GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer
Authors: Sayan Deb Sarkar
Sinisa Stekovic
Vincent Lepetit
Iro Armeni
Venue: NeurIPS 2025 poster ()
Keywords: 3D Diffusion
3D Appearance Transfer
Generative Guidance
Primary area: applications
TL;DR: A training-free method that steers pre-trained generative rectified flow with differentiable guidance for robust, geometry-aware 3D appearance transfer across shapes and modalities.

Abstract: Transferring appearance to 3D assets using different representations of the appearance object - such as images or text - has garnered interest due to its wide range of applications in industries like gaming, augmented reality, and digital content creation. However, state-of-the-art methods still fail when the geometry between the input and appearance objects is significantly different. A straightforward approach is to directly apply a 3D generative model, but we show that this ultimately fails to produce appealing results. Instead, we propose a principled approach inspired by universal guidance. Given a pretrained rectified flow model conditioned on image or text, our training-free method interacts with the sampling process by periodically adding guidance. This guidance can be modeled as a differentiable loss function, and we experiment with two different types of guidance including part-aware losses for appearance and self-similarity. Our experiments show that our approach successfully transfers texture and geometric details to the input 3D asset, outperforming baselines both qualitatively and quantitatively. We also show that traditional metrics are not suitable for evaluating the task due to their inability of focusing on local details and comparing dissimilar inputs, in absence of ground truth data. We thus evaluate appearance transfer quality with a GPT-based system objectively ranking outputs, ensuring robust and human-like assessment, as further confirmed by our user study. Beyond showcased scenarios, our method is general and could be extended to different types of diffusion models and guidance functions. Project Page: *[https://sayands.github.io/guideflow3d](https://sayands.github.io/guideflow3d)*

GuideFlow3D: Optinization-Guided Rectified Flow For Appearance Transfer
Sayan Deb Sarkar Stanford University
Sinisa Stekovic ENPC, IP Paris
Vincent Lepetit ENPC, IP Paris
Iro Armeni Stanford University
Abstract
Transferring appearance to 3D assets using different representations of the appear- ance object-such such as images or text-has garnered interest due to its wide range applications in industries like gaming, augmented reality, and digital content ation. However, state-of-the-ar methods still fail when the geometry between the input and appearance objects is significantly different. A straightforward approach is to directly apply a 3D generative model, but we show that this ultimately fails produce appealing results. Instead, propose a principled approach inspired by universal guidance. Given a pretrained rectified flow model conditioned on image text, our training-free method interacts with the sampling process by periodically adding guidance. This guidance can be modeled as differentiable loss function, and we experiment with two different types of guidance including part-aware losses for appearance and silllaiiil Our experiments show that our approach successfully transfers texture and geometric details the input 3D asset, outperforming baselines both qualitatively and quantitatively. We also show that traditional metries are not suitable for evaluating the task due their inability of focusing on local details and comparing dissimilar inputs, in absence of ground truth data. We thus evaluate appearance transfer quality with GPT-based system objectively ranking outputs, ensuring robust and human-like assessment, as further confirmed by our user study. Beyond showcased scenarios, our method general and could be extended to different types of diffusion models and guidance functions. Project Page: htlps://sayands, githaaaannnnng.hhh..a.aeaa. iogguieefloowdd
Introduction
Transferring appearance-includnng both texture and fine geometric detail-to 3D object is challeng- ing and increasingly relevant problem across applications in gaming, augmented reality, and digital content creation. While style transfer has seen substantial progress the 2D domain [20, 64, 65, 68], extending it to 3D introduces unique challenges due to the irregular, sparse, and diverse nature of 3D representations [26, 40, 22, 85]. Style cues may originate from 3D shapes, 2D images, or natural language, further increasing task complexity. An additional challenge lies in transferring the appearance across objects with substantial geometric differences, required in practical 3D design.
Several existing methods address 3D generation by reformulating it as a multi-view task, leveraging 2D diffusion models and conditioning on, e.g., rendered depth images preserve input geometry [47, 36, 84J. However, these methods often produce geometrically inconsistent results due to discrepancies across different views. Recent advances in 3D generative modeling-particularyy denoising- spproaches-enable high-quality synthesis of shapes and appearances conditioned on inputs like text [54, 5, 72]. Still, these models are constrained by conditioning signals and data distributions used during training. As a result, direct application to appearance transfer yields poor generalization
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Image Appearance Transfer
Emput Mesh
Appearance Object
Ousput 3D Asset
Iaput Meah
Appearance Object
Output Asset
Text Appearance Transfer
Modern chair, thick sent aushion, off-whike textured fibbii,, and short topered warn brewn polisheol wooden. legs
T
Modern triangular wooden. table with roundted edges,
geometric inlay
pattern and cektral oustout
Figure 1: GuideFlow3D a method for 3D appearance transfer robust to strong geometric variations between objects. Given input 3D mesh, e.g., designed using simple 3D primitives, it transfers the texture and fine geometric details of an appearance object (e.g., g., the rounded edges the table the top left and the base and mattress distinction of the bed the top right) but preserves
the geometric form of the input mesh. Its flexibility across appearance modalities like meshes or text makes GuideFlow3D efficient for generating diverse 3D assets.
and limited control, partieularly when the geometries between the input and appearance objects differ significantly, as we show in our experiments. Note that, in the appearance transfer setting, the input object dictates the global geometry and the appearance one the texture and finer geometric details.
In this work, we present GuideFlow3D, a training-free framework for 3D appearance transfer that adaptively steers pretrained generative model inference time. Our key insight is that the inductive bias of pretrained generative model can repurposed for a new task through guided rectified flow sampling, inference- time mechanism that interleaves flow updates with latent-space optimization. This strategy extends the concept universal guidance to 3D generation and enables conditioning on objectives for which the base model was not originally trained. Our method builds upon rectified flow models and structured latent representations [72], and introduces differentiable guidance functions that modulate the generation process without requiring retraining. To address prior work challenges in transferring appearance to an input 3D object while maintaining its global geometry, we propose two novel forms of guidance for appearance transfer 3D shapes that significantly improve robustness to geometric variations between given input and appearance objects: (i) a part-aware appearance loss, which co-segments the input and appearance 3D shapes into semantically meaningful parts and enforces localized texture and geometry correspondence between the shapes; and (ii) self-similarity loss, which preserves intrinsic structure within regions of the imput during transfer. Our method supports various representations for the appearance When mesh is available, the appearance loss applied; otherwise, the self-similarity loss guides the generation using either an image or text. In this way, users can control whether appearance affects both geometry and texture (with mesh) or texture alone (with image or text). When only an image is provided, mesh can be generated via existing methods such [72]. This flexibility allows GuideFFlow3D unify multiple appearance modalities under a single framework, bridging geometric and perceptual style transfer in 3D.
inclcuiinniiiinggg mesh-image pairs or text.
Quantitatively evaluating 3D appearance transfer remains challenging due to the absence of ground truth input shapes that have the transferred appearance and the difficulty of comparing across dissimilar geometries. While metrics like DINOv2 [48], CLIP [29], and DreamSim [25] are typically used to assess perceptual similarity and others like PSNR [31], J. SSIM [69], LPIPS [79], and FID [15]. for reconstruction quality, they require ground truth data that does not exist our setting. To address this, we use a GPT-based evaluation scheme [15, 71] that performs pairwise output ranking in human-aligned manner. further validate the consistency of these rankings through a user study, confirming that GPT-based judgments strongly correlate with human evaluation on this task. Our experiments demonstrate that GuideFlow3D consistently outperforms baselines, generating visually appealing results that accurately reflect the intended style, while also capturing fine -naiined geometry. Our contributions are as follows:

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We introduce GuideFlow3D, a novel framework for 3D appearance transfer that applies uni- versal, differentiable guidance a pretrained rectified flow model, enforcing the generation process to respect given constraints for which it was not originally trained.
We propose part-aware and self-similarity loss functions effective forms of guidance, enabling localized and gruuctue-prrsserving style transfer across diverse 3D assets.
Our method is training-free generalizable to different appearance representations and, it could be, in principle, extended to variety of 3D generative models.
By decoupling style control from the generation process and enabling inference-time conditioning on novel objectives, GuideFlow3D opens new directions in controllable 3D generation and asset stylization. We make our code and benchmark publicly available on our project website.
Related Work
3D Generative Models. A common method for learning 3D shape generation uses autoencoders, where an encoder maps a point cloud to an embedding, which a decoder reconstructs into a 3D shape. Previous approaches rely on normalized point flows [74] or gradient fields [9]. GAN formulations are common in 3D shape generation [70, 50, 10, 27]. Luo et al. [43] and more recent probabilistic diffusion processes. Some
approaches [13, 83, 11, 46] formulate 3D asset generation works take advantage of 2D generative models to create 3D assets [54, 63, 37J. Others [12, 67, 61] propose auto-regressive models for generating 3D meshes. As discuss further in Sec. 3, Trellis [72] uses rectified flow formulation to learn the generation of structured 3D latents that can effectively be decoded in different 3D representations. Due to its ability capture fine details in both geometry and texture, we develop our appearance transfer framework around this model.
2D Style Transfer. Transferring style between images has been extensively studied in computer vision. Early work [28] formulates it as an optimization problem over pretrained CNN features, later replaced by efficient feed- forward networks [32, 66]. With multimodal embeddings, StyleCLIP [49] enables text-driven style manipulation. Diffusson- approaches [81, 68, 73, 55] now dominate the field, offering fine-graines control through acore-based modeling. Cross-Image Attention [2] performs zero-shot appearance transfer by injecting cross-image attention into the diffusion process, while MambaST [8] leverages state-space models for efficient and expressive structure- -tyyee fusion. [64] trains a generator on a specific content-style example, where structure and appearance constraints are derived from pretrained vision transformer features. Such instance-specifie formulations highlight the challenge of achieving generalizable appearance transfer across diverse geometries a limitation our approach directly addresses by introducing a guided generative mechanism that preserves geometric consistency while adapting fine-graines style cues.
3D Style Transfer. Style transfer has been explored across diverse 3D representations, including point clouds [75], meshes [7], implicit fields [30, 14, 24, 391, and Gaussian splatting [40]. 3DStyleNet [75] separates geometry and texture through dual networks, while Mesh2Tex [7] maps style images into learned texture manifold. Recent methods such as StyleRF [39] and StyleGaussian [40] represent major progress but operate on implicit or point-based representations, producing render-only outputs that lack explicit mesh control and often require multi-view optimization. StyleGaussian [40] stylizes only color while keeping geometry fixed, with performance degrading Gaussian count increases. In contrast, GuideFlow3l performs training-free free stylization a structured latent space, enabling direct mesh editing, part-aware control, and constant inference cost. In diffusion- based 3D stylization. ControlNet-style adapters [77] have become standard for injecting additional cues such as images, edges, depth, or pose into pretrained models. For example, TEXTure [56] employs score distillation for text- -uiddg texturing while EASI-Tex [52] extends this with depth-aware conditional diffusion. TriTex [15] learns a semantic texture field for single-mi -eehh appearance transfer. While effective, these methods are tied to specific training setups and conditioning modalities, limiting their generality. Building on the broader line of diffusion guidance research [21, 6, 76), GuideFlow3D combines the concept of universal guidance and rectified flow model from Trellis [72]. By incorporating geometric priors from PartField [41], our method achieves robust and geometry-awan appearance transfer without the need for task-specific finetuning.
3

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Output 3D Asset
Condition
De
Imput
2 =
åt Vo t| V, (z
Redd Armechai, Velvet upholstery
Ginided Rectified Flow
Figure 2: GuwdeFlow3D introduces guided rectified flow for appearance transfer between input object O and an appearance object. We extend the denoising process of structured latents 8", conditioned by c, by introducing an objective function L that enforces strong geometric and semantic priors during the process. We show denoised structured latents at different stages of the process, along with corresponding meshes decoded using pretrained decoder Dan. The output 3D asset displays robustness to strong geometric variations between input and appearance objects.
3 Preliminaries
Structured Latent From 3D Assets. The geometry and appearance of 3D object mesh O can be encoded using a structured latent (SLAT) representation z [72], composed of local latent codes anchored on 3D voxel grid. This representation defined
2 = (()) 2, ERR, P: E N
where denotes the position of an active voxel intersecting the surface of O, and 2; is the latent vector associated with that voxel. N is the spatial resolution of the grid and L is the number of active voxels, intersecting with the object's surface. Since the number of active voxels is significantly lesser than that of full grid, this representation is computationally less demanding, allowing to work at a higher resolution. The set of active voxel positions p; outlines the coarse structure of the object, while the corresponding latents 2; capture fine-grained geometric and visual features, as shown in [72]. This representation thus effectively captures both the global shape and detailed surface characteristics of the object. In practice, following Trellis [72], multi-view images of O are rendered, and V2 [48, 18] features are extracted for each image. After back-projection and aggregation of these features per voxel, shallow transformer encoder [57, 35] compresses them into SLAT. Structured latents can be decoded into 3D Gaussians [33]. Radiance Fields [45], or Meshes [60] using decoders D3D that share common architecture, differing only output layers and trained with representation-speeiff losses. Please note that, in our case, voxel positions remain fixed to preserve the coarse input geometry, while only the latent codes 2, are steered during generation.
Universal Diffusion Guidance. We briefly introduce the concept of universal guidance from [6]. Diffusion models are generative models that reverse the process of adding noise, typically Gaussian noise, to the data over time steps. A noisy version of the input at time step is defined as:
Ft V Bi zu -1- (0,
(2)
where 3r represents the noise scale at time step t, determined by a scheduling mechanism. Hence, It is a weighted sum of the original image and Gaussian noise. As t increases, 3 decreases, and 2y approaches pure noise as T. With some generalization, trained diffusion network fo( reverses the process by predicting the added noise. The denoising process can then be defined as:
4 2. fo(
While diffusion models can be typically guided by adding a condition to the diffusion model (a c). image, this limits applications e.g., c can be text differentiable guidance function L( can be used to condition diffusion. guidance from [6], any Then, the update in the reverse step is defined
general conditions. In universal
- 2. fold,, c) + V2,
Such approach enables general conditioning without altering the diffusion training process.
4 GuideFlow3D
present an overview of our approach for appearance transfer Fig. 2. Given an input 3D object mesh O9 and an appearance object Oo, would like modify the appearance of Of based O

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GoldeFlow3D
GuideFlow3D
Initializo
Flow Transfomerr
Flow
X
InageTest
Appearance Guidance
Self-Similarity
Impai NO Mo
Guidance
foppaamance
Asstructure
Co wigrriistiiin Clanering
Structured Latent (SLA))
Geometric Chocoring
Tyy
Positive Parti
Oibect O+
Cuppu 3D Aieel
3D Negative Parts
(b)
Figure 3: GuideFlow3D with different guidance objectives. (a) When textured mesh is avail- able for appearance object O we use our co-segmentation based objective Lappearance to guide appearance transfer. It encourages consistency between structured latents z" and noisy latents 29, In such case, we use an image of object O" to condition the generative model R. (b) When textured mesh is available, encourages intra-cluster similarity and inter-cluster disparity when denoising 59, We use text or image to condition R in such case. For both cases, we use decoder Dap to obtain the output 3D asset.
use our geometric clustering based objective Letracture for guidance.
while respecting the geometric structure O?, Oa can be represented as an image-mesh pair or text. We assume here that the appearance object provides the mesh alongside the image. In practice, the mesh can be generated by running [72] on the image. We first discuss how we use structured latents from [72] for appearance transfer problem. Then, we show how sampling using our objectives to transfer appearance while accounting for structural consistency.
guide the rectified flow
4.1 Structured Latents for Appearance Transfer
We leverage the latent structure from [72] represent object O9, but in general, different representa- tions could be considered. We initialize structured latents 2a {(# | for the input object O9, where pi are the positional indices of the active voxels, as in Eq. (1). The E/'s 's are initialized by sampling from a normal distribution. In practice, p not trainable parameter but the 2/'s are learnable in our appearance transfer formulation. This effectively enforces global geometric consistency the output with O4, Following, we define two appearance transfer objectives (Fig. 3).
Appearance-based objective. When the appearance object is an image-mesh pair, we can extract latents z for the mesh of Oa using a sparse VAE encoder [72]. Ideally, we seek 'oracle' mapping that aligns each input latent z with a semantically and geometrically corresponding appearance latent z²: e.g., in Fig. 3 (a), we would like the žạ for voxels of the back leg of an input chair be mapped to the 2j for the voxels of the back leg of the appearance chair. Since such correspondences are not readily available, we approximate them by assigning each E to its nearest neighbor in 2 based on feature similarity. Formally, our objective for appearance transfer defined as:
Tig
Cappearance
(5)
where is the index of the corresponding feature in structured latents z" for zy. While it could be possible to extract correspondences based on these latents, they are not trained to be part-aware, and this would lead to false assignments, shown in Sec. C. Instead, we establish correspondences based on geometric oe-semmenttiion clustering using bast-based feature fields from [41]. In practice, we compute PartField [41] features per voxel and run m-mass clustering, thus, relying approximate matching rather than one-to-one correspondences.
Self Self-similarity objective. In the second scenario, assume that the mesh is not available, hence Oe represented as an image or text. We observe that structure-based self-similarrty descriptors have been shown to effectively capture structural information while being invariant to appearance [59, 4,
5

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34]. We, thus, rely on a loss that encourages similarity between same object parts while promoting inter-part separability the feature space. As the appearance mesh is not available, this loss aligns structural features with the part-aware semantics implied by the textual description or image for the source. To so, we first perform geometric clustering that assigns each voxel P to cluster C. (i). Given (9,, for voxels p; and Pj E qq. we compute pairwise cosine similarity between geometric features simyy and define a part-aware contrastive loss based on self-similarity. Specifically, for each voxel Pa, the set of positive samples is defined as all j * i such that C₂ Cg(s), and all other voxels serve as negatives. The objective can then be defined by:
Σ (EC.()
exp(sima))
Suruuture
log
(6)
Lg
exp(sim.;)
JEC
where Lg denotes the of all voxels and C' the complement set C. Due its inherent contrastive nature, Luuuuure promotes local consistency without homogenizing appearance globally.
4.2 Guiding Structured Latents for Appearance Transfer
Our objective is defined using Bayesian formulation. Given the input 3D object mesh O and appearance object Oo, we would like to maximize the posterior P(O*(0)):
log P(O*10*) = log P(O9) + log P(O*
(7)
where prior log P(O') models the geometric prior of O² while likelihood log P(O* ( models the appearance. The objectives de- fined in the previous section are not enough to perform appearance transfer. While Cpppearance models the posterior log P(O* P(O)) solely op- timizing it proves insufficient. It tends to shift the latent distribution away from the one mod- eled by the generative network, often leading to implausible or incoherent results across di- verse shapes (Fig. 4 (a)). Meanwhile, Lanuctur only models P(O%) of our Bayesian formula- tion. To address these issues and better align with the generative prior, we introduce appear- transfer as guidance in rectified flow.
wio Rectified Flow
wio Guidance
w/ GuideFloww3
Obbcc 2
(a)
(b)
(c)
Figure 4: Effect of different modules. (a) Using only optimization with our objective functions to transfer appearance is insufficient as it does not en- force realistic distribution over the structured latent space. (b) The rectified flow model from [72] fails to transfer appearance when appearance and input objects have significantly different geometries. (c) We obtain appealing 3D assets when using rectified flow guidance of our GuideFlow3D.
Rectified Flow Guidance. Rectified flow models [42, 3, 38] define linear forward pro- cess, (t) = (1 t)zg + tc, which interpo- lates between data samples Zu and noise over time t. The corresponding backward process is modeled
time-depene denendepn vector field (2, = V.2, which transports noisy samples back toward the data distribution. This vector field can be approximated by a neural network Vs. trained via the Conditional Flow Matching (CFM) objective [38]:
LCFM( Er. (z,(z,t) t) (E 20)
(8)
While rectified flow enables sample generation that remains close to the learned data distribution, using it alone is insufficient for semantic control, particularly when transferring fine-grained appearance or structure. As shown in Fig. 4 (b), applying rectified flow without additional constraints often yields outputs that are within the learned distribution but fail to reflect the style texture or semantic structure, especially across diverse object categories and shapes. To enforce semantic control while preserving realism, we interleave satent-ppace optimization with sampling steps from a rectified flow model vo f c), conditioned on global signal c. Based on [72], the signal e can either be an image or text. Starting from an initial latent zr NO, I). reverse-time flow defined as:
c + At (a t |cc)
(9)

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where t E [0, 1] is linearly spaced. We adapt the equation to include guidance:
2 + At (a
c) + V Zzz |c)
(10)
where L is general guidance objective. The optimized latent 2 is then used for the next flow step. Such optimization-foon scheme allows for flexible conditioning via differentiable objectives without retraining the model, extending universal guidance [6] to any rectified flow model R. The final output
can be decoded into the 3D representation of choice based on 3D decoders from [72]. The guidance can be fit into our Bayesian formulation, where conditioned rectified flow models both prior P(O) and likelihood P(c|O), and the guidance terms become additional factors in the prior and likelihood terms. We find that this interleaved scheme preserves global realism while enabling fine-grained control over both appearance and structure, as shown in Fig. 4 (c). Qualitative "in-the-wild" transfers (Fig. 6) further illustrate robustness under large geometric discrepancies, e.g., ffumntuee
Condition-Specifee Guidance. Our method supports flexible conditioning depending on the form of the input. When reference image is provided, we may employ either appearance- or structure-based guidance. For appearance transfer, we assume access to the appearance mesh O" from which struc- tured latents z" are extracted, and we optimize Luppearance transfer appearance. Alternatively, even in the absence of a mesh, the same reference image can be used to compute geometric features, enabling structure-awan transfer via L-sructure. When the condition is text prompt, we rely exclusively on fummucture using semantic clustering of geometric features to induce part-aware correspondences, This setup enables our framework to seamlessly handle both visual and textual inputs while maintaining geometric fidelity and semantic consistency.
5 Experiments
Dataset. Since there are no publicly available datasets for our task of transferring appearance across different shapes, create benchmark for evaluation. First, for input mesh, we generate synthetic objects using procedural models from [62]. Second, for the appearance mesh and images, we leverage the ABO dataset [16], with the text captions provided by [72]. ABO contains ~8K artist-designed 3D models from Amazon, featuring complex geometries and high-resolution materials across 63 categories, mainly focused on furniture and interior decor. We use 5 categories: bed, cabinet, chair, table, and sofa, randomly sampling 100 objects from each dataset. Here onwards, we refer to the dataset of procedurally generated objects as simple and the ABO subset as complex. Using simple and complex meshes for the different categories, we create 250 input-appearance object pairs for each of our experimental setups: (i) simple-comples intra-category, (ii) simple-complex inter-category, (iii) complex-comples intra-calegory, and, (iv) complex-complex inter-calegory.
Evaluation Metrics. In absence of ground truth transferred texture for the input 3D mesh encoder- based metrics are not representative of appearance transfer for 3D shapes of very different geometries (see more in Appendix See. F). LLMs have been previously shown be a human aligned evaluator for generation tasks capturing structural preservation and content alignment [71, 80, 51]. We use GPT-5 to carefully develop more nuanced human- aliinned ranking system. This system evaluates results across six Piteeri-Sttlle Fidelity, Structure Clarity, Style Integration, Detail Quality. Shape Adaptation, and Overall Quality- capturing both semantic intent and the perceptual quality of texture transfer in 3D. Following prior work [15, 71], we use multi-view renders of input, appearance, and output for all methods and prompt GPT-5 to give us a ranking (details Appendix Sec. H). We show that GPT-5 is aligned with human preferences on this task via user study in Appendix Sec. G.
Baselines. We compare our GuideFlow3D for appearance transfer against multiple baselines: (1) UV Nearest Neighbor: We find the nearest neighbors in Euclidean space for each point in the input mesh O to the mesh of the appearance object O", and map the coordinates of the UV texture map accordingly; (2) Image-to-3D. We render the input mesh from multiple views, apply sfate-of-the-a art 2D style transfer models [2, 8], and lift the stylized renderings to 3D via the image-conditionel Trellis model; (3) EasiTex [52]: is a conditional diffusion-basee method that uses edge-aware conditioning and ControlNet [77] to texture an existing 3D mesh from single RGB image, (4) Trellis [72]: serves as our baseline without guidance, enabling detailed local texture transfer using structured latents; and (5) Text-to-3D: For text-based appearance, we first generate a reference image using Stable Diffusion [53] and then apply the same Image-to-3D pipeline described above.
7

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5.1 Appearance Transfer
Our first goal is to transfer appearance from an object given as image and textured mesh, or text, to an untextured 3D object, within the same semantic category. This setting allows us to systematically assess how well each method preserves stylistic intent while adapting to varied geometries within each semantic category. Tab. shows the results on the simple-complex intra-calegory set under both image-mesh (puppeuranee) and text conditioning (Lnsnecture) As illustrated in Fig. (top row), transferring the appearance from chair to another chair reveals clear differences in quality. MambaST [8] produces textures that are globally consistent due to its state-space backbone, ensuring smooth overall color and material coherence. However, residual gray tones from the input mesh persist, causing uneven blending and inconsistent local texture alignment. Cross Image Attention [2] effectively transfers local appearance patterns through image- congge-ondtiioeedd attention, yet fails to maintain consistent mapping when applied to 3D surfaces, introducing artifacts during transfer. EASI-Tex [52] performs competitively with its ControlNet-basee edge and depth conditioning, yet struggles under large geometric deviations due to limited generalization beyond its training setup. Trellis [72] improves upon this with its probabilistic structured latent generation but struggles to integrate appearance seamlessly across large geometric variations. Although it associates some textures reasonably (e.g., the chair seat transferred between the two), it often results in loss of fine-grained details and patchy surfaces. In contrast, GuideFlow3D achieves strong improvements in both appearance transfer and structural clarity preservation image and text settings. The transferred appearance aligns well with object structure, as seen in the smooth material transitions and consistent fabric textures on the chair. This demonstrates the ability of our approach effectively translate both visual and semantic cues into high-fidelity, structure-awes aware textures across diverse 3D objects (more results in Appendix Sec.
Table 1:
Quantitative comparison based GPT-based ranking metrics that rank quality of appearance transfer based on different criteria. Results are on the simple-complex intra-category set.
Ranking metrics
Methods
Fidelity
Clurity
Integration | Quality Adaptation + Overall
w/ Image Condition (gpperannce)
UV Nearest Neighbor
4.12
3.84
4.30
4.10
4,43
4.33
MambaST [8]
4.94
3.55
4.56
4.90
4,42
4.87
Cross Image Attention [2]
3.56
3.48
3.32
3.83
3.47
3.59
EasiTex [52]
3.18
4.30
4.08
3.17
4.18
3.81
Trellis [72]
2.51
2.58
2.53
2.69
2.61
2.62
FloddeFlow3D (Ours)
1.89
2.41
2.07
2.23
2.28
2.12
w/ Text Condition (Crreeure)
UV Nearest Neighbor
3.12
3.21
3.82
3.61
3.43
3.64
SDXL Cross Image Attention
2.88
2.52
3.25
3.38
3.29
2.98
Trellis [72]
2.01
1.89
2.67
2.75
2.39
GlideFlow3D (Ours)
1.54
1.63
2.01
2.15
2.44
1.95
Furthermore, to understand cross-category generalization, we evaluate more challenging setting where the structure and appearance inputs come from different object categories, e.g., transferring the appearance of a cabinet to a bed (Fig. 5, bottom row). Results under image conditioning for both simple-complex and complex-complex sets are in Tab. 2. We show results for text conditioning in the Appendix (Sec. A). This scenario tests not only fidelity to style but also the ability to adapt and maintain perceptual coherence under stronger geometric variations. GuideFlow3D maintains good performance across all metrics despite the added difficulty. In Fig. 5, compared to Cross Image Attention [2] and EASI-Tex [52], which degrade under large shape discrepancies, our method preserves both global coherence and fine structural fidelity. Trellis [72] not only fails to transfer the cabinet's appearance effectively but also introduces a major geometry change by closing hole on the side and texturing the altered region. Our robust performance highlights the ability to disentangle style from structure, enabling us to transfer appearance features even when spatial priors do not align. This demonstrates that method is applicable to variety of real-world applications.
5.2 Application: In-the-wild Appearance Transfer
To assess the generalization GuideFlow3D beyond furnituretooffrmiture, we explore appearance transfer in-the-wild using structurally diverse 3D assets from Objaverse-XL [19] and ABO [16],

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MamteST
Closs
GvsiFlood (Ouss)
Figure 5: Qualitative Comparisons showing quality of appearance transfer. Top and bottom rows show intra-class (chair to chair) and inter-class (cabinet to bunk bed) results respectively. In both examples, MambaST [8] blends the textures from both input and appearance objects, giving grey hue to the final result. EasiTex [52] generates non-smooth repetitions of texture and fails to generate textures for the entire object (e.g., the handles of the chair or the bottom part of the bunk bed). Cross Image Attention [2] performs better but omits texture details (cabinet's wood texture) and fails preserve good local geometry. Trellis [72] preserves better the texture of the appearance object and does better on matching it to the input object, however, it fails at providing uniform texture on the arms of the chair and does not preserve the overall geometry of the bunk bed by closing the side hole. Ours performs the best by adhering to the appearance object's 's texture and matching on the input object, while preserving the overall geometry of the input object.
Table 2: Quantitative comparisons ranking of our GuideFlow3D against baselines for different experimental settings. Results are shown with image conditioning (Cuppearance).
Ranking metries
Methods
Fidelity 4
Clarity
Adaptation
Fidelity
Clarity $
Adaptation
Intra-Categon
Inter- Inter-Category
Simple-Compln
UV Nearest Neighbor
4.12
3.84
4.43
4.06
3.51
4.17
MambaST [8]
4.94
3.55
4.42
4.87
3.57
4.38
Cross Image Attention [2]
3.56
3.48
3.47
3.54
3.55
3.52
EasiTex [52]
3.18
4.30
4.18
3.25
4.21
4.10
Trellis [72]
2.51
2.58
2.61
GuideFlow3D (Ours)
1.89
2.41
2.28
1.99
2.75
2.45
Complex- Complex
UV Nearest Neighbor
3.31
3.11
3.41
3.54
Cross Image Attention
4.00
4.13
4.07
3.79
3.99
3.91
MambaST
4.63
3.88
3.42
4.54
3.33
3.92
EasiTex
3.29
4.21
4.23
3.19
4.26
4.21
Trellis [72]
2.82
2.73
2.81
2.99
3.15
3.09
GuideFlow3D (Ours)
2.21
2.31
2.34
2.24
2.69
2.56
where either dataset can provide the input or appearance object. This setting poses significant robustness challenges due to extreme variation object categories, mesh quality, and geometric complexity. In this setting, appearance objects are represented as mesh-image pairs, allowing us to the part-aware appearance loss Lappearance. as guidance. As illustrated in Fig. 6, approach successfully transfers texture and fine geometric details while preserving the original structure. transfers the appearance between objects various semantic categories, such animals, vehicles. and furniture. Despite the lack of semantic alignment or category overlap, we are able to apply texture part-aware and structurally consistent manner, highlighting the strength of co-segmentation and geometric clustering based guidance mechanism. The resulting outputs show high visual fidelity with textures that adhere naturally the surface topology of the target mesh. Compared Trellis [72], which often loses part-aware texture continuity under large semantic shifts, GuideFlow33D maintains structurally aligned and visually coherent material transfer across categories. Notably, this application does not require additional assumptions or adaptations, and generalizes to unseen shape categories and object styles, reinforcing the adaptability of GuideFlow3D. The ability to generate visually coherent, stylized outputs has relevance for 3D asset stylization in AR/VR and digital twin generation.

---

Iapul 3D Mesi
Appearance Object
Output 3D Asset
Figure 6: Qualitative Comparisons showing in-the-wild appearance transfer. Our GuideFlow3D robustly transfers appearance between diverse semantic categories. Patterns from the body of the giraffe transferred to the seat, arms and backrest of the chair, while the appearance of giraffe legs transferred to chair legs. Across other categories we observe interesting appearance transfers: chair legs bike wheels, bed legs golden retriever legs, and cabinet handles to airplane propeller. The smaller insets in orange boxes show Trellis [72] results, which exhibit weaker structural grounding and inconsistent material mapping, demonstrating GuideFlow3D's advantage in preserving geometry- aware texture alignment even under large semantic shifts.
Conclusion
Our GuideFlow3D shows promising results in the field of 3D appearance transfer, possibly enabling exciting new applications. Given that our approach can generate realistic 3D assets from simplistic CAD designs, could play role in democratizing and simplifying 3D content creation. Tools for 3D content creation would become more accessible to artists which would significantly reflect on rapid development, prototyping, and creativity in XR and gaming platforms for example.
Limitations and Future Work. Our method is optimization based, thus is not meant for real-time use cases. Our runtime is 96s on an NVIDIA 4090 GPU, compared to 78s for baseline [72]. In the future, we could train self-supervised feed cor-ffo models for faster inference. Our implementation depends on the performance of [72] and [41]. and failures of these models would impact our performance. Our approach assumes noiseless meshes which a limiting factor for some future applications scenarios. Developing novel guidance objectives for new applications is interesting future research direction to address such scenarios. The scope of our main experiments includes furniture objects from [16] and [62], but in practice, our method can be applied to variety of object categories, as shown in Sec. 5.2, and downstream tasks such as scene editing (see Appendix Sec. D).
Ethical Considerations. Next to the exciting possibilities, there are considerable risks that should be addressed including manipulation and Deepfakes for spreading misinformation, concerns regarding intellectual property, and bias amplifications. Ethical usage of our method includes aspects of disclosing when 3D content is generated using AI, respecting and attributing source content licenses, and building systems for understanding biases some of the ways for tackling these issues.
10

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7 Acknowledgements
We thank Nicolas Dufour and Arijit Ghosh from Imagine Labs for helpful discussions on universal guidance, and Liyuan Zhu and Jianhao Zheng from Gradient Spaces Research Group for help with conducting the user study.
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Appendix
In the appendix, provide the following:
Inira- and inter-category results using text condition with Laructure (Sec. A)
Results on using rendered image condition with Lappearance (Sec. B)
Ablation study on design choices (Sec. C)
Application of GuudeFlow331 on scene editing (Sec. D)
Experimental and evaluation setup details (Sec. E)
Issue with percepiual similarity evaluation metrics (Sec. F)
Results of Human Evaluation (Sec. G)
Details and analysis of the GPT-bused evaluation setup (Sec. H)
Additional qualitative results (Sec. I)
A
Text Conditioning with Lstructure
Similar to image conditioned results in Tab. 2, we report results on using text prompts as the condition of the rectified flow model in Tab. A.I. As shown in Fig. 7, UV Nearest Neighbor fails to capture the described materials or object semantics, often ignoring features such as drawers, metal frames, or color attributes. SDXL Cross-Image Attention [2] produces locally coherent patterns but lacks structural grounding, resulting in oversaturated or unrealistic textures. Trellis [72] improves the plausibility of the output yet misses fine-grained attributes-suce as metallic elements, accent colors, or multi-material compositions and frequently distorts local part boundaries. In contrast, GuideFlow3D achieves consistently superior performance across all ranking metrics, both intra- category and inter-category. In particular, our method preserves structural clarity and convincingly adapts texture, even when appearance is only described abstract terms. The intra-category results reveal GuideFlow3DD's capacity to maintain semantic coherence between structure and style despite geometric variation. More strikingly, in the inter-category setting, where structure and appearance belong to different object types, method still maintains better ranking scores (i.e., higher quality), demonstrating its robustness to both semantic drift and structural misalignment. This showcases the strength of our self self-similarity guidance, which encourages intra-patt consistency and inter-par contrast, enabling meaningful texture placement without explicit mesh supervision. Our flexible guidance based sampling process bridges the gap between structure and high-level semantic intent.
Table A.I: Quantitative comparison ranking of our GuideFlow3D against baselines for different experimental settings. Results are shown with text conditioning (Cstrucure).
Ranking metries
Methods
Fidelity 1
Clarity Adaptation
Fidelity 1
Clarity L
Adaptation
Intra Category
Inter- Mnter-Category
Comple-
UV Nearest Neighbor
3.12
3.21
3.43
3.45
3.66
3.72
SDXL + Cross-lmage Attention
2,88
2.52
3.29
3.18
2.96
3.33
Trellis [72]
2.01
1.89
2.55
2,48
2.38
2.69
GlodeFlow3D (Ours)
1.54
1.63
2.04
1.85
2.01
1.98
Complex- Complex
UV Nearest Neighbor
3,45
3.52
3.61
3.58
3.67
3.75
SDXI. Cross-Image Attention
2.97
2.78
3.22
3.15
3.06
3.31
Trellis [72]
2.16
2.05
2.49
2.35
2.41
2.66
GlideFlow3D (Ours)
1.36
1.48
1.53
1.72
1.87
1.90
B Rendered Image Conditioning with Cappearance
In many practical scenarios, an appearance mesh provided without the corresponding reference image-eithe because the asset was procedurally generated, sourced from a CAD dataset, or previews were never created. To address this scenario, we render the appearance mesh from a fixed viewpoint to synthesize image, which serves as the conditioning input for Lappearance- For all objects
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Mesh
Appearance
UN Nearest Neighbox
SDNL CLA
Trelis
Guide (Curs)
heedeliih
lege with two diitwers, aw open shelf, avd bublt sooibat.
Ligat blue resining armuchaii
with parides sem, thisk armerssts,
restinguular headeestt
brows lever the lower right.
Light restaiguular seffee three plank top, black metal accteds, control drowir with ring rapered lege.
blacks mutall-frmmad nnd with hendlbaardt, white rectangu lar wattress, two white varsingular gillows.
mediaum sized, restangular weoden. caliiet naturtii finish, two paneled deors and single drawer, with black losebs visible grain potterns
Figure 7: Qualitative Comparisons showing results using text condition with Lstructure- Each row shows input 3D mesh, descriptive appearance text, and textured results generated by different methods. show challenging examples with considerable discrepancies between the appearance text prompts and input geometries. GuideFlow3D grounds textual descriptions in geometry, producing coherent, part-aware textures. The fifth row illustrates a failure case where abstract terms like "metal" or "cushioned" not fully captured, highlighting the difficulty of interpreting underspecified text. Overall, GuideFlow3D delivers detailed, realistic appearances aligned with object semantics.
Figure 8: Examples of rendered mesh views.
use the same viewpoint, chosen to provide slightly angled perspective on the object as shown in Fig. Such view of the appearance mesh captures sufficient visual cues such as texture, material, and shape context-to serve as effective guidance for our framework. This strategy enables mesh- based guidance the absence of external visual data. Table B. shows results for this setup across intra- and inter-category pairs. Note that these ranking scores are not directly comparable to those Tab. 2, as this experiment includes only the top performing baseline (Trellis); the absence of roweee outputs shift absolute values due to the relative nature of GPT-based evaluation.
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While the reference image variant performs best overall, our cendered-view approach still consistently outperforms Trellis [72]. demonstrating GuideFlow3D's robustness against limited image data.
Table B.1: Quantitative comparison ranking of our GuideFlow3D against baselines for different experimental settings. Results are shown with image conditioning (Cappeorance).
Ranking metries
Methods
Fidelity
Clarity
Adaptation | Fidelity
Clarity +
Adaptation J
Intra-Catega Category
Inter- Category
Simple- Complee
Trellis [72]
2.94
2.96
2.95
2.93
2.97
2.96
w/ Rendered Image
1.96
1.98
1.99
1.97
1.99
1.98
w/ Reference Image
1.08
1.03
1.02
1.06
1.02
1.01
Complex-Complee
Trellis [72]
2.94
2.97
2.96
2.95
2.97
2.97
w/ Rendered Image
1.96
1.99
1.98
1.97
1.99
1.98
w/ Reference Image
1.01
1.02
1.02
1.07
1.02
1.01
C Ablation Study
We conduct an ablation study to analyze the impact of various design choices in our framework. Results in Tab. C.I show performance the simple-complex intra-category under image condi- tioning. Inspired by the use of global appearance loss in [64), (i) we model global appearance for guidance. We do this using combination of minimum, maximum, and average pooling operations which we found insufficient for our application. This highlights the ineffectiveness of global latents in capturing rich semantic correspondence required for high-quality appearance transfer. (ii) Re- placing such global features with nearest-neighbo, (NN) matching in SLAT space slightly improves performance, especially in fidelity, but lacks robustness integration and adaptation, confirming that unstructured similarity fails to align content meaningfully across different shapes. (iii) Next, we evaluate a version using K-means-basee based co-segmentation over SLAT features instead of the PartField-driver segmentation [41] in GuideFlow3D. While guided flow improves generation realism, this variant still underperforms relative to the full method, indicating that semantically informed segmentation is crucial for establishing accurate part correspondences. (iv-v) Finally, we compare the two guidance objectives used our framework: Luppearance: and Lsstruttress both using image as condition. Each guidance improves transfer quality in its respective setting, with appearance guidance achieving stronger detail fidelity and structure guidance yielding better alignment and adaptability. These results validate our design of condition-specific guidance, where the choice of loss is tailored to the nature of the input, enabling flexible and semantically consistent transfer.
Table C.I: Ablation study ranking design choices using image conditioning. Results the simple-complex intra-category set.
Ranking metries Quality
Methods
Fidelity
Clarity
Integration
Adaptation
Overall
Rectified Flow
oo-segmentation [41] & global feat.
4.52
4.51
4.53
4.51
4,49
4.50
(ii) Co-segmentation [41] NN
3.58
3.62
3.61
3.60
3.61
3.63
w/ Guidance in Rectifed Flow
(tii) K K-means Co-segrmentation + NN
2.57
2.65
2.60
2.63
2.61
2.66
w/ Image Condition (Cammuur))
2.17
2.05
2.12
2.05
2.11
2.03
(V) Image Condition (Cagpesnace) Ouers
1,23
1.08
1.16
1.11
1.12
1.06
D Application: Scene Editing
To demonstrate broader applicability GuideFlow3D beyond isolated object transfer, we explore its use in full-scene editing. In this setting, we start with indoor scans from ScanNet [17] and utilize per-object CAD mesh annotations from PyTorchGeoNodes [62] and Scannotate [1] to obtain 3D mesh geometries of objects in the scene. For each semantic category present in the scene, select
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representative appearance object from the ABO dataset [16] and perform appearance transfer individually using Cappeurance on the corresponding meshes. As shown in Fig. 9, our method is capable of stylizing multiple objects across a cluttered scene while preserving their geometric structure. Note that objects with inaccurate pose annotations or high computational demands for Trellis processing are excluded from the transfer process. Despite relying on clean input meshes, our framework generalizes well in complex, real-world environments and opens avenues for future extensions with learned guidance in noisy or incomplete settings. This highlights GuideFlow3D's s utility for interactive scene customization, where designers can selectively restyle environments by swapping appearance while preserving structural layout.
Input Scene
Appearance Objects
Output Scene
Figure 9: Scene editing on scenes from ScanNet [17]. For each semantic category, represented with a unique color in the middle column, we select a single appearance object and perform appearance transfer using our GuideFlow3D. Appearance is robustly transferred to different objects in the scene, showing another potential application of our approach.
E
Experimental Details
For evaluation, we render all assets dasets-including the input, appearance, and output ueshes-using Blender with smooth area lighting. Each object is rendered from 4 viewpoints, sampled around the origin at a fixed radius of 2 units and a pitch of 30 with yaw angles spaced every 90 starting from 45 All meshes are in canonical pose to ensure input, appearance reference, and output are aligned, avoiding mismatches like comparing the front side of one object to the back side of another. For the rendered mesh-view guidance experiments (Sec. B), we use a pitch and yaw of 30 and 45,, respectively, to obtain a single consistent view of the appearance mesh. However, evaluation setup remains the same. Following standard practice [58, 15], we compute every metric, perceptual and GPT-based per view and per object, and report the average across all objects. This ensures that evaluations account for view-dependent variations.
Evaluation Setup.
Implementation Details. Our implementation based on the pre-trained models and configurations from Trellis [72] and PartField [41]. We use sallissimage-lagg for image conditioning and for text, and adhere to their default settings for any configuration. For part- aware guidance, we compute part feature fields using PartField [41] and query them using the voxel coordinates p; of each mesh We run GuideFlow3D for single-instance optimization interleaved with rectified flow sampling over 300 steps. Optimization is performed using Adam with a learning rate 5 x 10-4, All experiments are conducted on a single NVIDIA RTX 4090 GPU. We use identical optimization settings across all conditioning types to ensure fairnsss and consistency.
F
Issues with Perceptual Similarity Evaluation
Quantitatively evaluating appearance transfer in 3D is challenging due to the absence of ground truth outputs. Unlike traditional image-to-3D pipelines, where the generated asset can be directly compared to reference view or textured model, our setting lacks definitive target for what the
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"Ground Truut""
CLIP 80.26
CLIP Score: 84.24
Trellis
CL.IP 86.85
CL.IP 87,80
GuideFlow (Ours)
Figure 10: Issue with Perceptual Similarity. Even though our GuideFlow3D visually outperforms the Trellis baseline [72], CLIP scores do reflect these improvements. this example, we observe that the geometric forms of the input and appearance mesh are very different. Therefore CLIP score a suitable metric for evaluating similarity of output. We observe that the difference in scores increases for cropped part of the chair, showing the difference between local and global geometry on metric.
transferred appearance "should" look like. As illustrated in Fig. 10, the input and output geometries intentionally dissimilar, and the appearance is adapted-not reconstructed-makee
traditional reconstruction based metrics inapplicable. To this end, we report commonly used perceptual similarity scores such DINOv2 [48], CLIP score [29], and DreamSim [25] in Table F.1, image-conditionss appearance transfer for entra-category on simple-compler set. Due to large geometric differences between appearance and input geometries, these metrics offer only coarse proxy for evaluating style transfer quality and often fail to capture the localized and semantic nuances essential to this task. Furthermore, in case of conditioning using text prompt, CLIP score is actually slightly lower than the score for the Trellis baseline which is deceiving. This because the text prompt often describes a very different geometry which metrics less applicable for our experimental setting (e.g. "padded reclining armchair" vs. input mesh of simple stool, shown in second of Fig. 7).
aligned with the geometric form of the input object making such
For instance, Fig. 10 shows an example where our method produces visibly better texture alignment and part-aware fidelity, yet perceptual similarity scores (c.g., CLIP) remain close even favor inferior outputs. Comparisons on image crops, where scores between methods show larger gaps, highlight how such metrics overlook structural misalignments, texture stretching, poor integration when focusing on the global scale that are perceptually obvious to humans. While such encoder- based metrics could be useful in setups with ground-truth part-level correspondences that would enable an evaluation on cropped regions, their effectiveness is limited in our setting where no such reference exists. These limitations underscore the need for our fine-grained, human-aligned evaluation.
G Human Evaluation
We conducted user study on Amazon Mechanical Turk with participants compare our approach against basclines. The evaluation set consisted of 100 randomly selected object and image pairs from
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Table F.1: Quantitative comparison based on perceptual similarity metrics. Results are on the simple-complex intra-category set. Note that DinoV2 and DreamSim can only be evaluated in an image setting.
Perceptual similarity
Methods
DinoV2 f CLIP DreamSim
w/ Inage Condition (Lagooraece)
Nearest Neighbor
36.32
81.23
42.27
Cruss Image Atlention
44.67
84.60
41.67
MambaST
38.33
81.73
47.55
EasiTex
44.42
83.80
41.13
Trellis [72]
52.22
87.15
36.52
Flow3D (Ours)
52.74
87.37
36.06
w/ Text Condition (Lanews
UV Nearest Neighbor
25.32
SDXL + Cross-Imas Attention
25.96
Trellis [72]
27.64
GuideFlow:3D (Ours)
27.23
25
Win (in
20
15
10
GuodeFlow3D (Ours)
Trelis
UV Nearest Neighbor
EasiTex
Cross Image Allention
MambaST
Figure 11: Results of Human Evaluation on randomly sampled outputs from both intra- and inter-
category on simple-complex set.
the simple-complex intra- and inter-category. on the image condition setting. The participants were shown the appearance image and two views (front and back at a camera angle 45 degrees) each of the structure mesh + outputs from different methods, without any knowledge about the methods. Then, they were provided with the same prompts as the L.LM (Figs. 13 and 14) and asked rate the best performing considering overall quality. As shown in Fig. 11, out of approximately 1000 evaluations, GuideFlow3D received the highest preference (27.2)). demonstrating preservation and appearance fidelity. Interestingly, UV Nearest Neighbor was ranked third, despite its lower quantitative performance in Tab. likely due to visually appealing but less semantically grounded results. Overall, the relative ranking across methods remains consistent with our L.L.- based evaluation, confirming strong alignment between L.LM-based and human judgments texture fidelity and perceptual quality.
ability to balance structural
H GPT-based Evaluation
We outline the issues with automated perceptual similarity evaluation Sec. and resort GPT-based evaluation mechanism for analysis. GPT-4V has shown strong alignment with human judgments across vasion-language tasks, including text-to-3D generation [51], image understanding, and 3D content evaluation. [71, 44, 23, 80]. In designing the GPT--ased evaluator, we follow the human-aligned evaluation protocol proposed in GPT-Eval3D [71] and adopt for appearance transfer using gpt-5-mini. We begin by clearly explaining the task to GPT evaluator: ranking textured 3D
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outputs based on the quality of appearance transfer from style source (image or text) to structure mesh, using single rendered view per mesh. All images are rendered from a consistent viewpoint, and the evaluator is instructed to imagine each output as a complete 3D object when judging texture consistency and structure preservation. As shown in Fig. 13 and Fig. 14, the instruction prompt defines six specific evaluation criteria: Style Fidelity, Structure Clarity, Style Integration, Detail Quality, Shape Adaptation, and Overall Quality. Each criterion is accompanied by a detailed explanation, emphasizing semantic consistency (c.g. appropriately mapping wood textures to structural elements like legs or seats, depending on the design), 3D geometric clarity, texture sharpness, and perceptual coherence. This structured rubric guides the LLM to perform fine- fine-grained comparisons across outputs. To ensure consistency in output formatting and facilitate large-scale evaluation, we explicitly define a fixed output format corresponding to the six criteria. An example format is provided directly in the prompt to reduce ambiguity and improve parsing during automatic result aggregation. Additionally, support in-context learning by optionally breaking the prompt into multiple stages with example completions shown before evaluation begins. These design choiccs-sstrctuune strict formatting, and optional in-context examples-ense ensure that our -pssed evaluator produces reliable, human-aligned rankings across all experiments. Please note that we use output from all methods for comparison at time (three shown as an example in Figs. 13 and 14).
rubric, visual clarity,
Limitations. While our GPT- evaluation protocol proved practical and consistent for our purposes, prior work highlights several factors that future users should consider. Large multimodal models are known to exhibit hallucinations [71, 80] and systematic biases, such as position sensitiv- ity [82, 78], and preference for high high-contrast textures. These factors may distort rankings in ways that do not reflect actual quality. Moreover, GPT-based evaluation could be vulnerable to adversarial artifacts designed to exploit the multimodal model's visual priors. Ensuring such metrics remain "ungamable' is an open challenge. Future research can explore more efficient comparison strategies such single method evaluation using a scale, e.g. 1 to 5 [51].
I Qualitative Results
We provide qualitative comparisons for both text- and image- condicoond appearance transfer. Figs. 7 and 12 highlight GuideFlow3D's ability to produce semantically faithful and geometrically aligned textures across diverse scenarios. In the text-conditioned setting (Fig. 7), we evaluate challenging examples with significant discrepancies between textual descriptions and input geometries. UV Nearest Neighbor and SDXL [53] + Cross Image Attention [2] produce unrealistic outputs, often failing to capture semantic or color cues. Trellis [72] shows improvements texture plausibility but struggles with global color consistency and local part correspondence, e.g., missing "reddish-brown" tones or "black metal frames." contrast, GuideFlow3D effectively grounds textual descriptions in geometry, yielding coherent, part-aware textures that accurately reflect material and structural cues. Nonetheless, when descriptions are abstract (e.g., "metal," "mirror," " or "cushioned"), all methods show limitations, as shown in Fig. 7 (Row 5), highlighting the inherent ambiguity in interpreting such prompts. Under image conditioning (Fig. 12), UV Nearest Neighbor frequently misplaces textures due to limited geometric reasoning, though it performs reasonably when single uniform texture dominates. MambaST [8]. Cross Image Attention, and EasiTex [52] tend to oversmooth details or blend appearance cues across regions, while Trellis improves realism yet lacks explicit geometric grounding, sometimes introducing artifacts, such as dark textures from correspondence failures. GuideFlow3D achieves semantically and structurally consistent transfers, faithfully reproducing upholstery and wood patterns while maintaining sharp boundaries and material coherence. However, certain edge cases remain challenging (Fig. 12, Column 5), such strong geometry-appearane mismatches or texture flattening under large shape variations. Overall, GuideFlow3D demonstrates robust semantic and structural fidelity across both modalities, effectively bridging the gap between appearance semantics and geometric form. These results underscore its capability to generate realistic, cndductiio-ready 3D assets.
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Jupur 50 Mesh
App: Imagge
UV/AN
MantusS
Cios Image Attetion
Easiios
Tralte
Guidee (Ous))
Figure 12: Qualitative Comparisons showing results using image condition with Lappearance- We show 5 examples (one per column) and the results for all methods. UV Nearest Neighbor (UV NN) often misplaces textures across parts but performs better with uniform materials. MambaST, Cross Image Attention, and EasiTex blend or oversmooth textures, while Trellis improves realism yet lacks geometric grounding. GuideFlow3D achieves semantically and structurally consistent transfers, accurately mapping materials like upholstery and wood. Column 5 illustrates a failure case for all methods, though UV NN yields the most visually coherent result and interleaved guidance scheme cannot recover from the black output produced by Trellis.
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Over task is vauce three textured SD outpucts eash createol by transfering texture from a style mush owto strusturrt mesh.. we will evaluate the results based on the visual quality of the texture tranafor, using single rendered view each wussh. All images reusered from the same visupoiut. white only one image is available per mesh,, please imagine each full object consider dimenssons. The methed should not only transfer color and texture from the style mush, but also do so with semantic awareness applying textures meaningficlly to appropriate parts of the structure. Additionally. the geometry of the structure mesh must be preserved and visually clear. we would like to compart based on the following oritería:
textures might wrap around and behave aoross surffree in three
# In.trrcction
1. Style Fidelity: How well dloes the outpuct capturre the visuel essence of the style wesh? Look for colour Rcourday, material feel (e.g. matts, glassy, metallio), and stylistie patterns or motifs. A strong transfer should respeot the semantio instent of the texture e-g., wood textures wappped to handles, not faces. The result should feel like coherext restylinng, not a ranalow recoloring.
Strueture clarity: boes texture preserve recognizable geometry of strusture wesh? Key parts (arms, legs, surfaces, joints) should remain distinguishabbe, Textures should enhance, not obscure. Imagine rotating the abjest: would the structurt remeiv, clear? Preservation. form and toundaries is oritical.
Style Integration: How smoothly and appropriately is the style applied to the structure? Evaluate transitions across surfasss, texture seams, and alignment with part boundaries. Semantisally aware integration maps the right textures to the right parts. Bad integration Lookss pastedl or wissatthhed.
4. Detail Quality: Are local textures (grains, brushwork, ornamentation) clean, sharp. and artifact-free? Loole for woise, blue, or visual insonsistenoies. Even with stylization, the details should feel intentional and uniformll high quality aoross the mesh.
5. Shape Adaptation: boes the texture waturally follow an geometry? Looke for flow along surves, alignmmnn to contours, and atsence of warping or stretshing. Imagine wrapping the style aoress full sts object. well. adapted textures wainERii realism. and part continuity.
which output delivers the best result overall? at visual appeal, tschnical excoution, and whether the texture feels intentionally and coherently applied the structured shope.
overall Quality: Considering all
# Output Format
For each criterion, rank outputs from best (1) to weyst (x). Tées are allowed (e.g, 22 Summarize in this format: Final auswer vankA / raueR/ ranlec / raukD / vauke/ rawkF
(Style Fidelity / Structure clarity/ / Style Integration betnil Quality / shape Adaptation Overall)
Example:
912/A3//442/2113/522113
Figure 13: Image GPT prompt. Prompt provided to GPT-5-mini for providing ranking of textured 3D outputs given an appearance object the form of an image.
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Our task to vauk three toxtured sD outputs, based on desoriptive style caption onto structurt wush.. We will eveluate the results based ow the visual quality of the textuore tranafer, using a single rendered view of each mesh, All images are rendered from the same viewpoint. while only one image available per mush, please imaginee eRch as full object consider how textures wighht wrap around and behave aoross surfaces in three dim.ensions. The muthhod should translate style desoribed
caption. includding material, color palette, pattern, and visuel feel and apply meaningfullyy and coherently the structure. This insludes respecting semantic oues (e.g, weod for hawalles, fabrio for seats) and weinceining the clarity of the original geawetry.
# Instruction
We Would like to compare based on the following oriteria:
I. style Fidelity: How well does the output capture the visual essence of the style captiou? Looke for oolor acouraoy, material feel (t.g. matte, glassy. metallio), and stylistie patterns or mottiff. A strong transfer should respect the semantic intent of the texture 2.g. weed textures mapped to handles, not faces. The result should feel like a coherent restyling, not randow. resploringg.
Structurt clarity: Dees the texture preserve the resognizable geometry of the strusture wesh? Key parts (arms, legs, surfaces.joive should remaii, distinguishable Texturts should eniaaned not obtsours. incegine vetatinng the object: would the strusture remain clear? Preservation. of form and part: boundaries is oritioal.
as Style Integrations How smoothly and appropriately is the desovided style applisd to the structure? Evaluate transitions seross surfaces, texture seams, and alignment with part boundarriss. semantieally aware intagration maps the right textures to the right parts. Bead integration lookss pasted or wismatthhdd.
4. Detail Quality: Are locel textures (grains, brushwerle, ornamantation) clean, sharp. and artifact-ffree Looke for noise, blur, or visual inconsistencies. Even with stylization, the desils should feel intentional and uniformlly high quality aoross the wussh.
Shape Adaptation: the texture naturally follow the geometry? for flow along ourves, aligmmmnt sontours, and absonce of warping stretshing. imagine wropping the style aoress full SD object. well actppted textures wRintaan, realism and part continuity.
6. Overnll Quality: considering all of the above, which output delivers the best result everall? Looke at visual appeal, teshnical excoution, and whether the texture feels intentionally and coherently applied to the structured shape.
# Output Format
For eRch oriterion, ranke outputs from best (x.) to worst (s). Ties are allowedl (e.g.. 22 Summarizz in this format: Final awswer: vanke ranles ranleo / rawles/ vawle ranler (style Fidelity/ Strusture clarity Style integration. Debail Qualityy / Shaps Adaptation overall)
Example:
912/291/522//11/19///22
Figure 14: Text GPT prompt. Prompt provided to GPT-5-mini for providing a ranking of textured 3D outputs given an appearance object in the form of text.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Abstract and Introduction (Sec 1), we claim a novel method for appearance transfer between 3D objects based on guidance for rectified flow models. We claim that method achieves very good results outperforming other baselines. We support the claims with experimental analysis in Sec. 5.
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Answer:
[NA]
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Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
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Reviews:
[{"review_id": "jwKrCp5oIQ", "reviewer": "Reviewer_nLwK", "summary": "GuideFlow3D introduces a training-free framework for 3D appearance transfer, effectively addressing challenges posed by geometric differences between input and appearance objects. It leverages pre-trained rectified flow models and employs part-aware appearance and self-similarity guidance losses during inference. The method supports various appearance representations, including images and text. This approach offers a flexible solution for high-quality 3D asset stylization.", "questions": "Please refer to weaknesses.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. The paper is clearly structured and presented, making the proposed methodology and experimental results easy to follow and comprehend for the reader.\n2. GuideFlow3D’s training-free nature and its support for various appearance representations (images, text) make it a highly flexible framework for diverse 3D appearance transfer tasks.\n\nWeaknesses\n\n1. The core methodology of GuideFlow3D can be perceived as an incremental combination of existing techniques. It heavily relies on pre-trained rectified flow models, and the concept of guidance for generative processes is well-established. The specific guidance losses, while adapted, may not introduce fundamentally new principles.   \n2. A major limitation is the absence of direct comparisons with recent state-of-the-art 3D appearance and style transfer methods, such as TEXTure [1], StyleGaussian [2], and StyleRF[3]. Without benchmarking against these prominent approaches, it is challenging to ascertain GuideFlow3D's true competitive standing and relevance in the rapidly evolving landscape.\n3. The prompts and scenarios used in the experimental evaluation lack complexity and diversity, raising concerns about the method's robustness and its generalizability to more challenging real-world settings.\n4. The results shown typically use relatively clean input 3D meshes and clear appearance conditions. It's unclear how well GuideFlow3D performs when dealing with noisy, incomplete, or low-resolution input meshes, or low-quality/ambiguous appearance images/text. How does the quality of the input 3D mesh or the clarity of the appearance source (image/text) affect the output quality?\n\nReferences\n\n[1] Richardson, Elad, et al. \"Texture: Text-guided texturing of 3d shapes.\" ACM SIGGRAPH 2023 conference proceedings. 2023.\n\n[2] Liu, Kunhao, et al. \"Stylegaussian: Instant 3d style transfer with gaussian splatting.\" SIGGRAPH Asia 2024 Technical Communications. 2024. 1-4.\n\n[3] Liu, Kunhao, et al. \"Stylerf: Zero-shot 3d style transfer of neural radiance fields.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.", "was_revised": true, "final_justification": "The authors' clarifications on the method's robustness and the scope of their experiments have resolved my concerns. The new experiments comparing GuideFlow3D against strong, recent baselines like EASI-Tex, Cross Image Attention, and Mamba-ST provide the necessary context and demonstrate the competitive performance of the proposed method.", "rebuttal": "We thank Reviewer nLwK for finding our paper **clearly structured** and our method's **training-free nature, highly flexible** for diverse 3D appearance transfer. Below, we address all the questions:\n\n> Q1: The core methodology of GuideFlow3D can be perceived as an incremental combination of existing techniques. \n\nWhile guidance is a well-established topic in 2D denoising models, our work extends this concept for transfering appearance (texture and geometric details) between  two objects in 3D. Our guidance formulation repurposes a given generative model for a new task (different from the one it was originally trained for) in order to leverage the inductive bias of a pretrained rectified flow model. We propose a novel inference-time mechanism that interleaves rectified flow sampling with latent-space optimization, incorporating custom guidance losses into the denoising trajectory of structured latents. This formulation enables conditioning the generative process without retraining or modifying the base model, extending universal guidance in 3D. **This approach elegantly avoids the need for costly retraining or complex architectural modifications, e.g., ControlNet-like adapters** (Reviewer dDnT), \npositioning it as a **flexible, training-free solution** (Reviewer dDnT, zJCJ). The proposed loss functions are tailored to the specific use case of 3D appearance transfer, by **establishing a semantically meaningful part-aware correspondence and preserving intrinsic structures in absence of explicit geometric supervision** (Reviewer dDnT).\n\n> Q2:  A major limitation is the absence of direct comparisons with recent state-of-the-art 3D appearance and style transfer methods.\n\nWhile we compare with Trellis to show how guidance can improve its local editing capabilities to support 3D appearance transfer, we understand that it is challenging to ascertain competitive advantage without benchmarking against recent style transfer works. Direct comparison with StyleRF [1] and StyleGaussian [2] is not feasible due to fundamental differences in representation and output format—these methods operate on implicit 3D scenes (NeRFs, Gaussians) and produce render-only outputs, while GuideFlow3D works with explicit textured meshes. Additionally, they rely on per-instance optimization and often require multi-view supervision, whereas our method is training-free and fast at inference (please also refer to our response towards Reviewer dDnT Q4 for an explanation on how we can inherently address these two methods' limitations). While TEXTure [3] tackles the same task as us, we compare with EASI-Tex [4], a more recent faster and scalable approach shown to perform better than TEXTure.\n\nFollowing reviewer suggestions, we added three new baselines: (1) **EASI-Tex (SIGGRAPH 2024)** [4] is a conditional diffusion-based method that uses edge-aware conditioning and ControlNet to texture an existing 3D mesh from a single RGB image, (2) **Cross Image Attention (SIGGRAPH 2024)** [5] builds upon the self-attention layers of generative models and introduces a cross-image attention mechanism that implicitly establishes semantic correspondences across images; (3) **Mamba-ST (WACV 2025)** [6] adapts the behavior of State Space Models to simulate the behavior of cross-attention layers, which are able to combine two separate embeddings into a single output. Please note that (2) and (3) are 2D style transfer methods which we adapt to our use-case following the suggestion of Reviewer zJCJ. We conduct an experiment with image condition using $\\mathcal{L}_{\\text{appearance}}$ on the _simple-complex_ set (same setting as Main Paper: Tab. 1). Below are the results:\n\n| Method                 | Fidelity &darr; | Clarity &darr; | Integration &darr; | Quality &darr; |  Adaptation &darr; | Overall &darr; |\n|------------------------|----------------|-------------------|-------------------|----------------|------------------|-----------------|\n| Mamba-ST                | 3.34       | 3.43          | 3.47          | 3.52       | 3.46         | 3.45        |\n| Cross Image Attention | 2.61       | 2.64          | 2.69          | 2.71       | 2.68         | 2.66        |\n| EASI-Tex                | 1.78       | 1.83          | 1.900          | 1.89       | 1.85         | 1.82        |\n| GuideFlow3D                | **1.15**      | **1.14**          | **1.16**         | **1.21**       | **1.11**         | **1.06**        |\n\nGuideFlow3D achieves strong improvements in both appearance transfer and structural clarity preservation. This demonstrates the ability of our approach to effectively translate visual and semantic cues into coherent, high-fidelity textures across different 3D objects. We will add these results alongside comprehensive benchmarking evaluation and qualitative comparison with the baselines on all of our experimental settings to the final manuscript.\n\n> Q3: The prompts and scenarios used in the experimental evaluation lack complexity and diversity.\n\nThe main paper focuses on controlled intra- and inter-category furniture benchmarks to enable systematic evaluation. However, we also show the generalizability of GuideFlow3D in open-domain scenarios (Appendix D: In-the-wild Appearance Transfer). These examples involve transferring appearance across semantically and geometrically dissimilar object categories such as _giraffe → chair_ and _cabinet → airplane_ covering varied object domains including animals, tools, and vehicles (Appendix: Fig. 4). Despite the increased complexity and lack of semantic alignment, our method successfully preserves material styles, maintains structural coherence via part-aware matching in the latent feature space, and produces realistic outputs. This robustness is also noted by reviewers, **the in-the-wild appearance transfer in the Appendix looks great** (Reviewer 2LHX), and the method **tackles an important task… vital for large-scale applications** (Reviewer zJCJ), highlighting GuideFlow3D’s scalability towards complex meshes and scenarios. \n\n> Q4: The results shown typically use relatively clean input 3D meshes and clear appearance conditions.\n\nOur evaluation benchmark is designed for demonstrating content creation possibilities, hence we use clean input meshes to have a fair measure of performance. We also demonstrate the application of GuideFlow3D to real-world scenes in Appendix C: Scene Editing, where it stylizes reconstructed ScanNet objects with pose noise, occlusion, and class ambiguity. Without retraining or scene-level modeling, our method applies object-wise appearance transfer to maintain stability across pose and mesh variation highlighting its suitability for practical AR/VR scenarios and digital asset creation.\n\n\n[1] Liu, Kunhao, et al. \"Stylegaussian: Instant 3d style transfer with gaussian splatting.\" SIGGRAPH Asia 2024.\n\n[2] Liu, Kunhao, et al. \"Stylerf: Zero-shot 3d style transfer of neural radiance fields.\" CVPR 2023.\n\n[3] Richardson, Elad, et al. \"TEXTure: Text-Guided Texturing of 3D Shapes\". SIGGRAPH 2023.\n\n[4] Perla, Sai Raj Kishore et al. \"EASI-Tex: Edge-Aware Mesh Texturing from Single Image\", SIGGRAPH 2024.\n\n[5] Alaluf, Yuval et al. \"Cross-Image Attention for Zero-Shot Appearance Transfer\", SIGGRAPH 2024.\n\n[6] Botti, Filippo et al. \"Mamba-ST: State Space Model for Efficient Style Transfer\", WACV 2025."}, {"review_id": "OQw371PDQ2", "reviewer": "Reviewer_dDnT", "summary": "This paper addresses the task of 3D appearance transfer, focusing on scenarios where the input object (providing the geometry) and the appearance object (providing the style) have significant geometric differences. The authors propose GuideFlow3D, a training-free framework that operates by steering a pretrained rectified flow model at inference time. The method is built upon the Trellis generative model and its structured latent representation. \n\nThe core contribution is a guided sampling process that extends the concept of universal guidance to rectified flows. During the reverse flow, the sampling steps are periodically interleaved with optimization steps that incorporate a differentiable guidance loss. This allows the generation to be constrained by objectives for which the underlying model was not explicitly trained. The paper introduces two such guidance functions tailored for different conditions:\n\n1) a part-aware appearance loss, L_{appearance}, which is used for mesh-based appearance objects and leverages part-based co-segmentation to enforce localized correspondence, and \n2) a self-similarity loss, L_{structure}, a contrastive objective used for image or text-based appearance objects that preserves the intrinsic structure of the input mesh.\n\nTo evaluate their method, the authors created a new benchmark dataset and employed a GPT-4V-based ranking system, arguing that traditional metrics are unsuitable for this task. The experiments show that GuideFlow3D outperforms baselines both qualitatively and\nquantitatively according to this evaluation scheme.\n\nThe paper is interesting and good quality, my current reccomandation is temporary: a successful rebuttal that provides insights about human validation for the metric and a more thorough discussion/comparison with SOTA baselines could raise my score to a 4\n(borderline accept) or 5 (accept).", "questions": "- The reliance on GPT-4V as the sole quantitative evaluator is a major concern due to potential un-analyzed biases. Did the authors perform a small-scale human study (even with a limited number of participants, similar to the user study in the StyleRF paper ) to validate\nthe GPT-4V rankings against human judgments?\n\n- The paper's comparison is limited to a naive baseline and its own foundation model (Trellis). To better situate this work, could you please elaborate on the relationship between GuideFlow3D and other recent state-of-the-art 3D style transfer methods like StyleRF or StyleGaussian? What are the primary challenges in performing a direct comparison (e.g., differences in 3D representation from NeRF/Gaussians to meshes), and what are the conceptual advantages of your mesh-based guided flow approach over these methods? A\nconvincing argument here would help establish the significance of your work.\n\n-The Lappearance guidance relies heavily on the co-segmentation from PartField . How does GuideFlow3D's performance degrade if the segmentation quality is poor, for instance, when transferring appearance between two objects where PartField produces inconsistent or\nincorrect part labels (e.g., mapping a chair leg to an armrest)? Showing a failure case analysis would provide valuable insight into the method's robustness and practical limitations.\n\n- In Eq. (6), the self-similarity loss is a contrastive loss on geometric features that encourages parts within the same cluster to be similar. However, in appearance transfer, one might want to apply different textures to semantically distinct but geometrically similar parts\n(e.g., applying a fabric texture to a chair's seat cushion and a wood texture to its structurally similar backrest). How does this loss handle such cases, and does it risk homogenizing the texture across all geometrically similar regions, even if the text prompt implies variation?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. High Significance and Clear Motivation: The paper tackles a well-defined and important problem: transferring appearance between 3D objects with substantially different geometries. This is a known failure case for many existing 3D generative and style transfer methods, which often struggle with geometric inconsistencies orrequire strong structural similarity between source and target. The ability to stylize a\nsimple geometric primitive with the complex appearance of a detailed, but structurally different, object has clear and immediate applications in democratizing content creation for gaming, AR/VR, and rapid industrial design prototyping.\n2. Originality: The core technical contribution is original. The proposed method of integrating optimization-based guidance directly into the sampling process of a pretrained rectified flow model is a clever and powerful idea. This approach elegantly avoids the need for costly retraining or complex architectural modifications (e.g., ControlNet-like adapters), positioning it as a flexible, training-free solution. The logic\nis sound: directly applying the base generative model (Trellis) is insufficient for the task (Fig. 4b), while naive optimization of the guidance loss would push the latents out of the learned data distribution, resulting in unrealistic artifacts (Fig. 4a). The proposed interleaved optimization-flow scheme provides a balance between adherence to the guidance objective and the powerful generative prior of the pretrained model.\n3. Guidance Functions: The two proposed loss functions, L_{appearance} and L_{structure}, are well-motivated and tailored to their specific use cases. The use of part-based feature fields from PartField to drive the co-segmentation for L_{appearance} is a particularly strong design choice. It establishes a semantically meaningful correspondence between parts (e.g., chair leg to chair leg), which is far\nmore robust than naive nearest-neighbor matching in a generic latent space, a claim strongly supported by the ablation stud. Similarly, the contrastive self-similarity loss is a well-founded approach for preserving an object's intrinsic structure when only non-geometric guidance (image or text) is available, drawing on established concepts from the literature.\n\nWeaknesses:\n1. Reliance on an Un-validated and Potentially Biased Evaluation Metric: The paper's quantitative evaluation rests entirely on a GPT-4V/4o-based ranking system. While the authors correctly argue that traditional metrics like FID or LPIPS are ill-suited for this task, their chosen alternative introduces serious methodological concerns. Relying on a large language model as the sole measure of quality is a major weakness without rigorous validation. As recognized by the authors in the supplementary, recent studies have highlighted that LLMs can exhibit significant biases. The paper presents the GPT-based rankings as objective fact without a crucial validation step: a small-scale human user study to measure the correlation between the LLM's judgments and those of human evaluators.\n2. Limited Comparison to State-of-the-Art Baselines: The experimental comparison is limited to a simple UV mapping baseline and Trellis , the very model that GuideFlow3D is built upon. This is not sufficient to properly contextualize the work within the broader field of 3D style transfer. The field has seen rapid progress with methods based on different 3D representations, such as NeRFs (e.g., StyleRF ) and\n3D Gaussian Splatting (e.g., StyleGaussian ). While these methods may have different input/output formats, a thorough discussion of their relationship to GuideFlow3D, and the conceptual trade-offs, is missing. The current experiments only prove that adding guidance improves Trellis; they do not establish that GuideFlow3D is superior to, or even competitive with, other contemporary state-of-the-art techniques. This is a critical omission for a top-tier conference paper.\n3. Narrow Experimental Scope and Unsubstantiated Generalization: The experiments are conducted almost exclusively on furniture items (chairs, tables, etc.). While the appendix shows also good quality examples on more general categories, a deeperquantitative generalization study would be crucial for evaluating the generalizability and real-world applicability of the approach.", "was_revised": true, "final_justification": "Having read the authors rebuttal I agree on increasing my score to 4 (borderline accept). I acknoledge the work done to improve the evaluation and comparison, but part of the concerns about the possibly biased evaluation remains as well as the limited scope of the forniture scenarion.", "rebuttal": "We thank Reviewer dDnT for their detailed review and for recognizing the **high significance and clear motivation** of our work, the **originality** of our training-free framework, and the **clever and powerful nature of our interleaved optimization-flow scheme**. We also appreciate the acknowledgment of our **well-motivated and tailored guidance functions** and the flexibility our method offers by **avoiding the need for costly retraining or complex architectural modifications**. Below, we address the concerns:\n\n> Q1: Reliance on an Un-validated and Potentially Biased Evaluation Metric.\n\nTraditional metrics like CLIP similarity, LPIPS, or FID fail to capture semantic consistency, geometry-aware stylization, or perceptual realism in cross-category 3D appearance transfer [1]. Recent works across 3D generation and stylization tasks have shown that GPT-4V exhibits strong alignment with human preferences [50, 67, 43, 76], and is increasingly adopted as a practical tool for perceptual evaluation in absence of ground truth. However, we agree that without rigorous validation, relying solely on an LLM can be a major weakness. Based on the suggestion, we conducted a user study on Amazon Mechanical Turk with 21 participants using 51 randomly selected object and image pairs from the _simple-complex_ evaluation set, on the image condition setting. The participants were shown the appearance image and two views (front and back at a camera angle of 45 degrees) each of the structure mesh + outputs from different methods, without any knowledge about the methods. Then, they were provided with the same prompts as GPT (Appendix: Fig 6) and asked to rate the best performing considering overall quality. Based on 1070 responses, GuideFlow3D achieved the highest win rate (34.8%) compared to Cross Image Attention (26.7%), EASI-Tex (21.6%), and Mamba-ST (16.9%), consistent with the GPT-4V rankings (please refer to Q2 for a discussion on the baselines and GPT evaluation). These findings validate the reliability of our LLM-based evaluation, demonstrating strong alignment with human perception in assessing texture fidelity, structural preservation, and overall appearance quality. We will include a summary of this user study in the main paper and provide full details in the appendix.\n\n> Q2: Limited Comparison to State-of-the-Art Baselines.\n\nFollowing reviewer suggestions, we added three new baselines: (1) **EASI-Tex (SIGGRAPH 2024)** [1] is a conditional diffusion-based method that uses edge-aware conditioning and ControlNet to texture an existing 3D mesh from a single RGB image, (2) **Cross Image Attention (SIGGRAPH 2024)** [2] builds upon the self-attention layers of generative models and introduces a cross-image attention mechanism that implicitly establishes semantic correspondences across images; (3) **Mamba-ST (WACV 2025)** [3] adapts the behavior of State Space Models to simulate the behavior of cross-attention layers, which are able to combine two separate embeddings into a single output. Please note that (2) and (3) are 2D style transfer methods which we adapt to our use-case following the suggestion of Reviewer zJCJ. We conduct an experiment with image condition using $\\mathcal{L}_{\\text{appearance}}$ on the _simple-complex_ set (same setting as Main Paper: Tab. 1). Below are the results:\n\n| Method                 | Fidelity &darr; | Clarity &darr; | Integration &darr; | Quality &darr; |  Adaptation &darr; | Overall &darr; |\n|------------------------|----------------|-------------------|-------------------|----------------|------------------|-----------------|\n| Mamba-ST                | 3.34       | 3.43          | 3.47          | 3.52       | 3.46         | 3.45        |\n| Cross Image Attention | 2.61       | 2.64          | 2.69          | 2.71       | 2.68         | 2.66        |\n| EASI-Tex                | 1.78       | 1.83          | 1.900          | 1.89       | 1.85         | 1.82        |\n| GuideFlow3D                | **1.15**      | **1.14**          | **1.16**         | **1.21**       | **1.11**         | **1.06**        |\n\nGuideFlow3D achieves strong improvements in both appearance transfer and structural clarity preservation. This demonstrates the ability of our approach to effectively translate visual and semantic cues into coherent, high-fidelity textures across different 3D objects. We will add these results alongside comprehensive benchmarking evaluation and qualitative comparison with the baselines on all of our experimental settings to the final manuscript.\n\n> Q3: Narrow Experimental Scope and Unsubstantiated Generalization.\n\nWe agree that generalizability is important and appreciate the reviewer noting the quality of our open-domain examples. Our focus on furniture categories in the main paper is intentional, driven by practical downstream applications (e.g., AR/VR, asset libraries) where editable 3D meshes are commonly used. This also enables controlled, semantically coherent benchmarks, making user studies and GPT-based evaluations more interpretable and consistent. We also provide in-the-wild transfer results (Appendix: Fig 4) across diverse categories demonstrating that GuideFlow3D generalizes to semantically and geometrically dissimilar objects. We will make this motivation clearer in the revised manuscript.\n\n> Q4: To better situate this work, could you please elaborate on the relationship between GuideFlow3D and other recent state-of-the-art 3D style transfer methods like StyleRF or StyleGaussian?\n\nRecent methods like StyleRF [4] and StyleGaussian [5] represent important progress in 3D style transfer. However, these approaches operate on implicit or point-based representations (NeRFs, Gaussians), whereas GuideFlow3D works on explicit textured meshes, enabling direct editing, part-aware control, and downstream usability. Notably, StyleGaussian stylizes only color while keeping geometry fixed, limiting its ability to reflect fine structural patterns. It also scales poorly with the number of Gaussians due to memory and compute constraints. In contrast, GuideFlow3D offers geometry-aware stylization with training-free inference, and fixed compute cost via structured latent sampling. We will revise the related works section with such conceptual tradeoffs, highlighting how mesh-based controllable appearance transfer can be useful.\n\n> Q5: How does GuideFlow3D’s performance degrade if the segmentation quality is poor?\n\nWhile the co-segmentation based objective is designed using PartField, in practice, we get the\nPartField feature vectors per 3D point/voxel and run K-means clustering. Instead of PartField\nfeatures, we can also use SLAT features for clustering (Main Paper: Tab. 3, Ablation Study). While a slight\nperformance drop is expected, our method does not rely on 1-1 correspondences, more on approximate\npart matching across objects. As shown with in-the-wild appearance transfer results (Appendix: Fig 4), we are able to\nsupport diverse object categories, for example, patterns from the body of the giraffe are transferred to\nthe seat, arms, and backrest of the chair, while the appearance of giraffe legs is transferred to chair\nlegs. However, in extreme diversity cases with no semantic meaning between two meshes, the co-segmentation can fail due to inconsistent features, either SLAT or PartField. We will add qualitative analysis of co-segmentation failure cases to our manuscript for the final version.\n\n> Q6: How does the self-similarity loss handle such cases, and does it risk homogenizing the texture across all geometrically similar regions, even if the text prompt implies variation?\n\n$\\mathcal{L}_{\\text{structure}}$ encourages intra-cluster consistency and inter-cluster contrast in appearance by operating on geometry-derived feature clusters. However, it does not enforce global texture uniformity across all geometrically similar parts. Instead, the contrastive formulation (Eq. 6) preserves local appearance consistency while allowing global variation across different clusters. Clustering is performed in the latent geometry-feature space, which captures more than raw shape similarity (e.g., spatial location, curvature context). In practice, this often separates semantically distinct parts even if they share structural traits.\n\n[1] Perla, Sai Raj Kishore et al. \"EASI-Tex: Edge-Aware Mesh Texturing from Single Image\", SIGGRAPH 2024.\n\n[2] Alaluf, Yuval et al. \"Cross-Image Attention for Zero-Shot Appearance Transfer\", SIGGRAPH 2024.\n\n[3] Botti, Filippo et al. \"Mamba-ST: State Space Model for Efficient Style Transfer\", WACV 2025.\n\n[4] Liu, Kunhao, et al. \"Stylerf: Zero-shot 3d style transfer of neural radiance fields.\" CVPR 2023.\n\n[5] Liu, Kunhao, et al. \"Stylegaussian: Instant 3d style transfer with gaussian splatting.\" SIGGRAPH Asia 2024."}, {"review_id": "5Hqvytlvzm", "reviewer": "Reviewer_2LHX", "summary": "The authors propose GuideFlow3D, an optimization based method that tries to edit the latent representation of a pretrained rectified flow model to edit the appearance of 3D assets. The authors propose two different types of guidance including part-aware losses and self-similarity. With the proposed guidance, the authors are able to transfer the texture and geometric details of one 3D asset to another. Experiments are conducted on 5 rigid object categories. The qualitative results show that the proposed method outperforms the baseline in different Ranking metrics.", "questions": "The key points are addressed in the weakness section. I will adjust my rating if the authors answer my questions properly.\n\n\nI have some questions for detail clarification:\n\n1. The paper mentions that the objects are mainly in canonical pose, how is the scene editing in Scene Appendix C implemented? Do you use GT pose or another off-the-shelf pose estimator?\n\n2. Will object categories have big impacts on co-segmentation based objective?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n**Detailed Explanations** The authors explain the proposed method very well in the method section. Specifically, the authors provide enough details for the two proposed guidance, which makes the paper easy to follow and reimplement. \n\n**Good Visual Quality** The authors provide good qualitative results for the proposed method. Noticeably, the in-the-wild appearance transfer in the Appendix looks great. \n\n### Weaknesses\n\nThe biggest concern of the paper is that the proposed method is an application extension of the paper [14]. The goal for [14] is 3D generation , and the proposed method  aims for another direction: editing the latent representation of one object so that it matches the appearance of another one. \n\nThe novelty of the paper potentially lies in 1) how to obtain the latent representation and 2) how to edit the latent representation. Since the pretrained model is adopted, the authors should make efforts to validate the novelty in the second point. However, in the experiment section, the authors still use [14] as one of the main baseline, which is not sufficient to validate the novelty of the paper.\n\nIn general, image/object editing is a popular topic. I suggest the authors could adopt the editing method from another paper (even though the object representation could be different) and make comparison with the proposed method.\n\n\n### Minors\n\nI would suggest adding some image pairs in Fig 4 of Appendix to the main paper.", "was_revised": true, "final_justification": "The authors' rebuttal address my concerns about baselines and the contribution of the work. I raise my rating accordingly.", "rebuttal": "We thank Reviewer 2LHX for highlighting the **detailed explanations for easy reimplementation**, **good visual quality**, and noting that the **in-the-wild appearance transfer in the Appendix (Fig 4) looks great**. Below, we address all concerns: \n\n> Q1: The authors could adopt the editing method from another paper (even though the object representation could be different) and make comparison with the proposed method.\n\nFollowing reviewer suggestions, we added three new baselines: (1) **EASI-Tex (SIGGRAPH 2024)** [1] is a conditional diffusion-based method that uses edge-aware conditioning and ControlNet to texture an existing 3D mesh from a single RGB image, (2) **Cross Image Attention (SIGGRAPH 2024)** [2] builds upon the self-attention layers of generative models and introduces a cross-image attention mechanism that implicitly establishes semantic correspondences across images; (3) **Mamba-ST (WACV 2025)** [3] adapts the behavior of State Space Models to simulate the behavior of cross-attention layers, which are able to combine two separate embeddings into a single output. Please note that (2) and (3) are 2D style transfer methods which we adapt to our use-case following the suggestion of Reviewer zJCJ. We conduct an experiment with image condition using $\\mathcal{L}_{\\text{appearance}}$ on the _simple-complex_ set (same setting as Main Paper: Tab. 1). Below are the results:\n\n| Method                 | Fidelity &darr; | Clarity &darr; | Integration &darr; | Quality &darr; |  Adaptation &darr; | Overall &darr; |\n|------------------------|----------------|-------------------|-------------------|----------------|------------------|-----------------|\n| Mamba-ST                | 3.34       | 3.43          | 3.47          | 3.52       | 3.46         | 3.45        |\n| Cross Image Attention | 2.61       | 2.64          | 2.69          | 2.71       | 2.68         | 2.66        |\n| EASI-Tex                | 1.78       | 1.83          | 1.900          | 1.89       | 1.85         | 1.82        |\n| GuideFlow3D                | **1.15**      | **1.14**          | **1.16**         | **1.21**       | **1.11**         | **1.06**        |\n\nGuideFlow3D achieves strong improvements in both appearance transfer and structural clarity preservation. This demonstrates the ability of our approach to effectively translate visual and semantic cues into coherent, high-fidelity textures across different 3D objects. While our method leverages the pretrained Trellis model, the core novelty lies in our training-free inference-time optimization framework, which combines universal guidance with structured latent flow sampling. This is enabled by our two proposed guidance objectives—part-aware appearance loss and self-similarity loss—specifically tailored to handle geometric dissimilarity in 3D appearance transfer. The strong performance over both 2D and 3D baselines empirically validates this contribution. We also conducted a user study to measure the correlation between the LLM’s judgments and those of human evaluators  and found that GPT-4 performs comparably to humans. Please refer to our response to Reviewer dDnT Q1 for a detailed explanation. We will add these results alongside comprehensive benchmarking evaluation and qualitative comparison with the baselines on all of our experimental settings to the final manuscript.\n\n\n\n> Q2: How is the scene editing in Scene Appendix C implemented? Do you use GT pose or another off-the-shelf pose estimator?\n\nWe use an off-the-shelf CAD model retrieval and alignment algorithm to get per-object pose estimations and obtain 3D mesh geometries of objects in ScanNet [4, 5].\n\n> Q3: Will object categories have big impacts on co-segmentation based objective?\n\nWhile the co-segmentation based objective is designed using PartField, in practice, we get the PartField feature vectors per 3D point/voxel and run K-means clustering. Instead of PartField features, we can also use SLAT features for clustering (Main Paper: Tab. 3, Ablation Study). While a slight performance drop is expected, our method does not rely on 1-1 correspondences, more on approximate part matching across objects. As shown with in-the-wild appearance transfer results (Appendix: Fig 4), we are able to support diverse object categories, for example, patterns from the body of the\ngiraffe are transferred to the seat, arms, and backrest of the chair, while the appearance of giraffe legs\nis transferred to chair legs.\n\n> Q4:  I would suggest adding some image pairs in Fig 4 of Appendix to the main paper.\n\nThank you for the suggestion, we will revise the final manuscript with some in-the-wild appearance transfer results in the main paper. \n\n[1] Perla, Sai Raj Kishore et al. \"EASI-Tex: Edge-Aware Mesh Texturing from Single Image\", SIGGRAPH 2024.\n\n[2] Alaluf, Yuval et al. \"Cross-Image Attention for Zero-Shot Appearance Transfer\", SIGGRAPH 2024.\n\n[3] Botti, Filippo et al. \"Mamba-ST: State Space Model for Efficient Style Transfer\", WACV 2025.\n\n[4] Stekovic, Sinisa et al. \"Pytorchgeonodes: Enabling differentiable shape programs for 3d shape reconstruction.\" CVPR 2025.\n\n[5] Ainetter, Stefan et al. \"Automatically annotating indoor images with cad models via rgb-d scans\". WACV 2023."}, {"review_id": "1oRy34TKK5", "reviewer": "Reviewer_zJCJ", "summary": "This paper tackles the task of transferring appearance specified by images or texts to a given 3D geometry. For this, it proposes a training-free approach that is based on a pre-trained generative model, i.e., TRELLIS [14]. Specifically, the authors incorporate the universal guidance [56] scheme into the pre-trained model's sampling. Namely, during each flow matching step, the authors find an optimized latent that minimizes losses based on 1) part-aware appearance loss, or 2)self-similarity-based geometry loss (Sec. 4.1). Experiments on a newly-proposed dataset verify the effectiveness of the proposed approach according to GPT-4V ranking.", "questions": "I do not fully get what the baseline \"TRELLIS\" in L258 means:\n> Trellis enables detailed local texture transfer using the appearance condition with structured latents generation, and serves as our baseline without guidance.\n\nCan authors clarify?", "limitations": "My main concern is that the baselines are too weak.\n\nThough there are not many 3D appearance transfer works, there are tons of works that excel on 2D style transfer. Thus, I think the following pipeline is a competitive one:\n1. Render the given mesh in one or multiple views (we can also easily obtain masks since the geometry is given)\n2. Use state-of-the-art 2D style transfer model to change the appearance in the 2D rendered image. For example,\n    1. https://github.com/garibida/cross-image-attention\n    2. https://github.com/diyiiyiii/StyTR-2\n    3. https://github.com/silky1708/local-style-transfer\n    4. https://github.com/FilippoBotti/MambaST\n3. For text-based one, we can first use text-to-image to generate appearance 2D reference and then use style transfer to generate 2D image\n4. Run TRELLIS image-conditioned model to output 3D assets\n     1. If we want to keep the mesh we can also keep the active voxel positions\n\nFrankly speaking, even 7 years ago, there were works that conducted 2D-to-3D style transfer, e.g., https://github.com/hiroharu-kato/style_transfer_3d. Please compare and provide suitable credits.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. Quality-wise: the qualitative results in the paper are of high quality;\n2. Clarity-wise: the paper is well-written and easy to follow;\n3. Significance-wise: the paper tackles an important task of editing 3D assets, which is vital for large-scale applications;\n4. Originality-wise: the proposed approach that is training-free and based on universal guidance is interesting and looks promising.", "was_revised": true, "final_justification": "After carefully reading the authors' responses and other reviews, I still think this is an interesting paper that tackles the task of appearance transfer across 3D assets. The rebuttal also added new baselines that include 2D style transfer, making the paper more comprehensive. Thus, I lead towards acceptance.", "rebuttal": "We thank Reviewer zJCJ for their positive feedback, highlighting the **high quality** of our results, the **well-written and easy to follow** presentation, and the **interesting and promising** nature of our training-free, universal guidance-based approach to the **important task of editing 3D assets… vital for large-scale applications**. Below we address all concerns:\n\n> Q1: My main concern is that the baselines are too weak. Though there are not many 3D appearance transfer works, there are tons of works that excel on 2D style transfer.\n\n\nFollowing reviewer suggestions, we added three new baselines: (1) **EASI-Tex (SIGGRAPH 2024)** [1] is a conditional diffusion-based method that uses edge-aware conditioning and ControlNet to texture an existing 3D mesh from a single RGB image, (2) **Cross Image Attention (SIGGRAPH 2024)** [2] builds upon the self-attention layers of generative models and introduces a cross-image attention mechanism that implicitly establishes semantic correspondences across images; (3) **Mamba-ST (WACV 2025)** [3] adapts the behavior of State Space Models to simulate the behavior of cross-attention layers, which are able to combine two separate embeddings into a single output. Please note that (2) and (3) are 2D style transfer methods which we adapt to our use-case following your suggestion. We conduct an experiment with image condition using $\\mathcal{L}_{\\text{appearance}}$ on the _simple-complex_ set (same setting as Main Paper: Tab. 1). Below are the results:\n\n| Method                 | Fidelity &darr; | Clarity &darr; | Integration &darr; | Quality &darr; |  Adaptation &darr; | Overall &darr; |\n|------------------------|----------------|-------------------|-------------------|----------------|------------------|-----------------|\n| Mamba-ST                | 3.34       | 3.43          | 3.47          | 3.52       | 3.46         | 3.45        |\n| Cross Image Attention | 2.61       | 2.64          | 2.69          | 2.71       | 2.68         | 2.66        |\n| EASI-Tex                | 1.78       | 1.83          | 1.900          | 1.89       | 1.85         | 1.82        |\n| GuideFlow3D                | **1.15**      | **1.14**          | **1.16**         | **1.21**       | **1.11**         | **1.06**        |\n\nGuideFlow3D achieves strong improvements in both appearance transfer and structural clarity preservation. This demonstrates the ability of our approach to effectively translate visual and semantic cues into coherent, high-fidelity textures across different 3D objects. We also conducted a user study to measure the correlation\nbetween the LLM’s judgments and those of human evaluators  and found that GPT-4 performs comparably to humans. Please refer to our response to Reviewer dDnT Q1 for a detailed explanation. We will add these results alongside comprehensive benchmarking evaluation and qualitative comparison with the baselines on all of our experimental settings to the final manuscript.\n\nWe appreciate your suggestions for adapting 2D style transfer methods to 3D and, if desired, we will also provide the text-based results during the discussion phase, as a way to better situate the context of our work in the evolving editing landscape.\n\n> Q2: I do not fully get what the baseline \"TRELLIS\" in L258 means.\n\nSince TRELLIS supports flexible local editing based on their manuscript, the idea of this baseline is to understand how the pretrained model performs without any guidance. We pass the input image/text condition and the active voxels intersecting with the surface of the input 3D object mesh (structure) to the structured latents (SLATs) generation (second stage) of TRELLIS model to generate the output 3D asset.\n\n\n[1] Perla, Sai Raj Kishore et al. \"EASI-Tex: Edge-Aware Mesh Texturing from Single Image\", SIGGRAPH 2024.\n\n[2] Alaluf, Yuval et al. \"Cross-Image Attention for Zero-Shot Appearance Transfer\", SIGGRAPH 2024.\n\n[3] Botti, Filippo et al. \"Mamba-ST: State Space Model for Efficient Style Transfer\", WACV 2025."}]

Author rebuttal:

Meta-review: This paper proposes a training-free method for transferring the appearance of an image onto a 3D mesh. The method leverages a pre-trained generative model together with a generative model guidance strategy, and introduces part-aware and self-similarity loss functions.

Reviewers raised concerns about the novelty, the rigor of the evaluation, and the lack of discussions and comparison with the latest baselines. The rebuttal addressed many of these issues by providing additional experiments and clarifications. However, concerns remain regarding the rigor of using GPT as a judge, even though additional (small-scale) user studies were provided. AC shares this concern and believes the evaluation should be more rigorous. In addition, the paper should explicitly discuss and compare with more recent methods such as [a].

Given that the paper currently holds an average rating of 4.25, which is a positive score, and the introduced loss for the optimization framework is new, AC recommends acceptance but request a more rigorous evaluation to be included in the revision, including but not limited to a large-scale user study, different LLMs, multiple trials to reduce the uncertainty, and metrics like CLIPScore.

[a] TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion
Decision: Accept (poster)