
We evaluate 40 prompts from GPTEva13D for three baselines: MVDream, DreamFusion, and DreamCraft3D. Measurements are reported for meshes before and after AMF SDS steps 4000, 8000, 12000, 16000, and 20000. The averaged results are shown in Table 6.
Table 6: Geometric deviation of meshes before and after the Adaptive Mesh Fusion step in DreamCS. CD and HD denote Chamfer Distance and Hausdorff Distance (95%-quantile). respec- tively.
Method
CD (x10-3)
HD (x10-2)
MVDream
3.1
2,9
DreamFusion
4.0
3.2
DreamCraft3D
2.7
2.4
As shown Table 6, the geometric deviation introduced by AMF is negligible (less than 0.35% average error), confirming that AMF preserves geometric details throughout optimization.
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E IMPLEMENTATION DETAILS
EXPERIMENTAL. SETUP
The conversion of point clouds to meshes was performed using the MeshAnythingg2 frame- work (Chen et al., 2024) across 8 L40-S GPUs. For dataset annotation, we employed 4 L40-S GPUs. The training of the reward model, DreamCS, was conducted on 8 x L40-S GPUs using proposed 3D-MeshPref dataset. The reward model was fine-tuned based on MeshMAE checkpoint for 100 epochs using the AdamW optimizer with learning rate of le-3 in total around 90 GPU hours. The CS divergence is computed at the batch level, and in our setup we fix the total batch size to m
256. The Geometry-Asser Assset Alignment Reward (GA) metric, derived from Re- wardCS, which is trained on approximately 10,000 annotated meshes from the Objaverse-XX XL subset of Cap3D (Luo al., 2023). Since GA provides a model-agnostic and mrchitecture-agnstie mea- sure of geometric-semantee consistency, it offers fair basis for comparing DreamCS with other text-to-3D generation approaches. This metric functions as an independent evaluator for assessing the alignment between 3D mesh geometry and textual descriptions.
E.2 ANNOTATION PIPELINES
Llama-Mesh Annotation. To annotate the quality of the converted 3D meshes in our dataset, we employ structured threc-criterion annotation rubric. Each criterion is rated Likert scale from 0.00 to 5.00, where 5.00 denotes the highest level of performance. The final score is calculated as the average of the three individual scores:
A Alignment with the provided description: Measure the degree to which the 3D mesh accu- rately reflects the corresponding textual description. Deductions applied for any semantic misaligoment, omission of key elements, or inclusion of irrelevant features.
Plausibility and realism of the 3D structure: Assess the physical plausibility and visual realism of the 3D mesh. Artifacts that violate basic physical principles or render the object visually implausible result in lower scores.
Completeness of geometry: Evaluate the geometric and textural completeness of the 3D mesh. Penalties are applied for missing components, structural incompleteness, or ab- sent/erroneous textures that compromise model integrity.
To control the potential biases in the automated assessments, rather than relying on a single anno- tation process, we score every mesh with three independent inference runs of Llama-Mesh and use the averaged score.
Human Refinement. Meshes with Llama-Mesi score greater than 2.0 are subsequently re- evaluated by human annotators using the interactive annotation panel shown in Figure 12. The same three-criterion rubric is applied, ensuring consistency with the automated Llama-Mesh scor- ing while allowing humans to correct potential errors or biases. Human raters assign scores on the same 0.0-5.0 Likert scale, and the final score is computed as the average across the three criteria, providing refined assessment of each mesh's 's quality.
Alignment with the provided description: Measure the degree to which the 3D mesh accu- rately reflects the corresponding textual description. Deductions are applied for any semantic misalignment, omission of key elements, or inclusion of irrelevant features.
Plausibility and realism the 3D structure: Assess the physical plausibility and visual realism of the 3D mesh. Artifacts that violate basic physical principles or render the object visually implausible result in lower scores.
Completeness of geometry: Evaluate the geometric and textural completeness of the 3D mesh. Penalties are applied for missing components, structural incompleteness, or ab- sent/erroneous textures that compromise model integrity.
E.3 EVALUATION PIPELINES
MiniCPM-o Evaluation. To evaluate the quality of the generated 3D assets across MVDream- based pipelines, we use MiniCPM-o based on three-part evaluation rubric. Each criterion is rated
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on Likert scale from 0.00 to 5.00, where 5.00 denotes the highest performance. The final score is calculated as the average the three individual scores:
Text Prompt and Asset Alignment: Assess the semantic fidelity between the multi view renderings and the original textual prompt. The images should depict all referenced entities and contextual elements with accurate visual attributes. Annotators are instructed to first describe the output of each model and then evaluate how comprehensively and accurately the visual content reflects the prompt.
3D Plausibility: Evaluate the inferred 3D structure by examining the set of RGB and normal images. Reviewers consider the overall realism, solidity, and structural coherence implied by the views. Deductions are made for implausible geometry, unnatural proportions, duplicated floating parts, and any visual artifacts that suggest physical inconsistency. High-quality outputs should convey convincing, well-formed 3D shapes from all angles.
Geemetry-Texxtur Alignment: Measure the local consistency between geometry and tex- ture across views. Specific attention paid to whether visual features, such as edges, pat- tems,, object parts, are coherently represented in both RGB and normal maps. Discrepan- cies between texture and underlying geometry reduce the score.
User Study. To assess the perceptual quality of the generated 3D meshes, we conduct a user study with 30 participants on 30 prompts based on three core criteria across MVDream- based pipelines, as shown in Figures 12 and 13. To avoid bias, models were anonymized, and the response order was randomized. Participants are asked to inspect the 3D meshes from multiple viewpoints and assign a score on a Likert scale from 0.00 to 5.00 for each criterion, where 5.00 denotes the highest performance. The final score computed as the average of the three individual ratings:
Text-Asset Alignment: Measure how accurately the 3D mesh represents the content de- scribed in the original text prompt. Participants assess whether all described objects, at- tributes, and spatial arrangements are present and correctly rendered in the mesh. Partial or incorrect representations lead to a lower score.
3D Plausibility: Evaluate the realism and structural integrity of the mesh geometry. Partici- pants judge whether the mesh appears physically plausible and free of distortions, unrealistic proportions, or functional geometry. 3D meshes with topological errors or unnatural forms are penalized.
Geotetry-Texture Alignment: Assess the coherence between the mesh geometry and its texture. Annotators examine whether surface textures appropriately follow the underlying shapes and contours of the geometry. Inconsistencies, such as textures that misalign with surface features obscure geometric details, result in a lower score.
Figure 12: Interface built on Online 3D Viewer used in the user study for evaluating 3D meshes. Participants could interact with the mesh (e.g., rotate, zoom).
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User Study
generated response textual prompts. each criterion, assign between 0.00-5.00 based the degree which the mesh meets the stated objectives.
presented
Indicates required question
Text-to tritttt--ssetttteeee Alignment
Evaluate the fidelity of the 3D mesh to the original text description.
Your anewer
3D Plausibility
Assess physical realism and structural integrity the mesh.
Your answer
Geometry-to Ceamettr-to-Texturr Texture Consistency
Judge the all gnigemett and coherence between the mesh geometry and its texture.
Your answer
Figure 13: User study questionnaire built on Google Form for evaluating 3D mesh quality. Partic- ipants can evaluate each mesh based on (1) Text-to-Asset Alignment, (2) 3D Plausibility, and (3) Geometry-to- Texture Consistency.
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F EXTENDED EMPIRICAL RESULTS
F.1 COMPARISON WITH 3D CONTROLLABLE GENERATION METHIOSS
3D controllable generation methods such as MVControl (Li et al., 2023), MT3D (Nath et al., 2025), and DreamControl (Huang al., 2024) can provide guidance for optimization- basedd 3D generation, but they rely on strong 3D shape prior or external conditioning 2D inputs and are architecture- dependent, making them difficult to apply in real-world text-to-3D settings. Among them, MVCon- trol is the most compatible with MVDream framework, so we adopt it for comparison.
Under the MVDream backbone, we adopt the pretrained MVControl checkpoint. Given 30 evalua- tion prompts from GPTEvaI3D, first generate 2D image prior using stable diffusion and then use the MVcontrol method to generate the 3D assets. We report performance using the same met- rics in Tables 1, 2, and 3 of our main paper, where T-A, 3DP, and G-T are derived from MLLM evaluation.
Table 7: Comparison with 3D controllable generation method MVControl.
Model
CP (t)
VR (t)
GA (1)
T-A (t)
3DP (t)
G-T (1)
Proportion (1)
MVDream
0.23
-3.30
2.77
2.95
3.10
3.07
0.55
MVControl
0.27
-3.09
2.86
3.42
3.47
3,51
0.40
MYDreea++Reward3
0.27
-3.13
2.85
3.38
3.34
3.20
0.46
MVDrremm-RewardCS
0.29
-2.15
2.96
3.58
4.04
3.93
0.31
As shown in Table 7, RewardCS outperforms both MVControl and Reward3D across all metrics, including CP, VR, GA, and especially in 3D plausibility and geometry-texture consistency. More- over, RewardCS achieves the lowest Janus proportion (0.31), confirming that it effectively mitigates the Janus issue-withow requiring any auxiliary conditioning input.
F.2 COMPARISON WITH ADVANCED TEXT-TO-3D GENERATION PIPELINE
Advanced mesh-based text-to-3D generation pipeline. We extend our experiments to advanced multi-stage SDS-based pipelines: ProlificDreamer (Wang al., 2023b) and DreamCraft3D (Sun et al., 2023) under 30 evaluation prompts from GPTEva13D. Following their default configurations, for PremificDreammrr we adopt 20000, 10000, and 10000 optimization steps for object, geometry, and texture stages, respectively. For DreamCraft3D, NeRF, geometry, and texture stages. In addition, we use Stable Diffusion to generate the 2D image prior. We adopt Zerol 23-XI. the backhone and use Omnidata for depth and normal estimation. For both methods, the rendering resolution set to 128x in the first stage and 512x512 in the subsequent stages. We report performance using the same metries Tables 1, 2, and of our main paper, where T-A, 3DP, and G-T are derived from MLLM evaluation.
use 5000 optimization steps for each of the
Table 8: Comparison with mulli-stage SDS-based text-to-3D generation pipelines.
Model
CP (t)
VR (f)
GA
T-A
3DP (t)
G-T (f)
Proportion (L)
ProlificDreamer
0.26
-3.25
2.83
3.03
3.24
3.13
0.43
+Reward3D
0.28
-3.18
2.89
3.41
3.48
3.39
0.37
ReewardCS
0.29
-2.11
3.04
3.58
4.06
4.01
0.29
DreamCraft3
0.28
-3.20
2.84
3.11
3.26
3.16
0.41
+Reward3D
0.29
-2.66
2.90
3.43
3.51
3.45
0.35
+RewardCS 0.30
-2.08
3.13
3.61
4.11
4.07
0.24
As shown in Table 8, we observe that RewardCS consistently outperforms both the vanilla baselines and the 2D reward variant. In multi-stage pipelines, 2D reward signals often unstable and require sensitive hyperparameter tuning, when optimization collapses in an early stage, the degradation propagates later stages. This underscores the robustness of 3D reward guidance.
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Additionally, RewardCS yields strong improvements DreamCraft3D, which also adopts DMTet as its underlying 3D representation. This structural compatibility allows RewardCS to provide more accurate and geometry-awart feedback on-the-fly.
Advanced mesh-based text-to-3D generation pipeline. To further demonstrate its generality, we apply RewardCS finetune the advanced end-to-end text-to-3D generation model TRELLIS-text- base (Xiang et al., 2025). We construct a finetuning dataset by randomly selecting 100K prompts from the TRELLIS-50KK training set. For each prompt, 3D asset generated and differentially rendered to DMTet or 2D multi-view images. We then use either Reward3D or RewardCS as the reward guidance finetune the TRELLIS backbone, following an objective similar to Equation
Given 60 evaluation prompts from GPTEva13D, we report performance using the same metrics in Tables 1, 2, and 3 of our main paper, where T-A. 3DP, and G-T are derived from MLLM evaluation.
Table 9: Comparison of TRELLIS with reward- based enhancements.
Model
CP (t)
(1)
GA (f)
T-A (1)
3DP (t)
G-T (t)
Proportion (L)
TRELLIS
0.29
-1.53
2,86
3.13
3.30
3.51
0.43
+Reward3D
0.28
-1.47
2.90
3.20
3.35
3.58
0.39
+RewardCS
0.30
-1.39
3.02
3.25
3.43
3.60
0.35
shown in Table 9, these results demonstrate that RewardCS consistently improves text-asset alignment, 3D plausibility, geometry-texture consistency, and reduces Janus artifacts, outperform- ing both the base TRELLIS model and the 2D reward variant. This highlights RewardCS's flexibility and effectiveness model-agnostic reward signal for advanced end-to-end 3D generation frame- works.
F.3 QUALITATIVE VISUALIZATION
(DFa
DF- DF-RewadD3D
*RewardCS
DF-DreamDPO
+RewardCS
DF+RewardCS
An assortment of vinntageffrggaan perfiem display
Figure 14: Qualitative comparisons with 1-stage generation pipelines: DreamFusion.
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DreamFusionnDD))
DF-eewrdd3D
-RewardCS
DE+DreamDPO
+RewardCS
DF+RewardC'S
S
An oldh-fashldned rotary phome with a tangled cord
Figure 15: Qualitative comparisons with 1-stage generation pipelines: DreamFusion
DreamFunion(DF)
DF-Reward3D
DF-DresmDPO
+RowardCS
DF-RowardCS
Orange monarch butterfly resting on dandefion
Figure 16: Qualitative comparisons with 1-stage generation pipelines: DreamFusion.
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DreamFusion(DF)
DF+Reward3D
DF+DreamDPO
DF+RewardCS
Tortoíse with a shell that looks like pentagon glass
Figure 17: Qualitative comparisons with 1-stage generation pipelines: DreamFusion.
MVDream(MV)
MV+Reward3D
-RewardCS
MV-DreamDPO
+RewardCS
MV-RewardCS
A thorny rose
Figure 18: Qualitative comparisons with 1-stage generation pipelines: MVDream.
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MVDream(MV)
MV-Reward3D
+RewardCS
MV+DreamDPO
+RewardCS
MV+RewardCS
A rough rock
Figure 19: Qualitative comparisons with 1-stage generation pipelines: MVDream.
MVDream(MV)
MV-Reward3D
MV+DreamDPO
MV-RewardCS
An intricately carved, solid, bronze figurine, with jagged contours depicting an ancient deity, the grain visible under matte finish, on a stone aftar
Figure 20: Qualitative comparisons with 1-stage generation pipelines: MVDream.
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MVD
MV+Reward3D
MV+DreamDPO
MV+RewardCS
A medium-sized, hollow, asymmetrical teapot, crafted to look like a shumbering dragon, with a scaly, rough texture
Figure 21: Qualitative comparisons with I-stage generation pipelines: MVDream.
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MVDream(MV)
MV+Reward3D
MV-DreamDPO
MV-RewardCS
pose A cat in sitting pose with two different colored eyes
Figure 22: Qualitative comparisons with 1-stage generation pipelines: MVDream.
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MVDream(MV)
MV+Reward3D
MV-DreamDPO
MV+RewardCS
CITY
DSLR photo of blue jay standing on a large basket of rainbow macarons
Figure 23: Qualitative comparisons with I-stage generation pipelines: MVDream.
Magic3D-SSagel
Stgicc
MagicIDDStapil Rewand5D
Stapc Reward3D
MagiclD-Stuge DreamDPO
MagiclD-Stuued DreamDPO
MagicID-Suged RReaarkS
Mage3D-Suge2 RowardCS
Tucuur
Tosii
Tucus
Tix
A vintage rable lamp casting soft, warm glow guier, dark room
Figure 24: Qualitative comparisons with 2-stage generation pipelines: Magic3D.
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MMggcc3-SSngrr
Stage
Stggec3-SSrgel
Magic
Megk3D
Magk
StagciD-Stugel
Magic3 Stage3
Tuckua
Tustes
Soaus
Tosas
Orange monarch butterfly resting on dandelion
Figure 25: Qualitative comparisons with 2-stage generation pipelines: Magic3D.
MagidDDSttag
MagkcID-Stagel
MagicID-Stagd2
DreambPO
DraaDDO
MagicDD-uggd
Toore
Tizua:
Tweas
Tacke
Torrm
Toocal
Toxael
Teokle
Texker
carveá proyfng pose
Figure 26: Qualitative comparisons with 2-stage generation pipelines: Magic3D.
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MagicID Magic3D-Stapp
MagieeD Staggel Rewardhi
MagictD-Sltgr22 RewardJD
Magk-II-Stagel DreamDPU
MagicID-Naapa DrcanDNO
MapictD-Staaal RRmanith
Magic30-Stagcd RewardtN
Ioxar
Toras
Tocas
A rubbery cactus
Figure 27: Qualitative comparisons with 2-stage generation pipelines: Magic3D.
Fantasia3D- Stagel
Fantasia3D- Stage2
Fantasia3D-Stagel + RewardCS
Fantasia3D-Stagel RRewardCS
Texture
Texture
Texture
Texture
Texture
Texture
Texture
A brick house covered with green grass
Figure 28: Qualitative comparisons with 2-stage generation pipelines: Fantasia3D.
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Fantasia3D- Stagel
+ Fantasia3D-
Fantasia3D-Stagel + RRwaarCCS
-antasia3D-Stagel RewardCS
Stage2
Texture
Testure
Texture
Texture
Texture
Floating bonsaí tree, roots in mid-aír
Figure 29: Qualitative comparisons with 2-stage generation pipelines: Fantasia3D.
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Fantasia3D- Stagel
Fantasia3D- Stage2
Fantasia3D-Staggl
+ FantasiaaDDSStagel
RewardCS
+ *RewarCCS
Texture
Texture
Texture
Texture
Texture
Texture
Texture
A lone, ancient tree stands tall in the middle of a quiet field
Figure 30: Qualitative comparisons with 2-stage generation pipelines: Fantasia3D.
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Trellis
Trellis-Rewar331
Trelis-RewardCSS
A dancing elephant
Figure 31: Qualitative comparisons with the end-to-end generation pipeline: Trellis.
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Trellis
TrellistReward3D
Trellis-RowarCCS
An ancient, twisted, and unsymmetric tower.
Figure 32: Qualitative comparisons with the end-to-end generation pipeline: Trellis.
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Trellis
Trellis RReaard3D
Trellis RewardCs
Floating bonsai tree roots in mid-atr.
Figure 33: Qualitative comparisons with the end-to-end generation pipeline: Trellis.
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Trellis
Trellis-Reward3D
Trells-RewardCS
Four colorful and vibrant balloons tied together.
Figure 34: Qualitative comparisons with the end-to-end generation pipeline: Trellis.
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Trellis
Trells-Reeard33D
Trellss
A collection of solid, frragularfy shaped hand tools, with wooden handles and metal ends, well-used and slightly rusty, hanging on a pegboard in workshop.
Figure 35: Qualitative comparisons with the end-to-end generation pipeline: Trellis.
These images present multi-view visualizations of text-to-3D results generated by one-stage and two-stage generation pipelines, respectively, across diverse text prompts. We find that text-to-3D vanilla baselines and variants enhanced with 2D guidance methods often produce 3D assets with geometric defects and the Janus problem, while 3D generation backbone with our RewardCS yields geometrically consistent 3D content. In conclusion, DreamCS outperforms text-to-3D vanilla base- lines and variants enhanced with 2D guidance methods, 3D controllable generation methods, and advanced text-to-3D generative models by achieving superior geometric consistency and texture fidelity in 3D generation.
F.4 ADDITIONAL COMPUTATIONAL COST FROM MESHIZATION GUIDANCE.
Regarding the inference cost, we would like elarify that the computational overhead occurs only in the implicit NeRF-based SDS setting, since our 3D Reward Guidance framework employs DMTet for differentiable mesh extraction during optimization. For advanced mesh based text-to-3D genera- tive pipeline and end-to-end 3D generative backbone. the computational cost incurred by RewardCS negligible. We include these implicit NeRF-basse based SDS experiments to demonstrate that DreamCS model-agnostic guidance module compatible with general text-to-3D generative backbone.
Implicit NeRF-basee SDS-based Generative Pipeline. While this introduces some overhead, we emphasize the following:
a) Significant Quality Gains Justify the Cost. Despite the added time, cushizattio-guiddes reward feedback significantly improves multi-view consistency, reduces geometric artifacts (c.g., floaters, Janus effects), and enhances 3D structural plausibility. These quality improvements are especially crucial in downstream applications like simulation, animation, and fabrication, where geometric plausibility is often more valuable than marginal runtime gains.
b) Quality-Efficiema Trade-off via Resolution Control. The meshization process is tunable via the isosurface resolution parameter. our main experiments, we use a resolution 128, which we find offers good balance between efficiency and surface detail. Users opt for lower resolutions
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during fast prototyping or higher resolutions for final asset generation, providing practical flexibility in real-world workflows.
<) Future Modular Design. Our framework is modular and fully compatible with advances in mesh extraction techniques. Faster or more efficient meshization algorithms (c.g.. learned meshing) can be easily integrated into our pipeline to further reduce runtime without compromising guidance quality.
We conducted the ablation study using MVDream and its RewardCS variants at three different iso- surface resolution: 64, 128, and 256. Baseline refers vanilla MVDream without meshization. The evaluation was performed across 15 diverse prompts from the GPTEva13D benchmark, with each 3D) asset generated through optimization over 20000 iterations (it) single L40SS GPU.
Trade-off between Generation Speed and 3D Asset Quality.
Table 10: Performance metrics for different isosurface resolutions.
Resolution
Generation Speed (1)
IR (t)
VR (t)
GA (1)
T-A (t)
3DP (t)
G-T (t)
Baseline
0.64 it/s
-0.55
-3.46
2.72
2.97
3.03
2.99
64
0.59 it/s
-0.49
-2.96
2.81
3.04
3.37
3.32
128
0.50 it/s
-0.45
-2.11
3.02
3.23
4.03
4.01
256
0.38 it/s
-0.43
-1.84
3.18
3.34
4.10
4.09
Table 11: Computational cost metrics for different isosurface resolutions.
Method
Wall-clock Time ())
VRAM (L)
MVDream
8.5 hours
21872 MiB
+ DP
8.9 hours
25787 MiB
+eeward31)
8.8 hours
26385 MiB
+RewardCS
11.0 hours
23784 MiB
The results in Tab. 10 and 11 illustrate the trade-off between generation speed, computational cost and generated 3D asset quality. We report performance using the same metrics in the main paper, where T-A, 3DP, and G-T are derived from MILM evaluation. We outline two key observations from these results:
Consistent improvement mesh quality is observed across all metrics as the isosurface resolution increases.
ii) A diminishing return in quality improvement appears higher isosurface resolution. This indi- cates that our 128-resolution setting achieves a balance between performance and efficiency.
Reducing Overhead via 3D Guidance Frequency. To reduce computational overhead, ducted additional experiments under the 128-resolution setting, where we varied the RewardCS guidance frequency during 3D asset generation. In particular, we reduced the guidance frequency from the default of "i.e., applying 3D guidance every step). We evaluated configura- tions with 3D guidance frequency of it and Lit" corresponding to the guidance periods of iterations and iterations, respectively.
Table 12: Performance metrics for different 3D guidance frequencies.
Frequency
Generation Speed (1)
IR (t)
VR (1)
GA (t)
T-A (t)
3DP (1)
G-T (t)
Lit
0.54 it/s
-0.48
-2.46
2.98
3.14
3.64
3.59
it
0.56 it/s
-0.51
-2.53
2.96
3.12
3.59
3.57
As shown in Tab. 12, despite the reduced guidance frequency, both settings still outperform the MVDream baseline across all quality metrics, while also achieving faster generation speed than the standard RewardCS guidance configuration
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Advanced Text-to-3D Generative Pipeline. For advanced text-to-3D pipelines, where methods optimize am explicit mesh rather than an implicit NeRF field, RewardCS becomes lightweight. When integrated into mesh-based SDS systems such Magic3D and DreamCraft3D, RewardCS operates directly on explicit geometry and adds < 1% inference-time cost, making it negligible. In addition, RewardCS serve 3D prior finetune current end-to-end text-to-3I) models such as Trellis, which produces 3D asset in 3 minutes. As shown in Table 13, RewardCS improves 3D fidelity and geometry metrics without introducing any extra inference cost in guiding DreamCraft3D and TRELLIS under the setting detailed in Appendix F.2, which are better than MVDream.
Table 13:
Comparison of different text-to-3I) generative backbones.
Model
CP (1)
VR (t)
GA (1)
T-A (1)
3DP (1)
G-T (t)
Proportion (1)
Latency (J)
MVDream
-3.30
2.77
2.95
3.10
0.55
8.5 hours
DreamCraft3D
0.28
33,20
2.84
3.11
3.26
3.16
0.41
5.4 hours
+Reward3D
0.29
-2.66
2.90
3.43
3.51
3.45
0.35
5.5 hours
+RewardCS
0.30
-2.08
3.13
3.61
4.11
4.07
0.24
5.4 hours
TRELLIS 0.29
-1.53
2.86
3.03
3.30
3.51
0.43
2.91 mins
+Reward3D
0.28
-1.47
2.90
3.10
3,35
3.58
0.40
2.91 mins
+RewardCS
0.30
-1.39
3.02
3.15
3.43
3.60
0.35
2.91 mins
These results indicate that advanced mesh-based and end-to-end generative backbone not only out- perform implicit SDS-based, such as MVDream, quality but also achieve significantly lower inference latency.
46


Reviews:
[{"review_id": "vigiWSYvZe", "reviewer": "Reviewer_c45f", "summary": "This work addresses the high cost of paired multi-view preference datasets and geometric artifacts in text-to-3D generation. The authors create 3D-MeshPref, an unpaired dataset of 30,000+ text-mesh-score triplets labeled via Llama-Mesh. They propose RewardCS, a reward model that encodes 3D geometry and text to predict preference scores, trained with Cauchy-Schwarz divergence to separate preferred and dispreferred meshes in latent space without requiring paired supervision.", "questions": "Please see the Weaknesses section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- The Cauchy-Schwarz divergence training approach for unpaired preference data offers a potentially generalizable framework applicable beyond 3D generation tasks.\n- 3D-MeshPref provides a human-verified dataset of diverse unpaired 3D meshes, which may be a good community resource that helps reduces dependence on expensive paired annotations.\n- The differentiable meshization pipeline enables end-to-end optimization with geometry-aware supervision, demonstrating technical soundness in integrating 3D reward signals into existing text-to-3D frameworks.", "weaknesses": "- The CS divergence receives extensive theoretical treatment (Appendix B) but lacks empirical validation. No ablations demonstrate performance degradation without this loss, no clustering baselines justify its necessity, and Table 4's λ variations don't compare against removing the term entirely. The mathematical formalism appears to add complexity without proven practical benefit. I ask the authors to further elaborate upon this point.\n- Table 1 exposes a critical flaw: RewardCS underperforms Reward3D on ImageReward (DreamFusion: -0.21 vs 1.71) and only improves when combined with it (1.89). This pattern repeats across backbones, contradicting the claim that 3D supervision addresses 2D methods' limitations. I believe the method functions as a supplement rather than a standalone solution: please elaborate on this point.\n- MeshMAE embeddings are trained purely for geometric reconstruction, encoding shape but not color, material, or style. Cross-attention thus lacks features to evaluate stylistic prompts.\n- The experimental results seem to only show marginal improvements over baseline method despite increased computation overhead.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "qoZsrafCFK", "reviewer": "Reviewer_cTX5", "summary": "The paper addresses a fundamental limitation in text-to-3D synthesis: the prevalence of geometric artifacts, such as the Janus face problem, which arise from an over-reliance on 2D view-dependent supervision. The authors posit that these flaws are an inevitable consequence of using 2D reward signals to enforce 3D consistency and propose a novel framework that learns and applies 3D-native geometric preferences directly.", "questions": "Please refer to weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* The paper correctly diagnoses a key failure mode of 2D preference signals in 3D generation and proposes a principled 3D reward to address it.\n* The paper provides a formal mathematical justification, establishing the asymptotic equivalence between the proposed unpaired objective (using CS divergence) and traditional paired supervision, which instills high confidence in the method's soundness.\n* Building 3D‑MeshPref at 30k+ meshes with human‑verified thresholds, is a non‑trivial engineering contribution likely to be useful beyond this paper.\n* The method is successfully integrated and tested on a diverse set of backbones.", "weaknesses": "* A primary concern is the use of the \"GA\" (3D Geometry-Asset Alignment Reward) metric. The authors state (Section 4, Appendix F.1) that this metric is \"based on RewardCS\" and \"derived from RewardCS.\" Using a variant of their own proposed model as a key evaluation metric creates a significant risk of \"metric-method coupling,\" where the metric may be inherently biased to favor the architecture and training objective of the method being tested. This potential bias makes the \"GA\" scores in Table 1 less reliable for fairly comparing DreamCS against other methods.\n* While the creation of 3D-MeshPref is a notable contribution, its reliance on Llama-Mesh for initial scoring is a potential weakness. Any intrinsic biases within Llama-Mesh are likely to be inherited by RewardCS. The paper's \"human refinement\" process is insufficiently detailed to confirm whether this risk is mitigated, creating the potential for a model-centric feedback loop where RewardCS learns Llama-Mesh's preferences rather than genuine human ones.\n* Based on the paper's own analysis (Appendix G.2, Table 6), the added generation complexity results in a notable slowdown. For a 20,000-step generation, the baseline MVDream takes ~8.7 hours (at 0.64 it/s), while the proposed DreamCS (128-res) takes ~11.1 hours (at 0.50 it/s). This represents a ~27.6% increase in generation time. While the paper provides iteration-per-second metrics, a more direct comparison of wall-clock time, GPU memory usage, and throughput against the 2D-guided baselines would strengthen the analysis.\n* While the paper compares its methodology against 2D-guided preference methods (Reward3D, DreamDPO), this is not sufficient to fully support the claim of mitigating the Janus problem. The paper claims to reduce Janus artifacts but only compares against baselines and other preference methods. The argument would be much stronger if a direct comparison was provided against methods specifically designed to solve the Janus problem, such as MVControl [1], MT3D [2], or DreamControl [3].\n* The Cauchy-Schwarz divergence estimator (Eq. 3) used for training the reward model has a quadratic computational complexity of O(m²+n²+mn) with respect to the preferred (m) and dispreferred (n) batch sizes, as it relies on pairwise kernel sums. This can make the reward model training computationally intensive, potentially limiting its scalability or practical adoption.\n\n[1] Li, Zhiqi, et al. \"Controllable text-to-3D generation via surface-aligned Gaussian splatting.\" 2025 International Conference on 3D Vision (3DV). IEEE, 2025.\n\n[2] Nath, Utkarsh, et al. \"Deep Geometric Moments Promote Shape Consistency in Text-to-3D Generation.\" 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025.\n\n[3] Huang, Tianyu, et al. \"Dreamcontrol: Control-based text-to-3d generation with 3d self-prior.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "woOfVP5gsG", "reviewer": "Reviewer_pQXb", "summary": "This paper introduces DreamCS, a framework that improves text-to-3D generation by incorporating 3D geometric preference signals. To overcome the limitations of existing 2D reward models, which often cause geometric artifacts, the authors make three key contributions:\n\n1. 3D-MeshPref. A large-scale, unpaired 3D preference dataset of text-mesh pairs with scores from an LLM and human verification.\n2. RewardCS. A 3D reward model trained on this unpaired data using a novel Cauchy-Schwarz divergence objective, which learns to distinguish mesh quality at a distribution level without direct pairwise comparisons.\n2. DreamCS framework. A method that integrates RewardCS into text-to-3D pipelines via differentiable meshization, adaptive mesh fusion, and progressive reward guidance.\n\nExperiments show that DreamCS enhances geometric quality and consistency, reducing artifacts like Janus faces and outperforming 2D-reward-based methods on 3D-specific metrics.", "questions": "1. Can the proposed RewardCS be effectively adapted to fine-tune pretrained, feed-forward text-to-3D models? If so, what would be the proposed mechanism? and do you anticipate it would yield greater performance and efficiency gains compared to guiding slow SDS-based optimization?\n2. Given the reliance on Llama-Mesh for scoring 3D-MeshPref, how did you quantify or control for potential biases or errors in its automated assessments before human refinement?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed RewardCS model directly tackles the fundamental issue of 2D bias in existing text-to-3D preference alignment methods, which leads to geometric artifacts like the Janus problem. The 3D-geometric aware model also bypasses the need for hard-to-collect paired preference data.\n2. The use of Cauchy-Schwarz divergence for unpaired preference learning is both effective in practice and supported by a solid theoretical proof of its equivalence to paired learning.\n3. The method is shown to be compatible with diverse state-of-the-art generators (both implicit and explicit), greatly enhancing its utility and impact.", "weaknesses": "1. The entire DreamCS framework is built upon the increasingly dated Score Distillation Sampling (SDS) paradigm, which is slow, optimization-based, and prone to artifacts. The field is rapidly moving towards fast, feed-forward text-to-3D generators (e.g., Trellis). A more forward-looking and potentially more effective approach for preference alignment would be to directly fine-tune these feed-forward models using human preferences, rather than adding a complex reward guidance mechanism to a slow, legacy SDS framework. The proposed method, while clever, addresses a problem that may soon be obsolete.\n2. The differentiable meshization and reward guidance introduce non-trivial computational cost compared to vanilla pipelines, as acknowledged in the appendix. While a trade-off for quality, this limits accessibility and practical iteration speed.\n3. The quality of the 3D-MeshPref dataset and the RewardCS model is contingent on the external components used (e.g., MeshAnythingV2, Llama-Mesh). Any biases or limitations in these models are directly inherited and not ablated, potentially skewing the learned notion of quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "f7BP90xjrQ", "reviewer": "Reviewer_697y", "summary": "This paper introduces a systematic framework to address to the challenges of aligning optimization-based 3D generation with human preferences. In particular, the authors firstly construct 3D-MeshPref, an unpaired 3D preference dataset. They then train a reward model, RewardCS, on the dataset to predict the human preferences given a mesh. Building on the reward model, the authors propose DreamCS, which integrates RewardCS into score distillation-based optimization. Experiments on GPTEval3D benchmark demonstrate the improvements in geometric accuracy, human preference alignment, and reduction of Janus artifacts across major baselines such as DreamFusion, MVDream and Magic3D.", "questions": "* The paper only conducts experiments of integrating RewardCS to SDS-based method, have the authors considered try the reward model to those more advanced diffusion guidance algorithm (e.g., ProlificDreamer[1], LucidDreamer[2], DreamCraft3D[3], etc)?\n\n[1] Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. NeurIPS 2023.\n\n[2] Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. CVPR 2024.\n\n[3] Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior. ICLR 2024.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* Novel unpaired reward learning: the paper introduces the first 3D reward model capable of learning from unpaired preference data. The CS-divergence–based loss is an elegant theoretical contribution that bypasses the need for expensive paired supervision, which is often a bottleneck in 3D learning tasks.\n* Effective reward model integration: DreamCS integrates the RewardCS model smoothly into various 3D pipelines through thoughtful system design, including differentiable meshization and adaptive mesh fusion. This indicates solid engineering and practical feasibility.\n* Systematic method design: to address the mentioned challenges, the authors constructed a new dataset, trained a new reward model, and integrated the reward model to 3D generation pipelines, forming a coherent and well-structured workflow.\n* Solid theoretical foundation: the theoretical justification for using CS divergence is well presented, with rigorous proof of its asymptotic equivalence to paired supervision. This adds credibility to the learning mechanism.", "weaknesses": "* Limited exploration of efficiency: there is little discussion on the additional computational overhead introduced by RewardCS integration and the adaptive mesh fusion step.\n* Confusing experiment labels: the short titles for the experiments on different baselines are confusing. For example, in line 1, column 3 of Fig. 5, does \"+RewardCS\" mean \"DF+RewardCS\" or \"DF+Reward3D+RewardCS\"?\n* Potential quality drop: the mesh faces produced by DMTet could be very dense. However, the proposed Adaptive Mesh Fusion (AMF) fixes the mesh faces to match \"256 non-overlapping patches, each with 64 faces\". The conversion from dense faces might lead to a loss of geometry details. The authors should provide the results before and after the AMF operation for a clearer demonstration.\n* Non-superior metrics compared to baseline: In Table 1, the IR scores of pure RewardCS integration lag behind those of pure Reward3D integration by a clear margin.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)