Wukong's 72 Transformations: High-fidelity Textured 3D Morphing via Flow Models
Authors: Minghao Yin
Yukang Cao
Kai Han
Venue: NeurIPS 2025 poster ()
Keywords: 3D morphing
3D interpolation
Rectified flow
Primary area: applications
TL;DR: A training-free framework for high-fidelity textured 3D morphing via Flow Models.

Abstract: We present WUKONG, a novel training-free framework for high-fidelity textured 3D morphing that takes a pair of source and target prompts (text or images) as input. Unlike conventional methods -- which rely on manual correspondence matching and deformation trajectory estimation (limiting generalization and requiring costly preprocessing) -- WUKONG leverages the generative prior of flow-based transformers to produce high-fidelity 3D transitions with rich texture details. To ensure smooth shape transitions, we exploit the inherent continuity of flow-based generative processes and formulate morphing as an optimal transport barycenter problem. We further introduce a sequential initialization strategy to prevent abrupt geometric distortions and preserve identity coherence. For faithful texture preservation, we propose a similarity-guided semantic consistency mechanism that selectively retains high-frequency details and enables precise control over blending dynamics. This empowers WUKONG to support both global texture transitions and identity-preserving texture morphing, catering to diverse generation needs. Through extensive quantitative and qualitative evaluations, we demonstrate that WUKONG significantly outperforms state-of-the-art methods, achieving superior results across diverse geometry and texture variations.

Wukong's 72 Transformations: High-fidelity Textured 3D Morphing via Flow Models
Minghao Yin
Yukang Cao Kai Han
Visual AI Lab, The University of Hong Kong LS-Lab, Nanyang Technological University kaihanx0hku, hk
yinnh0connect. hku hk
yukang. caolntu. yuk edu.sg
SOUTCE
Langel gellery
High-fidelity textured 3D morphing of Wukong. Taking an image of Wukong (bottom left) as the source and image of another character (bottom right) the target, we demonstrate two types of textured 3D morphing by our method: (i) Purple arrows indicate texture-controlled morphing, where the geometric structure changes while preserving detailed textures from the source; (ii) Green arrows indicate textured 3D morphing guided by the target prompt.
Figure 1:
Abstract
We present WUKONG, a novel training-free framework for high-fidelity textured 3D morphing that takes pair of source and target prompts (image or text) as input. Unlike conventional methods- and deformation trajectory estimation (limiting generalization and requiring costly preprocesiing--Weeeee WUKKNG leverages the generative prior of flow-based transform- ers to produce high-fidelity 3D transitions with rich texture details. To ensure smooth shape transitions, we exploit the inherent continuity of flow-based genera- tive processes and formulate morphing We further introduce a sequential initialization strategy to prevent abrupt geometric distortions and preserve identity coherence. For faithful texture preservation, propose a similarity-guinn tains high-frequency details and enables precise control over blending dynamies. This empowers WUKONG to support both global texture transitions and identity- preserving texture morphing, catering to diverse generation needs. Extensive quan- titative and qualitative evaluations demonstrate that WUKONG significantly outper-
rely on manual correspondence matching
optimal transport barycenter problem.
semantic consistency mechanism that selectively re-
*Coresponniigg author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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forms state-of-ther the-art methods, achieving superior results across diverse geometry and texture variations. Project page: https://visual ai gi github. , io sukong
Introduction
3D morphing techniques (Seitz and Dyer, 1996; Shechtman et al., 2010; Tsai et al., 2022; Kim et al., 2024) aim to produce smooth transitions between the source object and target object by gradually altering 3D attributes such as shape and texture. These methods have broad applications in gaming. animation, and cinematic transitions. Existing approaches primarily focus on geometric transformations, relying on correspondence matching (Deng et al., 2023) and deformation trajectory estimation (Eisenberger et al., 2021). However, most existing methods are limited to untextured 3D meshes, leaving textured 3D morphing a largely under-explored problem. Moreover, their effectiveness is constrained by the limited availability of large-scale 3D datasets with source-target pairs, often resulting in unsatisfactory outputs when applied to unseen data. To bridge this gap, we introduce WUKONG, a method for high-fidelity textured 3D morphing from a pair of image or text prompts. This name is inspired by the legendary character Wukong and his 72 earthly transformations, as exemplified by the morphing result in Fig. 1.
With recent advances in large-scale 3D generation and reconstruction (Xiang et al., 2024; Tochilkin et al., 2024; Li et al., 2025), one may consider to address the 3D morphing problem by training a feed-forward network to model the transitions between two input objects. However, constructing paired 3D data for training based on existing large-scale 3D data (Deitke et al., 2024) non-trivial, both technically and economically. Therefore, such a straightforward approach is infeasible.
Recently, 2D image morphing has achieved remarkable success (Qiyuan et al., 2024; Zhang et al., 2024a), driven by advances in 2D diffusion models (e.g.. Stable Diffusion (Rombach et al., 2022)). Inspired by this progress, we aim to extend these successes to 3D morphing not only shapes but also textures - without relying on large-scale paired data. In other words, we aim to develop training- free framework for textured 3D morphing by leveraging the strong priors of generative models. However, building such a framework poses significant challenges due to the absence of large-scale 3D data with continuous shape and texture transitions.
In this paper, we propose WUKONG, a novel framework for high-fidelity textured 3D morphing that takes image text pairs as input delineate source and target objects. Built upon pre-trained flow-based transformer (Xiang et al., 2024) for 3D generation, WUKONG bridges the gap between 3D shapes and image/text conditions. Leveraging the deterministic property of flow models (where intermediate states are not stochastic), we derive morphing trajectories by solving a free-support Wasserstein barycenter problem. Additionally, we introduce sequential initialization strategy to enhance the smoothness of the transitions along the barycentric trajectory. This design also allows us to handle the texture morphing in 3D in a similar fashion. However, naive interpolation treats all texture regions uniformly, which may not satisfy the need for preserving specific source attributes, such as fine details, distinct styles, or identity patterns. To address this, we propose a similarity-gun guided consistency mechanism for texture controlled morphing, which selectively preserves high- frequency texture details while providing finer control over transition dynamics.
The main contributions of this work are as follows: (i) high-fidelity textured 3D morphing that takes pair of image or text prompts as input. By leveraging a flow-based generative model as prior, WUKONG enables smooth and controllable shape and texture interpolation. (ii) We introduce method derive faithful intermediate morphing states by solving an optimal transport barycenter problem, further augmented by a sequential initialization strategy to facilitate smooth transitions. (iii) We propose a similarity-guidee mechanism to enable texture controlled morphing, allowing users to selectively preserve high- frequency texture details and exercise fine-gra ine-grained control over appearance transitions. (iv) Through extensive experiments on diverse 3D morphing varying geometry and significant texture changes- we demonstrate that WUKONG outperforms existing methods, establishing a new state-of-the-arr in textured 3D morphing.
We propose WUKONG, a novel framework for
semantic consistency
different object categories with
2 Related work
2D morphing Classical image morphing (Liao et al., 2014; Beier and Neely, 1992; Darabi et al., 2012; Shechtman et al., 2010) typically involves three key steps: (1) finding feature-based correspon- dence; (2) mapping between two images using optimization frameworks; and (3) auxiliary techniques
2

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to ensure smooth transitional continuity. However, strong priors and limited model expressiveness often lead to ghosting artifacts. With growing data availability, data-driven morphing methods (Fish et al., 2020; Averbuch-Flor et al., 2016) shifted from explicit mappings to learning over dataset distributions using deep models. However, their generalization is limited by dataset-specific training. Recent approaches like DiffMorpher (Zhang et al., 2024a) address this by leveraging pre-trained diffusion models, enabling more flexible morphing. DiftMorpher achieves morphing by interpolating noise, conditional inputs, and selectively blending model parameters, enabling strong shape and texture transitions. Inspired by this idea, we propose a novel method that replaces linear interpolation with optimal transport- based strategy, offering a more principled and effective interpolation of latent conditions.
3D generation To achieve 3D generation, two dominant paradigms have emerged: (1) Distilling useful priors from pretrained generative models into 3D feed-forward reconstruction algorithm; (2) Training a unified 3D generative model from scratch. In the first paradigm, knowledge distillation from large generative models occurs via gradient-based fine-tunes 2D models to generate multi-view images (Yu et al., 2024; Shi et al., 2024; Shriram et al., 2025), which are then reconstructed into 3D using methods like Gaussian splatting (Kerbl et al., 2023). Gradient distillation, exemplified by Score Distillation Sampling (Poole et al., 2023), guides 3D optimization directly. However, both lack latent 3D space, limiting structural control. To bridge this gap, native 3D generative models have emerged (Lan et al., 2025; Zeng et al., 2022; Zhang et al., 2024b), typically combining VAE for dimensionality reduction with a 3D generative model. However, most are limited to either explicit (e-g-, point clouds, voxels, meshes) or implicit (e.g.. neural fields, Gaussians) formats. Trellis (Xiang et al., 2024) addresses this by introducing a Structured Latent Representation (SLAT) for flexible multi-format generation. We inherit Trellis's s versatility to support diverse 3D modalities.
data-based methods. Data distillation
3D morphing Early 3D morphing research centered on shape transition (Tam et al., 2013), following a three-step pipeline: finding correspondence, modeling the mapping, and refinement. Classical methods employed techniques like Wasserstein distance (Tsai al., 2022: Solomon et al., 2015; Sorkine and Alexa, 2007; Ren et al., 2020; Eisenberger et al., 2021). Despite their contributions, these methods suffer from oversimplified assumptions and high input sensitivity. Recent learning-bawe methods improve robustness by integrating foundation model priors. NSSM (Morreale et al., 2024) uses DINOv2 (Oquab et al., 2023) for 3D correspondence: SRIF (Sun al., 2024) incorporates a diffusion prior from DiffMorpher (Zhang et al., 2024a). Emerging works (Gao et al., 2023; Yang et al., 2025; Michel et al., 2022) explore text-driven morphing using CLIPP (Radford et al., 2021), though limited by CLIP's coarse latent space. Other efforts leverage topology-aligned datasets (Eisenberger et al., 202.) 1) with in-domain training to enhance semantic consistency (Yumer et al., 2015). Shape- only 3D morphing has limited practical use without texture and appearance cues. A recent concurrent work (Yang et al., 2025) explores morphing textured 3D representations using generative models, bypassing explicit correspondence computation. However, it mainly focuses on parameter fusion and pays less attention to latent condition interpolation. In contrast, our method emphasizes a principled interpolation schedule for latent conditions. Additionally, we adopt flow- dow-based model with unified 3D representation, enabling ficxible output across diverse formats.
3
Method
3.1
Overview and formulation
Given two input prompts (Poouce Purge) in image text form, method learns interpolation function I to generate a coherent sequence of 3D textured meshes G [G+ which forms a smooth morphing trajectory between the two concepts. Here, a E {0..... + +1} denotes the discrete interpolation step index. Among them, Go and G denote the 3D generation of the input prompts P Psource and Purgett respectively. Formally, the problem can be formulated as:
G. (PPsaE(()rrree))) { ((Punges),
where & denotes flow-based 3D generator, & represents the encoders that embed the prompts into the latent space. For feature extraction, we use DINOv2 (Oquab et al., 2023) as the encoder E for image prompts and CLIP (Radford al., 2021) for textual inputs.
In the rest of this section, we will discuss the details of our employed flow-based 3D generator & and how we formulate the interpolation function I. From a general perspective, an effective interpolation
3

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Controlable lexhuee
Testue: ornnllled ID morphing
Docoulrr
Cnosd mlsiion
Source
norphing
Deceder
y- boli
Boyoe
sale
spintaak
Torget
Liase
Baryarar
Deodae roults
inttialiatioon
Figure 2: Our WUKONG framework. Given source and target (image or tex(), we extract features using pretrained encoders and treat the condition tokens as empirical distributions. We compute their Wasserstein barycenter with weights (1 B) and to obtain interpolated condition tokens Z. These tokens are fed into a shared geometry flow model &geo and texture flow model Ptez to generate 3D outputs different values, producing textured 3D morphs. The top-right shows our texture-controlled morphing branch, and the bottom-right illustrates the recursive initialization strategy.
strategy in 3D morphing should satisfy two key requirements: (1) smooth shape transitions that preserve identity and prevent abrupt changes, and (2) controllable texture blending. As depicted in Fig. 2, we propose to model these two properties with (1) shape interpolation via optimal transport barycenter and (2) controllable texture generation via selective interpolation.
3.2 Flow-based 3D generator
Recently, Trellis (Xiang et al., 2024) advances 3D generation by introducing unified structured latent representation via rectified flow transformers (Xiang et al., 2024). It achieves high-fidelity outputs from image or text inputs by training test time separated into three distinct phases:
pipeline, with the generation process at
(I) Geometric structure generation that generates sparse structure p {pi from the condition C. which either text-embedded CLIP features (Radford et al., 2021) image-encoded DINOv2 features (Oquab et al., 2023):
{p = gen t);
(2)
where + geo denotes the flow transformer backhone, /. the timestep, and I. represents the number of active voxels.
(2) Textured latent generation that generates latents h (hi given the structure {p.
(1:) F. Diex (p),
where (ax) the positional embedded points.
(3) Latent decoder that decodes the structured latents = {(hi, into a 3D representation, which we opt for meshes our experiments:
Dmeshh (8)
where w/ E R215 are the flexible parameters in FlexiCubes (Shen et al., 2023) and de E R are signed distance values for the eight vertices of the corresponding voxel. Note that our employed 3D generator (Xiang et al., 2024) also supports output both 3D Gaussian Splatting (Kerbl et al., 2023) Brualla et al., 2021) formats, and our method naturally inherits this capability.
and NeRF (Martin-Brualla
For our experiment, we utilize the pre-trained Trellis model as the backbone network due to its demonstrated efficiency and high-quality outputs:
{ goo: Liex, Dmesh)
(5)
To maintain distinct control over different attributes, we design separate interpolation functions for geometric and texture features:
== (Ig3o: I.x)
(6)

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3.3 Shape interpolation via optimal transport barycenter
Given Co E(P and CJ+1 (arget), which represent the conditioning features respectively encoded from the input prompts Paource and Parget. we first model them as discrete probability distributions in feature space by extracting their respective sets of feature tokens:
== {x:) C R",, J'
R"
where is the embedding dimension. We can then obtain their empirical distribution:
M1
0,Sx,,
bydy,,
(8)
where 8x; and by, denote the Dirac measures located at the tokens x; and y,, respectively. a, and by are weights assigned to each token, which we set = bj = 1/n in our experiments.
To obtain the geometric structure Pa an intermediate step via the generator &geo, require its corresponding interpolated condition Z Since &geo is a deterministic mapping from conditions to structures, propose obtain & by solving supe-support Wasserstein barycenter problem:
Igco Za = arg min [1 - -3-W2 + B (22 v]], B = a/(J + 1),
/ d(æ, 3)² dr((x,))
(9)
(2, = inf vEI(u.)) nxn
where W (u, u) denotes the 2- 2-Wasserttein distance, = C is the target interpolated distribution with learnable support points (z: )== uniformly weighted by Cs = 1/n. The interpolated input to geometric structure
tokens Za {ax k= generated through this process
generator T1 goo- which produces the corresponding 3D structure Pa-
A critical design for solving the supeortpport Wasserstein barycenter problem is the initialization of
support points }z While naïve linear interpolation approach, Zkiinit = (1 - B) Xx + yk. might seem reasonable, we empirically observe that this often results in discontinuous inconsistent interpolations. This is particularly evident when interpolating between either: (1) geometrically
distant shapes, or (2) semantically divergent conditions (see Fig. 8). The failure arises because linear initialization overlooks the structure token distributions, often leading the barycenter optimization unstable unrealistic interpolations.
We address this limitation with a sequential initialization scheme that guarantees smooth transitions along the barycenter trajectory. Specifically, for interpolation step E [0, + 1J. we initialize its barycenter using the optimized solution from the previous step:
(a)) 2i
Xb:
if a a 0
(10)
(o
otherwise.
This recursive approach supports the barycenter evolves continuously on the data manifold, main- taining: (1) geometric coherence: support points adapt gradually, reducing abrupt deviations poor local minima, and (2) semantic stability: intermediate shapes retain recognizable object parts and consistent structural semantics throughout the interpolation.
3.4 Controllable texture generation via selective interpolation
As illustrated in Fig. 2, our framework supports two strategies for texture evolution. The default textured 3D morphing utilizes the barycenter interpolated tokens Z, (derived in Sec. 3.3) as the condition for the texture flow model Ptex- Although this produces smooth transformation, it strictly couples texture evolution with the structural transition. However, creative applications often demand more flexibility- Rexibbiity--peciffcclll the ability to preserve the source's visual identity (e.g.. facial features or iconic patterns) even the shape transforms. One may consider enforcing source tokens X' across all token regions, but this is not feasible: as the geometry deforms, forcing static tokens onto the evolving structure creates inherent texture-geometry misalignment, resulting in visual artifacts.
To address this, we introduce texture controlled 3D morphing. This mode employs a selective interpolation strategy that functions as tunable filter. By adjusting similarity threshold, users
5

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can explicitly regulate the degree of source preservation, spanning from subtle detail retention to strong identity maintenance. This mechanism retains high-frequency details from X' only where semantically aligned to prevent artifacts, while utilizing 2, elsewhere to ensure structural coherence.
Building upon the geometric interpolation introduced in Sec. 3.3, first compute the free-support barycenter Zo J from Eq. 9. While this provides general interpolation baseline, it may dilute fine-grained details from the source. To mitigate this, we augment the interpolation with semantic consistency evaluation between barycenter points Zд and the source tokens X. y
Specifically, for each barycenter point Zy., identify its closest source tokens x; and y, and compute the cosine similarity sim = cos (y). We then selectively retain high- sigh-frequency information from either x, or the interpolated tokens based on similarity:
a2 = Zg. if sim >T ZR
1xx Z. = [Z
while
(1I)
Xj
otherwise.
Here, E [0, 1] is pre-defined similarity threshold that determines whether semantic discrepancy is large enough to justify interpolation. See Fig. 6 for an analysis across different values of T. This approach enables asymmetric texture fusion, where more visually salient personalized texture features can be preserved from one condition, while maintaining semantically meaningful global structure via the barycenter.
The selectively refined condition tokens 2, are then passed into the 3D textured structured latents generator Puxx to produce the final latent representation h,, The interpolated 3D textured mesh at
step can then be obtained through the decoding function Eq. 4:
G Dimest (sa),
where sa {ha, Pa {(hi,a Pi,a)
(12)
Experimental results
Implementation details We adopt the Trellis framework as our 3D generative model. Both the structure flow and texture flow models are implemented using rectified flow with 25 sampling steps each. The Classifier-Free Guidance (CFG) strength is set to 3. DINOv2 (Oquab et al., 2023) and CLIP (Radford et al., 2021) are used for image and text feature extraction. Both the structure and texture flow-based generator &eo. Piex contain 21 cross-attentie performed on every condition token before each cross-attention layer. The morphing coefficient uniformly sampled during morphing and we set J for experiments presented in the paper. For shape interpolation, we compute the token-wise barycenter using the free-support Wasserstein barycenter implemented by ot.lp. _areeessprtttttee support._barycenter barycenter (Lindheim, 2023). We set the maximum number of optimization iterations to 100, and the convergence threshold (stop criterion) to 10 5, We conduct experiments on an NVIDIA A100 GPU, Generating a single morphed 3D output takes approximately 30 seconds.
blocks, where interpolation is
Metries Following (Yang al., 2025), evaluate textured 3D morphing quality using metrics for fidelity, plausibility,, and smoothness on their input pairs: (1) FID (Heusel et al., 2017) for visual fidelity; (2) STP-GPT and SEP-GPT for structural and semantic consistency; (3) GPT-4o (Hurst et al., 2024) for visual plausibility (0-1 score); (4) PPL (Karras et al., 2019) for perceptual smoothness; and (5) V-CLIP (Ma et al., 2022), which measures semantic alignment to "a smooth transformation from to B" using cosine similarity in a joint embedding space.
Baseline methods for evaluation We compare our 3D textured morphing results against two baseline methods: 3DRM (Yang et al., 2025) and MorphFlow (Tsai et al., 2022). To further evaluate performance, we render the 3D meshes into 2D images and compare them with state-of-the-ar 2D image morphing approaches, including DiffMorpher (Zhang al., 2024a), AID (Qiyuan et al., 2024), MV-Adapter (Jin et al., 2024), and Luma (Luma Labs AI, 2025).
4.1
Main results
4.1.1
Quantitative results
In Tab 1, we compare our method against a range of baselines across multiple evaluation metrics. As can be observed, our method consistently outperforms existing approaches across all metrics,

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Table
Quantitative comparison.
Model
FID 4
STP-GPT
SEP-GPT
PPI.
V-CLIP
DiffMorpher
218.07
0.14
0.10
5.23
0.61
AID
115.72
0.62
4.68
MV-Adapler
120.93
0.44
0.49
7.29
0.67
Luma
95.49
0.69
0.65
7.37
0.70
MarphFlow
147.70
0.79
3,10
0.78
3DRM
6.36
0.93
0.38
3.02
0.84
demonstrating superior quality and consistency in both shape and texture morphing. Specifically, outperform state-of-the-art textured 3D morphing methods across the board, 3DRM (Yang et al., 2025) and MorphFlow (Tsai et al., 2022), showing clear improvements in perceptual quality and semantic coherence. Furthermore, compared with image-based morphing methods (Zhang et al., 2024a; Qiyuan et al., 2024; Jin et al., 2024; Luma Labs AI, 2025), our approach also exhibits much stronger 3D consistency and higher fidelity in both appearance and structure. See Appendix for more quantitative evaluations.
Source image:
Target image:
Figure 3: Textured 3D morphing with image prompts. The leftmost column shows source image prompts, while the rightmost column shows target prompts. Intermediate columns depict the morphing trajectory generated by our method.
4.1.2 Qualitative results
Image-conditioned 3D morphing Fig. 3 shows image-conditioned 3D morphing results. Each row presents a smooth transition from source to target, with consistent shape and texture interpolation. Intermediate frames preserve structure without distortions. Rows 3-5 highlight our method's ability to handle cross-category morphing with clear semantic consistency.
Text-conditioned 3D morphing Our method also enables morphing conditioned on textual descriptions, allowing users to generate transitions directly from text prompts. As shown in Fig. 4, the results exhibit smooth transitions both shape and texture, with intermediate outputs maintaining high fidelity and semantic alignment. Notably, the second row captures precise castle-to-rood transformation, while the fourth row demonstrates realistic face morphing with consistent structure.
Comparison with existing methods We now compare our method with the current state-of-the-an -heetrrtt in textured 3D morphing (3DRM (Yang et al., 2025)) in Fig. 5. Visually, our method delivers higher overall quality with significantly smoother transitions both geometry and texture. Color variations in our morphing sequences also appear more continuous, and the interpolated shapes retain clear

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Source TEN!!
Torget text
"Car"
"Elephont"
"Clovle"
"Hoas""
"Briccinppto""
"Fiarr
"But
Conaar
Helmer
Figure 4: Textured 3D morphing with text prompts. The leftmost column shows the source text descriptions, and the rightmost column shows the target prompts. Intermediate results visualize the smooth morphing trajectory generated by our method.
3DRM
Ours
3DRM
Ours
Figure 5: Qualitative comparison with current SOTA method 3DRM. Rows and 3 show 3DRM's generated results, while rows and 4 display our method's 's outputs.
semantic coherence throughout, We further compare with MorphFlow, a recent textured 3D morphing approach, in the Appendix D.
Controllable texture morphing In Fig. 6, we demonstrate the capabilities of our texture controlled morphing mode, by employing our proposed selective texture interpolation. Each row represents a distinct morphing process with different interpolation thresholds, with the leftmost column fixed as the source shape. The results along the vertical axis (columns) are independent of each other. The far-left column does not show texture changes, it represents the same 3D source generated from the input source image. By adjusting the selective interpolation threshold т (from top to bottom), we can control the relative influence of source and target tokens during the morphing process. For instance, we can preserve source-dominant features such as facial identity, clothing styles, and color patterns in the intermediate 3D shapes, while still achieving smooth and semantically coherent shape transitions.
4.2 Ablation study
Ablation on shape interpolation In Fig. 7 (top three rows), we first present ablation studies evaluating different strategies for shape interpolation image-conditioned 3D morphing. Specifically, (1) the first row shows results from directly applying linear interpolation to the condition tokens. While this produces smooth blending in token space, it results semantically ambiguous intermediate shapes, incoherent textures, and noticeable color artifacts, revealing the limitations of naïve token- level averaging. (2) The second row presents our method using a linearly initialized free-support barycenter (i.e., linear initialization of support points). Although more flexible than fixed linear interpolation, it tends to converge to undesirable solutions, leading to distorted geometry and unstable textures in intermediate outputs. (3) In contrast, the third row shows our full method, where the

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Target Exnges
TESTIT contollzd surphing
Todl inmpppattcs
Onn
Figure 7: amage-conditioned ablation study. Top three rows compare textured 3D morphing: linear interpolation and no recursive initializa- tion show shape and color artifacts, while ours is clean. Bottom two rows compare texture control: increasing source weight preserves details but distorts color; our selective interpolation main- tains texture quality.
Figure 6: Texture controlled morphing results. The figure shows shape interpolation arranged horizontally, with each row representing mor- phing process at different interpolation thresh- olds, illustrating the smooth transition from the source image to the target image.
barycenter is properly initialized and refined through iterative optimization. This results in high- quality morphs with semantically meaningful structure, smooth geometric transitions, and coherent color blending.
We further conduct ablation studies for shape interpolation under the setting of text-conditioned 3D) morphing, as in Fig. 8. The results reveal the following: (I) Linear interpolation (row 1) produces unnatural artifacts, such as T-pose human figures, indicating that direct token blending often strays into semantically invalid regions. (2) Our method without recursive initialization (row 2) exhibits similar issues, underscoring the importance of proper initialization for barycenter optimization. Linear initialization fails to respect the underlying data manifold. often leading to unstable or artifact- prrone results, as detailed in Sec. 3.3. (3) Our full method (row 3), with recursive initialization, successfully avoids these problems, delivering smooth and structurally plausible shape transitions throughout the morph.
Source
Target
Linear interpolation
Ours wio recursive iniiialization
Ours
Figure 8: Ablation study on text-conditioned 3D shape morphing. Morphing from "'T-re"" to "cat," the first row (linear interpolation) shows multiple T-pose human artifacts. The second row (without recursive initialization) still has some artifacts. The third row (full method) achieves smooth, coherent shape transitions.
In flow-based 3D generators, condition tokens guide the generation by modulating cross-attention at each layer. Linear interpolation between latent features explores intermediate regions of the latent

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space. However, when the source and target features are semantically distant (e.g., from "T-Rex" to "cat" in Fig. 8) and lack appropriate supervision, the model's conditioning may become ambiguous or collapse into mode-averaged representations. This often results in "hallucinated" generic outputs, such as T-poses or default humanoid templates commonly seen generative models. This issue highlights the importance of our barycentric interpolation and sequential initialization strategy, which helps: (1) intermediate tokens remain on a semantically valid manifold; (2) abrupt jumps across unrelated modes are mitigated.
For quantitative comparison, Tab 2 provides ab- lation results to validate our method. The "Lin- ear" baseline, using direct interpolation without optimization, leads to unnatural shapes and vis- ible artifacts. The "w/o r-i" row employs opti- mization but omits recursive initialization; while improves upon the linear baseline, the lack of robust prior often causes convergence to sub- optimal solutions, resulting in distortions. In contrast, "Ours*" applies our full method to the TripoSG (Li et al., 2025) backbone. This config- uration includes recursive initialization, which acts as a strong prior to guide optimization toward stable and meaningful representations. Consequently, "Ours*" yields smoother geometry and better texture blending than the baselines. Notably, both "Ours and our default "Ours" achieve the highest fidelity, demonstrating that method generalizes well across models and improves quality through effective initialization and optimization.
Table 2: Ablation on shape interpolation.
Model FID J. STP-GPT SEP-GPT PPT. 4 V.CIJP
Linear 14.73
0.86
0.85
3.06
0.81
wori 12.52
0.90
0.92
3.03
0.84
Ours 4.16
0.96
0.98
2.95
0.91
Ours 4.01
0.97
0.95
2,91
0.90
Ablation on texture interpolation In Fig.
(rows 4 & 5), we evaluate the effectiveness of texture controlled morphing enabled by our se- lective interpolation strategy. In row 4, directly increasing source token weights via naive lin- ear interpolation leads texture collapse, with disorganized colors and unclear semantics. In contrast, row 5 shows that our selective interpo- lation yields smooth morphing results while preserving distinctive source features like Wukong's appearance and attire. This demonstrates that selective control is crucial for high-quality and identity- preserving texture transitions.
Table 3: Ablation on texture interpolation.
Model
FID
STP-GPT SEP-GPT PPL V-CLIP
who interp
6.52
0.83
0.85
3.04
0.81
Linear
5.17
0.92
0.90
2.95
0.86
Ours
4.20
1.00
1,00
2.91
0.90
We conduct quantitative ablation experiments for texture interpolation strategy as shown in Tab 3. The first row "wo interp" refers to directly copying the source texture tokens across all steps without any interpolation. The second row "linear" refers to applying standard linear interpolation between source and target texture tokens. The third row "ours" corresponds to using our proposed selective interpolation strategy based on similarity thresholding. Our method achieves the highest visual fidelity and semantic consistency throughout the morphing trajectory, demonstrating that our method generalizes effectively across various 3D generative models. Additionally, it significantly enhances interpolation quality by carefully designing both the texture interpolation procedures.
5 Conclusion
We present a unified and flexible framework, WUKONG, for high-quality 3D morphing driven by minimal maput-either in the form of image or text prompts. By leveraging a rectified flow-based generative model as prior, our method enables semantically meaningful and structurally consistent shape and texture transitions. We reformulate the interpolation process using an optimal transport barycenter approach, and further enhance its stability and realism through a sequential initialization strategy. Additionally, our selective texture interpolation module offers fine-grained control over appearance, allowing users to preserve or blend semantic attributes as needed. Extensive experiments across diverse categories confirm the effectiveness of our design, with our method consistently outperforming prior state-of-the-ee art in both shape fidelity and texture consistency.
Acknowledgmenn This work is supported by Hong Kong Research Grant Council General Research Fund (Grant No. 17213825). We would like thank Tianshuo Yan for the invaluable help during the paper preparation.
10

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Wukong's 72 Transformations: High-fidelity Textured 3D Morphing via Flow Models Appendix-
A Model details
To enable high-fidelity textured 3D morphing, we build upon two pretrained flow-based transformer models introduced in Trellis (Xiang al., 2024): the structure flow model and SLat flow model, orig- inally designed for unconditional 3D generation. The structure flow model operates on a structured latent representation and follows a transformer-based architecture with 24 modulated transformer blocks with cross attentions. Each block contains self-attention, cross-attention, and feed-forward components, modulated via AdaLN (Guo et al., 2022) conditioning from a learned timestep embed- ding. Root Mean Square Normalization (RMSNorm) (Zhang and Sennrich, 2019) is applied to both the query and key representations prior to their use in the attention mechanism. The SLat flow model incorporates hierarchical design with sparse 3D convolutional blocks and positional embeddings to encode spatial context. The transformer core comprises 24 modulated sparse transformer blocks with cross attentions, analogous in structure to the geometry model but enhanced with sparse atten- tion and feed-forward operations. Additionally, the model includes dedicated output convolutional blocks for upsampling and decoding, ensuring fine-grained preservation and modulation of high- frequency texture details. We use the free-support Wasserstein barycenter solver from the POT library (ot. llt.1p... free_ www.freeeeuppppeeeee support curport_barycenterr (Lindheim, 2023)), which based linear programming (LP). The cost matrix is computed using the squared Euclidean distance in the CLIP (Radford et al., 2021) (for text) and DINOv2 (Oquab al., 2023) (for image) embedding space.
B Ablation study
To ensure fair, opples-to-apples comparison with 3DRM, implemented our full morphing method on top of the AnythianAnything (Yushi et al., 2025) framework- same 3D generator used in 3DRM (Yang al., 2025), results shown in Tab 4. We denote this variant "Ours*" in the table below. Across all evaluation metrics, including FID, PPI., V-CLIP, and GPT-based perceptual scores (STP-GPT, SEP-GPT), our method consistently outperforms 3DRM, even when both share the exact same backbone. This clearly demonstrates that the improvement is not solely due to the use of stronger generator like Trellis, but rather stems from our core morphing algorithm. Note that the GPT-based results may differ from those presented in the main paper, as they are computed using only the four methods reported here.
Table 4: Quantitative comparison with GaussianAntthing backbone.
Model
FID 4
STP-GPTT
SEP-GPT
PPL 4
V-CLIP
MorphFlow
147.70
0.38
0.41
3.10
0.78
3DRM
6.36
0.85
0.80
3.02
0.84
Ours*
5.15
0.93
0.91
2.94
0.87
Ours
4.01
1.00
1.00
2.91
0.90
Besides, we conduct quantitative evaluations with different threshold values and present the results below as shown in Tab 5. We observe that the performance is robust across a reasonable range of thresholds (0.2-0.8). We default threshold 0.3 in our evaluation.
Table 5: Ablation study on threshold
Threshold
0.2
0.3
0.4
0.6
0.8
FID L
4.54
4.20
4.17
4.25
4.49
PPL 1
2.94
2.91
2.91
2.92
2.93
V-CLIP
0.88
0.90
0.91
0.90
0.87
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To evaluate the method's generalization to real 3D data, we conducted experiments using the Headspace dataset (Dai al., 2020), which contains high-quality 3D face scans along with corre- sponding rendered RGB images. In our pipeline, we used these rendered images as inputs and passed them through the DINOv2 (Oquab et al., 2023) encoder to extract texture and semantic features for morphing. The outputs were generated by our standard pipeline without any architectural changes or fine-tuning, Despite relying on pretrained components, our method shows strong generalization to real-world 3D scans. Our method outperformed both MorphFlow (Tsai et al., 2022) and 3DRM (Our own implementation) ( (Yang et al., 2025) on the same evaluation protocol. Quantitative results are shown below in Tab 6:
Table 6: Quantitative results on Headspace dataset.
Model
FID
STP-GPT
SEP-GPT t
PPL +
V-CLIP t
MorphFlow
95.24
0.53
0.47
3.22
0.84
3DRM
6,61
0.83
0.77
3.04
0.88
Ours
3.97
1.00
1.00
2.88
0.96
C Rectified flow models vs. diffusion models
Continuity The first reason choose the flow model over the diffusion model for 3D morphing is its mathematically grounded continuity with respect to the interpolation parameter a. In flow-based generative models, the mapping from to the output F(a) is deterministic and constructed via an invertible transformation T(2; o.). typically defined by an ordinary differential equation (ODE). Under standard regularity conditions (e.g., Lipschitz continuity of the velocity field), the solution T(2; ax) is guaranteed to be continuously differentiable with respect to a (Loud, 1987), ensuring that the morphing trajectory forms smooth path in the output space. This deterministic nature makes it possible to precisely control intermediate shapes and textures, yielding consistent and artifact-free transitions.
In contrast, diffusion models are typically governed by stochastic differential equations (SDEs), which introduce randomness throughout the generative process. While deterministic sampling methods like DDIM (Song et al., 2021) exist and are widely used, the underlying denoising process in these models often follows stochastic trajectory. Consequently, even with interpolated conditioning, the same value of a can yield different outputs across runs. This inherent variability makes it difficult to ensure continuity precise control in the morphing sequence, partieularly at intermediate points where uncertainties compound. In contrast, rectified flow models generate deterministic and unique interpolation paths, enabling forward integration with a guaranteed likelihood formulation. This property makes them more suitable for achieving smooth, stable, and controllable interpolation, which aligns with our need for consistency the latent space.
Convexity and optimality There are theoretical guarantees for flow models like the rectified flow model in maintaining the convexity of data during the generation process. This linearity ensures that any intermediate sample lies within the convex hull of the endpoints, thereby preserving the convexity of the data. In practice, the trajectory is hard to remain straight. There is analysis (Liu et al., 2023) on the straightness eттоr on the trajectory, which states that even an imperfect trajectory is close enough to straight lines and ensures the convexity of data to some extent. Theorems in this analysis further emphasize the uniqueness and optimality of the solution of rectified flow in matching distributions under convex cost functions. For diffusion models, the backward process generates data from the prior but does not theoretically guarantee convexity preservation. These models focus on matching data distributions, not preserving geometric properties like convexity. There is no theoretical guarantee on the data convexity in the backward process. The noise term in the reverse-time SDE can easily violate the convexity of the original data. Also, under the same condition as the rectified flow model, the path of diffusion models is not assured to be optimal. There exist certain crossing flows in the matching of two distributions, leading features that are out-of-diitribution in practice.
Inference speed The theoretical basis for the faster inference of flow models (such as rectified flow models) primarily stems from the geometric properties of their trajectories and the efficiency of numerical simulation. For rectified flow model used in this paper, it aims to make generation
15

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Table 7:
Quantitative comparison with shape morphing methods.
Metrics
MapTree
BIM
SmoothShells
NeuroMorph
SRIF
Ours
Dirichlet 4
17.7309
12.4723
14.0198
22.0461
6,4702
4.5163
Cov. 1
0.3967
0,4665
0.6275
0.1099
0.6418
0.8510
trajectories straight as possible. For ideal straight-ling line flows, the trajectory between any two points Zu My and 11 Ti given by the linear interpolation Z, tZ + (1 t) Zo. In this case, the drift field of the ODE is constant v( Zz, () Zi Zo. which can be solved exactly with a single Euler step: Zı = Zo + v Zo, 0) This eliminates the need for time discretization errors. Even in non-ideal cases, the optimized trajectories are close to straight lines, significantly reducing the number of required steps (in this paper, we take 20 steps). Diffusion models use nonlinear, stochastic trajectories requiring many steps ttypicalllyy 2,000 without sampling techniques around 200 with them-to achieve good results. Also, the reverse SDE process requires noise sampling, leading to additional computation cost.
D Comparison with other methods
D.1 Comparison with other textured 3D method
We compare our method with existing textured 3D morphing approaches, including MorphFlow, 3DRM, and our own. While the main paper presents qualitative comparisons with 3DRM, here additionally provide a side-by-side qualitative comparison with MorphFlow. As shown in the Fig. 9, and display results from MorphFlow, and rows 2 and 4 show corresponding outputs. Our method produces noticeably clearer and more accurate shapes and textures, with smoother and more coherent morphing transitions. These visual improvements results reported in the main paper, further corroborating the effectiveness of our approach.
consistent with the quantitative
MorphFlow
MorphFlow
Ours
Figure 9: Qualitative comparison with MorphFlow.
D.2 Comparison with shape morphing methods
Although previous 3D shape morphing methods do not consider texture transformation, we provide comparison focused solely on shape deformation. As shown in Tab we compare our method with several state-of-the-art shape morphing approaches, including MapTTree (Ren et al., 2020), BIM (Kim et al., 201 1), SmoothShells (Eisenberger et al., 2020), NeuroMorph (Eisenberger ct al., 202 I), and SRIF (Sun et al., 2024). Following the evaluation protocol from (Sun et al., 2024), we use the SHREC07 (Temerinac et al., 2007) dataset and report performance using Dirichlet energy (Ezuz et al., 2019) and Coverage (Huang and Ovsjanikov, 2017) metrics. Our method achieves superior results across both metrics, demonstrating more efficient and accurate shape interpolation.
For qualitative comparison, we show results against SRIF in Fig. 10, where rows 1, 3, and 5 show SRIF's outputs and rows 2, 4, and 6 show ours. Our morphing process smoother and preserves finer details in intermediate shapes-fir for example, the gecko's toes in row and the head structure in row 4. Additional comparison with NeuralMorph presented Fig. 11, where our results are again significantly more detailed and coherent.
16

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SRIF
Ours
SRIF
Ours
SRIF
Figure 10: Qualitative comparison with SRIE
NeuroMorph
Ours
NeuroMorph
Ours
Figure 11: Qualitative comparison with NeuralMorph.
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To ensure that intermediate shapes remain faithful to both source and target, we introduce a shape- aware initialization. Specifically, render both front and back views of the source and target objects and use these images as inputs to the flow model to extract initial condition feature. This feature then refined by minimizing the geometric difference between generated shapes and the original meshes, leading to accurate and consistent 3D representations throughout the morphing sequence.
E More results
Fig. 12 and Fig. 13 illustrate the textured 3D morphing process generated from "Wukong" to variety of objects. The texture can flexibly inherited from either the source or the target image, depending on the user's preference. Fig. 14 and Fig. 15 further demonstrate morphing between additional object pairs, showcasing the versatility of our method. Notably, our method capable of performing textured 3D morphing not only between geometrically complex objects, but also across different semantic categories, highlighting superior robustness and generalizability.
Broader impact
Our work introduces WUKONG, training-fres framework for high-quality textured 3D morphing, which significantly lowers the barrier to creating detailed and semantically consistent 3D transfor- mations from simple prompts. This greatly reduces the efforts on 3D content creation for artists, designers, and educators, enabling broader access to advanced generative tools without requiring technical expertise in 3D modeling or animation. The ability to produce controllable and high-fidelity morphing sequences could benefit applications in virtual reality, digital storytelling, education, and creative industries. We hope our work inspires further research into controllable and efficient 3D generation techniques, and that it serves foundation for inclusive and creative applications of generative 3D content.
G Limitation
While our method achieves state-of-the-ar performance textured morphing, several limitations remain. First, like existing morphing methods, our approach still encounters difficulties in cases involving extreme topological changes, such as splitting or merging parts. These scenarios remain a general challenge in the field and are not yet fully addressed by existing methods. Second, since our method operates without explicit 3D supervision or correspondence annotations, its results may be sensitive ambiguities in the input prompts or inconsistencies in multi-view generation, especially when the input lacks structural clarity.
18

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Presere Souuce sottre
Source
Tirget
Blext towndd targs
Preseve Soutce
Sounce
(ildd sount target
thesere Source resture
Source
Target
Bicad tarpttisttue
Proaen
Source
Biead tonad testur
Preeere Swaree sestim
Source
Target
sarga
Figure 12: Textured 3D morphing of Wukong (The Monkey King).
19

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Preienve
Sturce
Target
aarget
Scorco testuree
Prosere
Source
Target
Bllndd loward
Tagee
Presene
Sourcel
Sounc
Tirget
Homd
Preeeve
Source
Tirget
Figure 13: Textured 3D morphing of Wukong (The Monkey King).
20

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Souree textoe
Preserve Reea
Source
Preserve Source scatuee
Source
Targer
Blad oigedt
Sourror kostine
Piestrve
Source
Target
Blend srwved
Prosenve
Source
Target
Biadd
tirget
Seurcce tnstar
Prssorve
Source
Target
sttesd toward targan tetture
Figure 14: Textured 3D morphing of different objects.
21

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Presenst Soue cn tosioe
Source
Bland Jooand
Prostre Sauree tastue
Source
Tirget
Dial townl tagge testuee
Sourcee tenture
Source
Target
narget tonture
Preserve Source testure
Source
Target
Blood trgger
Presuree Source testure
Source
Target
Blend sowaard tampot scature
Figure 15: Textured 3D morphing of different objects.
22

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Reviews:
[{"review_id": "lgcOp1unrR", "reviewer": "Reviewer_43Xk", "summary": "This work proposes WUKONG, a training-free method for joint shape and texture morphing between two 3D assets. Building on top of the latent space of the TRELLIS (CVPR 2025), the authors propose an optimal-transport-based latent interpolation method to interpolate between shape latent space and the texture latent space. The proposed free-support Wasserstein barycenter optimization method takes the source and target image prompt feature tokens and builds an emperical distribution (using Dirac delta function with uniform weights), then optimizes the interpolated tokens (support points in the latent space) to minimize the Wasserstein barycenter distance. The authors solve this for shape space interpolation, then find the texture latent code, using the semantic consistency evaluation. The 3D generation pipeline itself borrows the pre-trained TRELLIS model. The authors use several plausibility and perceptual score metrics, including GPT scores, FID and PPL, etc.", "questions": "No outstanding questions (already discussed in weakness section). \nIt'd be good if the authors can also showcase the results for real images or real assets.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n- The problem formulation is interesting, and the solution is simple yet effective. While the authors rely on the pre-trained powerful 3D generation model TRELLIS, the method to enable smooth shape & texture interpolation from the learned latent space is inspiring and novel. Moreover, extensive evaluations and visualizations support that the naive linear interpolation of the latent codes are a sub-optimal, but the proposed optimal-transport barycenter method is effective. \n- The proposed tricks (recursive support initialization for Wasserstein barycenter problem and selective interpolation) are technically sound and effective.\n- Qualitative results look promising.\n\n\n\n## Weaknesses\nThis reviewer could not find significant issues or weaknesses. A few questions:\n- The method seems to work on some fictional images or artistic prompts. If one wants to use it for real 3D data, e.g., human face scans, which modifications can be applied? For example, this reviewer wants to know what happens if one wants to interpolate between real human face image and artistic human face image (e.g., ironman).\n- What could be the reason for T-pose human artifacts when using linear interpolation (Fig. 6, first row)? Any insights on this issues?", "was_revised": true, "final_justification": "The answers mostly address this reviewer's concerns. This reviewer believes that the combination of existing concepts to build a working solution for an interesting problem can also be viewed as a novelty. Still, this reviewer requires the authors to include some real scan examples and a discussion of T-pose artifacts in the final draft. Thus, this reviewer keeps the rating.", "rebuttal": "We sincerely thank the reviewer for their thoughtful and positive feedback. We are pleased that the novelty and effectiveness of our approach, particularly in enabling smooth shape and texture interpolation, was recognized. We address the concerns below.\n\n**Concern 1: The method seems to work on some fictional images or artistic prompts. If one wants to use it for real 3D data, what would happen, and what modifications can be applied?**\n\n**A1.1: Current model's ability for real 3D data.** \n\nTo validate the current model's ability for real 3D data, we conducted extensive tests on **real human face scans** from the Headspace dataset [1], as well as **cross-domain conditions** like real photo → stylized identity (e.g., human face → Ironman). Our method handled these scenarios successfully.\n\n1. **Experiments between two real human faces (Headspace dataset [1]):** To evaluate the method’s generalization to real 3D data, we conducted experiments using human face renderings from the Headspace dataset, which contains high-quality 3D face scans along with corresponding rendered RGB images. \n\n    Despite the domain gap between real scanned faces and the Trellis training distribution, our method produced smooth and coherent 3D morphing sequences between different real identities. Quantitatively, using the same evaluation metrics as in Sec. 4.1.1, we obtained: **FID: 3.97, PPL: 2.88, V-CLIP: 0.91** (see \"Quantitative results on Headspace dataset\"). These results are on par with our main test set results (main paper table 1), suggesting that our method generalizes well to real 3D data, even under a domain shift and without any additional learning. However, despite the good quantitative results, we still observe minor qualitative degradation for the fine-grained geometric details (e.g., wrinkles or subtle facial curvature). This is most likely due to the domain bias inherited from the pre-trained trellis model.\n\n2. **Experiments between a real human face and an artistic target:** We also tested morphing between a real human face image (photo) and a stylized image of Ironman’s helmet. The resulting 3D sequence showed a smooth and visually consistent transition, with clear semantic interpolation from the human facial structure to metallic plating, visor formation, and color/style blending. The quality is visually comparable to our result in Figure 4 (last row: \"bust of Caesar\" → \"Darth Vader helmet\"). Due to the limited number of such stylized test cases, we do not provide a quantitative average, but the results we obtained were qualitatively compelling and robust, reinforcing the method’s flexibility in handling cross-domain, real-to-stylized scenarios.\n\nNote that we conducted these experiments without any architectural changes or further fine-tuning. We will include these additional qualitative results and metrics in the updated version of the paper to better illustrate the method’s effectiveness under real-to-stylized morphing and real 3D scan domains.\n\n3. Our current implementation is based on Trellis, which is trained mainly on synthetic textured 3D objects. Therefore, it's reasonable to perform best when the input domain aligns with the training distribution. However, we want to highlight that our method (WUKONG) is a modular, training-free morphing framework, not tied to any specific backbone. During application, it can be easily adapted to more generalizable or domain-specific 3D generators without retraining.\n\n**A1.2: Modifications can be applied.**  To further improve performance on real 3D data, the following modifications can be tried:\n* Model adaptation:  Fine-tune the encoder/decoder on real 3D dataset such as human face scans.\n* Incorporating 3D scan priors (e.g., via FLAME or DECA) to regularize the geometry flow in our framework.\n* If real 3D scans are available, the source can be used directly in Trellis’s latent geometry representation to avoid re-encoding from image.\n\n**Concern 2: What could be the reason for T-pose human artifacts when using linear interpolation (Fig. 6, first row)? Any insights on these issues?**\n\n**A2:** In flow-based 3D generators like Trellis, condition tokens (e.g., from CLIP/DINOv2) guide the generation by modulating cross-attention at each layer. Linear interpolation between latent features explores intermediate regions of the latent space. However, when the source and target features are semantically distant (e.g., from “T-Rex” to “cat”) and lack appropriate supervision, the model's conditioning may become ambiguous or collapse into mode-averaged representations. This often results in \"hallucinated\" generic outputs, such as T-poses or default humanoid templates commonly seen in generative models. \n\nThis issue highlights the importance of our **barycentric interpolation + sequential initialization** strategy, which ensures that: 1. intermediate tokens remain on a semantically valid manifold, 2. abrupt jumps across unrelated modes are avoided. We will further clarify this insight in the revised paper and illustrate it with additional examples.\n\n[1] Pears, N. E. (Creator), Duncan, C. (Creator), Smith, W. A. P. (Contributor), Dai, H. (Contributor) (6 Jun 2018). The Headspace dataset. University of York. 10.15124/6efa9588-b715-44ec-b7bb-f10dff7ca93e"}, {"review_id": "JJzcndOENE", "reviewer": "Reviewer_qxwi", "summary": "The paper proposes Wukong's 72 Transformations, a training-free framework for high-fidelity textured 3D morphing using flow-based generative models. By formulating shape interpolation as a Wasserstein barycenter problem and introducing a recursive initialization strategy, it achieves smooth geometric transitions. Additionally, a similarity-guided texture interpolation mechanism preserves fine texture details. Wukong's 72 Transformations supports both image and text prompts and outperforms existing 2D and 3D morphing methods in fidelity, smoothness, and semantic consistency.", "questions": "see weaknesses", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1.The paper introduces a novel, training-free framework Wukong's 72 Transformations for textured 3D morphing using optimal transport and flow models.\n2.It achieves high-quality results with smooth shape transitions and detailed texture preservation.\n3.The method supports both image and text inputs and generalizes well across object categories.\nWeaknesses\n1.While the training-free nature of WUKONG is a major strength in terms of usability and scalability, it also introduces limitations. Specifically, the method depends entirely on the capacity and priors of existing pretrained models (e.g., Trellis, CLIP, DINOv2). This reliance restricts adaptability: if the pretrained model struggles with certain categories or domains (e.g., noisy real-world scans or rare object classes), WUKONG has no learning mechanism to correct or fine-tune behavior. In contrast, learning-based methods—although more resource-intensive—can be adapted to specific datasets or tasks via supervised fine-tuning.\n\n2.While the use of optimal transport and flow models for 3D morphing is well-motivated and technically elegant, it mainly adapts existing tools rather than introducing fundamentally new modeling paradigms. The formulation of morphing as a Wasserstein barycenter problem is novel in this application domain, but the components (e.g., barycenter optimization, flow-based generation) are largely drawn from prior works. As a result, the paper’s novelty lies more in integration and application than in methodological invention.\n3.Experiments focus on curated or synthetic data; real-world performance is not fully explored.\n4.Inference time (30s per sample on A100) may limit practical scalability. It is suggested to compare with other methods on the inference speed and computation burden.", "was_revised": true, "final_justification": "The new experiments on the Headspace dataset help demonstrate real-world applicability, and the efficiency comparison clarifies that the method is competitive in inference time. The clarifications on the modular, training-free design also highlight its flexibility and future-proof potential.\nWhile some components are adapted from prior works, the formulation of 3D morphing as a Wasserstein barycenter problem, the sequential initialization scheme, and the selective texture interpolation are non-trivial contributions that strengthen the methodological novelty. Although I still see room for broader real-world evaluation, the rebuttal has addressed my main concerns to a large extent.\n\nGiven these improvements and clarifications, I increase my score.", "rebuttal": "We thank the reviewer for the constructive comments. We are pleased that our method's ability to achieve smooth shape transitions and detailed texture preservation, as well as its generalization across object categories with both image and text inputs, was recognized. We address the concerns below.\n\n**Concern 1: While the training-free nature of WUKONG is a major strength in terms of usability and scalability, it also introduces limitations. Specifically, the method depends entirely on the capacity and priors of existing pretrained models (e.g., Trellis, CLIP, DINOv2). This reliance restricts adaptability. & Experiments focus on curated or synthetic data; real-world performance is not fully explored.**\n\n**A1:** We appreciate the reviewer’s thoughtful analysis. We would like to emphasize that our framework is intentionally modular and training-free by design, which brings several unique strengths and flexibility:\n\n1. **Future-proof and upgradable via backbone improvements:** Our method can immediately benefit from advances in foundational models. For example, replacing Trellis with future more powerful generators can directly improve morphing quality without retraining. This flexibility also allows adaptation to models pretrained on different domains. For instance, as shown in Appendix Table A1, we applied our morphing pipeline on top of TripoSG without other modifications, and the results are still the best among all other methods. This demonstrates the plug-and-play nature of our framework and its ability to inherit capabilities from better 3D priors as they emerge.\n\n2. **Challenges for training-based 3D morphing methods:** We note that building a learning-based 3D morphing system in the current landscape faces significant practical challenges. Specifically, there is no readily available dataset containing sufficient 3D morphing pairs—that is, sequences of high-quality 3D models capturing smooth transitions between two given concepts, with consistent geometry and texture supervision across frames. Constructing such a dataset is particularly difficult in open-vocabulary or cross-domain scenarios (e.g., “cat → dragon” or “human → cartoon Ironman”), where intermediate forms are ambiguous or subjective.\n\n    In contrast, WUKONG’s training-free design provides a flexible, data-efficient alternative. It enables 3D morphing between arbitrary inputs (text or image) using only pretrained generative and perceptual models, without relying on morphing trajectories as supervision. This makes it immediately applicable to a wide range of prompt combinations, including those beyond curated or canonical categories.\n\n3. **Generalization to real 3D data:** To evaluate the method’s generalization to real 3D data, we conducted experiments using the Headspace dataset[1], which contains high-quality 3D face scans along with corresponding rendered RGB images. In our pipeline, we used these rendered images as inputs and passed them through the DINOv2 encoder to extract texture and semantic features for morphing. The outputs were generated by the standard WUKONG pipeline without any architectural changes or fine-tuning. Despite relying on pretrained components, our method shows strong generalization to real-world 3D scans. Our method outperformed both MorphFlow and 3DRM(Our own implementation) on the same evaluation protocol. Quantitative results are shown below:\n\n**Quantitative results on Headspace dataset**\n| Model     | FID↓ |  STP-GPT↑ | SEP-GPT↑ | PPL↓ | V-CLIP↑\n| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |\n| MorphFlow      | 95.24   | 0.53| 0. 47 | 3.22 |  0.84 |\n| 3DRM     | 6.61   | 0.83 | 0. 77 | 3.04 |  0.88 |\n| Ours     | **3.97**   | **1.00** | **1.00** |  **2.88** | **0.96** |\n\n We will clarify and expand these examples in the revision to emphasize real-world applicability.\n\n[1] Pears, N. E. (Creator), Duncan, C. (Creator), Smith, W. A. P. (Contributor), Dai, H. (Contributor) (6 Jun 2018). The Headspace dataset. University of York. 10.15124/6efa9588-b715-44ec-b7bb-f10dff7ca93e\n\n**Concern 2: the components (e.g., barycenter optimization, flow-based generation) are largely drawn from prior works. As a result, the paper’s novelty lies more in integration and application than in methodological invention.**\n\n**A2:** While the building blocks are rooted in existing methods, their composition and adaptation in this context form a methodologically novel and non-trivial contribution that we believe will inspire further work in learning-free 3D content manipulation.\n\n1. We are, to our knowledge, the first to formulate 3D morphing as a free-support Wasserstein barycenter problem, enabling semantically meaningful, geometry-aware transitions in a continuous latent space.\n\n2. We introduce a sequential initialization scheme that is critical for ensuring stable interpolation in high-dimensional flow latent spaces, addressing issues not tackled by prior work in either 2D morphing or general OT literature.\n\n3. We further propose a similarity-guided selective texture interpolation mechanism, offering controllable and detail-preserving transitions in 3D appearance, going beyond naïve interpolation schemes used in prior art. 1.1-1.3 one point.\n\n**Concern 3:Inference time (30s per sample on A100) may limit practical scalability. It is suggested to compare with other methods on the inference speed and computation burden.**\n\n**A3:** We acknowledge that we did not include inference time comparisons in the main paper and will add this comparison in the revised edition. We report the time consumption in the following table. Our method is the most efficient one among all methods.\n\n| Model     | Morphflow |  3DRM | Ours |\n| ----------- | ----------- | ----------- | ----------- |\n| Time | 30 mins | 15 mins | 30 secs |"}, {"review_id": "s9Y6qkGlzO", "reviewer": "Reviewer_pJqM", "summary": "This paper introduces a training-free method 'Wukong' for textured 3D morphing. Given 2\ntext/image prompts ($P_{source}, P_{target}$), the method outputs a morphing\ntrajectory of\n3D generations, which smoothly transition from source to target prompt.\nThe method can perform 2 morphing types: Textured 3D morphing (both geometry and\ntexture are interpolated), and Texture-Controlled 3D morphing (interpolating\ngeometry only, whilst keeping the texture-style of the source) so that 3D\ngenerations remain coherent throughout; i.e. reasonable shapes and textures,\nwith smooth transitions, in all intermediate states between source and target\ngenerations.\n\nThe proposed method obtains 3D generations from the state-of-the-art conditional\n3D generation method, Trellis. The proposed method outputs a path through the\nlatent space of the condition encoder $\\mathcal{E}$, by interpolating the tokens\nof the encoded $P_{source}$ and $P_{target}$. An optimal transport approach is\nused to find the interpolation path; each intermediate code $z_{\\alpha}$ is found\nby minimising a weighted sum of the Wasserstein-2 distances from encodings\n$\\mathcal{E}(P_{source})$ and $\\mathcal{E}(P_{target})$. The paper contributes a recursive\ninitialization strategy for solving the Wasserstein objective, in which the\nmorphing condition tokens are obtained sequentially, with each one initialized\nfrom its predecessor in the morphing trajectory. The paper also contributes a\nselective interpolation strategy for the texture conditioning tokens, using\na cosine-similarity threshold to select which texture tokens to interpolate, and which\nones to retain unchanged from the source.\n\nThe paper reports qualitative and quantitative comparisons with 2 SOTA 3D\nmorphing methods, as well as several image-based morphing methods. They also\nperform ablation studies which validate the novel design choices.", "questions": "- Can the authors comment on how much of the reported improvement in results\nin Table 1 comes simply from their usage of TRELLIS? I note that the prior method\n3DRM [4] uses the CLIP-conditioned 3D generator GaussianAnything [5] as\na backbone. Would the\nauthors be able to run their proposed method using [5], so that we\ncan compare apples-to-apples results with [4]? If the\nauthors show strong evidence that their method outperforms 3DRM even when using\nthe same backbone (eg recomputing the metrics in Table 1),\nthen I would be willing to raise my evaluation score.\n\n- From line 295: **'see Appendix for more analysis’** regarding the selective\ninterpolation strategy. However, I did not find further analysis of the\nselective interpolation strategy there.\nCould the authors clarify what this was referring to?\nWould the authors be able to add quantitative ablation results to validate the\nselective interpolation strategy, into table A1?\n\n- It was mentioned in Appendix H - Limitation, line 137, that the\nmethod encounters difficulties on cases involving extreme topological\nchanges, eg splitting or merging. Could the authors show a\ncouple of examples, to illustrate this failure case?\n\n- Could the authors elaborate on what a plausible use case might be\nfor morphing between unrelated\nobject classes like a digger and a ship, as shown in figure 3?\n\n[4] Yang, Songlin, et al. \"Textured 3D Regenerative Morphing with 3D Diffusion Prior.\" arXiv 2025\n\n[5] Lan, Yushi, et al. \"GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation.\" ICLR 2025", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "### Strengths:\n- *Quality*: The work seems complete. The Introduction and Related Work sections\ngive a clear overview of the existing methods and frame the paper well. The\nmethod is clearly explained. The evaluations seem rigorous and the method\nperforms best across all metrics reported in the\nquantitative results. In the qualitative\nresults (eg comparison Figure 5), the 3D generations are highly detailed\ncompared with SOTA 3DRM.\n- *Clarity*: The paper is generally well written, well laid-out, and polished,\nmaking it a pleasure to read. I believe that an expert reader could certainly\nreproduce the results. Likewise, the figures generally\ndemonstrate the capabilities of the proposed method well.\n- *Significance*: The problem statement is interesting and relevant. The\nproposed method can output 3D morphing\nresults in just 30 seconds,\nwhich maintain the visual fidelity of TRELLIS throughout,\nmaking it a usable tool to generate 3D assets as part of a graphics pipeline.\n\n### Weaknesses\n- *Quality*: The main concern is that the improved results might\nmostly follow from using a significantly more powerful 3D generator\nbackbone; (TRELLIS [1]) compared to the baseline 3D morphing methods.\nWhilst the 3D outputs are of higher quality,\nit's unclear from Figure 5 whether the morphing has\nbeen improved. The perceptual quality difference in the\nunderlying 3D generation may likewise make quantitative comparisons to 3DRM and\nMorphFlow in Table 1 unfair.\n- *Clarity*: \nIt sometimes seems as though superfluous mathematical details\nhave been included, attempting to disguise\nthe naivety of the method.\nFrom the main paper, line 159:\n**‘Considering that the process $S = \\phi_{geo}(\\cdot)$ is Lipschitz continuous\n(see Appendix for mathematical derivation)’**... but I could\nnot find any such derivation in the appendix, \nonly a reference\nto a textbook on differential equations.\nThe authors claim that they used\na rectified flow model rather than diffusion because for a diffusion model,\n**'the denoising process samples from a stochastic trajectory'** (appendix line 60).\nThis seems to ignore well-known deterministic sampling methods for diffusion\nmodels (i.e DDIM).\n\n    - I would also suggest that the caption / labels for figure 7 should be made clearer. The caption states that this figure shows independent shape and texture interpolation, with texture supposedly interpolated on the vertical axis. However, confusingly, the texture changes are not visible on the far-left column. From line 257, it seems that the vertical axis is instead controlling the selective interpolation threshold $\\tau$, but this is not mentioned in the caption or the figure.\n  - On a minor note, I would also suggest clarifying the notation to state that $n$ is the number of tokens on line 155.\n- *Significance*: The proposed method is too simple and naive.\nIt seems to combine existing methods [1] and\n[2] almost out-of-the-box, with little modification. The 2 novel method designs\nwhich are added on top of these tools (sequential initialization strategy, and\nsimilarity-guided semantic consistency mechanism) appear to be simple\ninference-time tweaks to the conditioning signal $C$, and do not reveal deep insights.\n- *Originality*: As\nmentioned, the method relies too heavily on [1] and [2].\nMeanwhile, a number of prior works have also\nemployed Wasserstein distance \nto perform 3D morphing (as mentioned\nin the Related Work on line 98).\nOverall, the novelty of the proposed\nmethod seems weak.\n\n[1] Xiang, Jianfeng, et al. \"Structured 3d latents for scalable and versatile 3d generation.\" CVPR 2025\n\n[2] Lindheim, Johannes von. \"Simple approximative algorithms for free-support Wasserstein barycenters.\" Computational Optimization and Applications 2023\n\n[3] Tsai, Chih-Jung, Cheng Sun, and Hwann-Tzong Chen. \"Multiview Regenerative Morphing with Dual Flows.\" ECCV 2022", "was_revised": true, "final_justification": "Initially, I was concerned that the method's impressive results may simply follow from its use of Trellis. However, the authors have since provided new quantitative results which show that it can outperform the SOTA baseline method 3DRM, even when using the exact same 3D backbone. The new ablation study results further validate their contribution.\n\nThe method is unusually simple and closely related to existing Optimal Transport based 3D morphing methods. Nevertheless, if a hitherto-overlooked simple method can perform well, this is a fair contribution to the community.", "rebuttal": "We appreciate the reviewer’s feedback. We are pleased that the high-quality 3D results and efficient generation time of our method were recognized, highlighting its potential for 3D asset generation in graphics pipelines. We address the concerns below.\n\n**Question 1: Prior method 3DRM [4] uses the CLIP-conditioned 3D generator GaussianAnything [5] as a backbone. Would the authors be able to run their proposed method using [5], so that we can compare apples-to-apples results with [4]?**\n\n**A1:** Thanks for this important question. To ensure a fair, apples-to-apples comparison with 3DRM [4], we implemented our full morphing method on top of the GaussianAnything framework [5]—the same 3D generator used in 3DRM. We denote this variant as “Ours*” in the table below.\n\nAcross all evaluation metrics, including FID, PPL, V-CLIP, and GPT-based perceptual scores (STP-GPT, SEP-GPT), our method consistently outperforms 3DRM, even when both share the exact same backbone. This clearly demonstrates that the improvement is not solely due to the use of a stronger generator like Trellis, but rather stems from our core morphing algorithm. Note that the GPT-based results may differ from those presented in Sec. 4.1.1, as they are computed using only the four methods reported here.\n\n**Quantitative comparison with GaussianAnything as backbone**\n| Model     | FID↓ |  STP-GPT↑ | SEP-GPT↑ | PPL↓ | V-CLIP↑\n| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |\n| MorphFlow      | 147.70   | 0.38 | 0. 41 | 3.10 |  0.78 |\n| 3DRM     | 6.36   | 0.85 | 0. 80 | 3.02 |  0.84 |\n| Ours*     | **5.15**   | **0.93** | **0. 91** | **2.94** |  **0.87** |\n| Ours     | 4.01   | 1.00 | 1.00 | 2.91 | 0.90 |\n\n**Question 2: Would the authors be able to add quantitative ablation results to validate the selective interpolation strategy, into table A1?**\n\n**A2:** The reference in line 295 was intended to refer to an ablation analysis that was unfortunately omitted from the appendix in the initial version. We apologize for the confusion. To address this, we conducted quantitative ablation experiments under the same evaluation protocol described in Sec. 4.1.1, and compared three settings:\n* **W/o interp:** Directly copying the source texture tokens across all steps without any interpolation.\n* **Linear:** Applying standard linear interpolation between source and target texture tokens.\n* **Ours:** Using our proposed selective interpolation strategy based on similarity thresholding.\n\n**Ablation study on texture interpolation strategy**\n| Model     | FID↓ |  STP-GPT↑ | SEP-GPT↑ | PPL↓ | V-CLIP↑\n| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |\n| W/o interp     | 6.52   | 0.83 | 0. 85 | 3.04 |  0.81 |\n| Linear | 5.17   | 0.92 | 0. 90 | 2.95 |  0.86 |\n| Ours     | **4.20**   | **1.00** | **1.00** | **2.91** | **0.90** |\n\nOur results show that the selective interpolation strategy consistently outperforms both baselines across all metrics, confirming its effectiveness in preserving structure, appearance fidelity, and semantic consistency throughout the morphing process. We will include these quantitative results in the revised paper to make this analysis and comparison clearly visible.\n\n**Question 3: It was mentioned in Appendix H - Limitation, line 137, that the method encounters difficulties on cases involving extreme topological changes, eg splitting or merging. Could the authors show a couple of examples, to illustrate this failure case?**\n\n**A3:** Since figures cannot be included in the rebuttal, we briefly describe an example of failed case:\n\nThe source object (\"wind chime\") consists of multiple dangling rods suspended separately, while the target (\"apple\") is a smooth, unified solid. During morphing, the generator struggles to merge the disconnected chime parts into a cohesive apple shape. Intermediate outputs exhibit floating, misaligned fragments, and lack global structural coherence.\n\n**Question 4: Could the authors elaborate on what a plausible use case might be for morphing between unrelated object classes like a digger and a ship, as shown in figure 3?**\n\n**A4:** While morphing between unrelated object classes like a digger and a ship may seem unconventional, such transitions have practical value in creative industries, such as game cinematics, concept art, and visual storytelling, where surreal or symbolic transformations are often used to convey narrative or thematic shifts. Our framework enables such imaginative transitions in a controllable and high-fidelity manner.\n\n**Weakness 1: Quality.** \n\n**A5:** We kindly refer the reviewer to the experimental results in A1, where we implemented our full method using GaussianAnything—the same 3D generator used by 3DRM. This variant, denoted as “Ours*”, consistently outperforms 3DRM across all metrics, including FID, PPL, V-CLIP, STP-GPT, and SEP-GPT. This provides clear evidence demonstrating the effectiveness of our proposed techniques.\n\nWe respectfully emphasize that the core contribution of our work is not in proposing a new 3D generator, but in how we utilize and control the generative process through novel morphing strategies. Notably, all compared methods rely on pretrained generative priors. What distinguishes our method is the integration of:\n\n* **Latent space Wasserstein barycenter-based interpolation**, replacing simplistic latent-space linear blending;\n* **Recursive initialization strategy**, which ensures geometric stability and avoids degenerate intermediate shapes;\n* **Semantic-guided selective texture interpolation**, which provides fine-grained control over appearance blending via thresholded feature similarity.\n\nThese contributions form a principled and generalizable framework for high-quality 3D morphing, independent of the specific backbone used.\n\n**Weakness 2: Clarity on Lipschitz continuous, DDIM and figure 7 layout.** \n\n**A6: Lipschitz continuity.** We acknowledge that the reference to a “mathematical derivation” of Lipschitz continuity in the appendix was misleading. Our intention was to reference the theoretical property of rectified flow models, which are designed to be deterministic and Lipschitz-continuous by construction, as discussed in standard differential equations literature. We will revise the text to clarify that no formal derivation is provided, and instead cite the relevant structural property of rectified flows.\n\n**A6: Regarding the comment on diffusion models and DDIM .** We agree that deterministic sampling methods like DDIM are well-known and valid. Our statement in the appendix was not meant to suggest that all diffusion models are inherently stochastic, but to explain why we chose rectified flow models: they offer deterministic forward integration with a guaranteed likelihood formulation, which aligns well with our need for stable, continuous interpolation in latent space.\n\n**A6: Regarding the Figure 7 layout .** We agree that the caption and layout of Figure 7 could be clearer. Each row individually represents a separate morphing process with different interpolation thresholds, and the leftmost column remains fixed as the source shape. The results along the vertical axis (column) are independent. The far-left column doesn't exhibit texture changes because they are all the same 3D sources generated from the input source image. We will revise the caption and figure layout to make this clearer and indicate that the figure should be viewed horizontally, and clarify the relationship between the rows and the varying thresholds.\n\n**Weakness 3: Significance.**\n\n**A7:** While our approach builds on established components (e.g., flow-based generation and optimal transport), it is far from a simple combination of existing tools. Instead, it is a carefully designed framework that specifically addresses the unique challenges of textured 3D morphing.\n\n* The **sequential initialization strategy** serves as a **trajectory memory mechanism** in high-dimensional latent spaces, which helps stabilize barycentric optimization and avoids abrupt geometric artifacts—a failure commonly observed in naïve interpolation (see Fig. 6 and Fig. 8). Without this step, as shown in Appendix Table A1, visual quality significantly declines, highlighting its importance in ensuring smooth transitions.\n* The similarity-guided **selective interpolation** is not a trivial heuristic, but a principled mechanism that enables localized control over texture blending, which is critical for **semantic consistency and identity preservation** across the morphing sequence. Removing this step, as shown in the “Ablation study on texture interpolation strategy”, leads to blurry and incoherent textures, diminishing overall visual fidelity.\n\n**Weakness 4: Originality.**\n\n**A8:** We appreciate the reviewer’s comment and agree that optimal transport (OT) has been explored in prior works on 3D shape analysis and morphing. However, we would like to clarify that the core novelty of our work does not lie in using OT per se, but in how we formulate and apply free-support Wasserstein barycenter optimization within a flow-based generative framework for textured 3D morphing.\nTo the best of our knowledge, we are the first to leverage OT barycenters in the latent token space of a conditional 3D generator, enabling:\n* Semantically meaningful, geometry-aware interpolation in a high-dimensional flow space\n* Fine-grained texture control through selective semantic blending—something not addressed in prior OT-based morphing methods\n\nIn contrast, previous works typically：\n* Apply OT to fixed geometric representations, which do not incorporate semantic texture control or handle arbitrary image and text inputs in a unified framework.\n* Do not operate on latent token spaces with full texture control, limiting their ability to handle complex texture details in high-dimensional latent spaces."}, {"review_id": "J1zJmVQo5n", "reviewer": "Reviewer_WDq7", "summary": "This paper proposes a training-free framework for high-fidelity 3D morphing conditioned on image or text prompts. Given a pair of prompts, the system generates a semantically consistent and geometrically continuous 3D sequence that interpolates between the source and target shapes. The key technical components include: Wasserstein barycenter-based interpolation in the token space of pre-trained vision encoders (e.g., DINOv2 or CLIP), enabling geometry-preserving transitions; Recursive initialization to stabilize interpolation; Similarity-guided texture selective interpolation to avoid texture blur and semantic drift.", "questions": "- How is the cost matrix for the Wasserstein barycenter computed between source and target token sets? Is it Euclidean distance in CLIP/DINOv2 space? Have you tried cosine or learned distances?\n\n- Can you elaborate on the optimization solver used for computing the barycenter? Are you using LP, Sinkhorn, or other entropic OT solvers?\n\n- What happens when the source and target shapes differ significantly in topology or part structure (e.g., human ↔ airplane)? Are there failure cases?\n\n- How sensitive is your method to the choice of pre-trained encoder (e.g., CLIP vs. DINOv2) or the underlying 3D generator (Trellis)?", "limitations": "- The proposed method performs well under curated prompts and well-aligned source/target categories, but might struggle with topologically dissimilar or fine-grained prompt changes.\n\n- The morphing pathway is driven purely by token interpolation in feature space, which may lack semantic control (e.g., preserving pose vs. identity).\n\n- Heavy reliance on pre-trained backbones makes it hard to decouple where the quality improvements come from—WUKONG's algorithm vs. the generator or encoder.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- Training-free framework: The method relies on pre-trained encoders and flow-based 3D generators, which makes it efficient and widely applicable across categories.\n\n- Geometry-aware interpolation: By applying optimal transport (Wasserstein barycenter) over token distributions, the method avoids naive linear interpolation artifacts. Texture-aware fusion: The selective texture interpolation mechanism helps preserve high-frequency details and identity during transitions.\n\n- Good empirical results: The paper shows impressive qualitative and quantitative results, especially on challenging shape transitions, with various metrics (FID, PPL, GPT ratings).\n\nWeaknesses:\n\n- Limited novelty in core technique: Using Wasserstein barycenters for shape or texture interpolation is not new. Prior work, such as Solomon et al., “Convolutional Wasserstein Distances” (SIGGRAPH 2015), has applied this to 3D shape interpolation directly in geometric domains. In image generation, several works like STROTSS (CVPR 2019) and Wasserstein Style Transfer (AISTATS 2020) have already explored interpolating feature distributions via OT or barycenters. This paper applies a similar idea in a 3D conditional generation setting, but the novelty is more incremental.\n\n- Heuristic design choices: The recursive initialization strategy and similarity-guided interpolation heuristics, though empirically effective, lack theoretical justification or ablation on robustness. The texture fusion rule based on cosine similarity threshold $\\tau$ feels ad hoc. How sensitive is the result to this threshold?\n\n- Dependency on pre-trained components: The success of WUKONG is highly dependent on the quality and generalizability of the pre-trained encoder and 3D generator (e.g., Trellis). It is unclear how well the method generalizes to out-of-distribution prompts or categories not seen during Trellis’ training.", "was_revised": true, "final_justification": "I appreciate the additional experiments provided by the authors.\nThe rebuttal has addressed most of my concerns. Though I still think the novelty is a bit incremental, after reading the other reviews, I think a working solution for a training-free framework of 3D morphing is still valuable to the community.\nHence, I would raise my rating to borderline accept.", "rebuttal": "We thank the reviewer for the constructive comments. We address the concerns below.\n\n**Question 1: How is the cost matrix for the Wasserstein barycenter computed between source and target token sets? Is it Euclidean distance in CLIP/DINOv2 space? Have you tried cosine or learned distances?**\n\n**A1:** The cost matrix is computed using the squared Euclidean distance in the CLIP (for text) or DINOv2 (for image) embedding space. We also experimented with cosine distance(i.e., $C_{i j}=1-\\cos \\left(x_i, y_j\\right)$ ). Using the same evaluation pipeline described in Section 4.1.1, the quantitative results for FID, PPL, and V-CLIP are **4.37**, **2.93**, and **0.90**, respectively, which are worse than using Euclidean distance. Learning the distance via a network might be a good direction. However, we don't have enough (or such) datasets to support this type of training. We will continue to explore this in the future and would appreciate any suggestions from the reviewer.\n\n**Question 2: Can you elaborate on the optimization solver used for computing the barycenter? Are you using LP, Sinkhorn, or other entropic OT solvers?**\n\n**A2:** We use the free-support Wasserstein barycenter solver from the POT library (ot.lp.free_support_barycenter), which is based on linear programming (LP). We also experimented with Sinkhorn-based barycenters, but found that Sinkhorn-based barycenters tend to blur the interpolants, especially in early morphing stages. LP-based methods provide more spatially coherent transitions with better geometry preservation.\n\n**Question 3: What happens when the source and target shapes differ significantly in topology or part structure (e.g., human ↔ airplane)? Are there failure cases?**\n\n**A3.1:** In practice, our method is able to handle significant topological and structural differences between source and target shapes in many cases. For instance, as shown in Figure 3, morphing from “Elephant” to “Pyramid”, or “Digger” to “Pirate Ship”. Another example is included in Appendix Figure A5, showing a successful morphing from “Wukong” to “Train”, which again involves a major shift in object class and part layout. More videos in the form of rotating videos can be found in the supplementary demo video.\n\n**A3.2:** Our method may struggle in cases involving explicit splitting or merging of semantic parts. A representative failure case is “Sphere” → “Dining Table with Chairs”: The source is a single-mass geometry, while the target consists of multiple, semantically distinct components. During morphing, the model fails to resolve how to meaningfully split the sphere into independent regions. As a result, the intermediate shapes appear blobby or tangled, with chairs fusing into the tabletop or floating detached from the structure. \n\n**Question 4: How sensitive is your method to the choice of pre-trained encoder (e.g., CLIP vs. DINOv2) or the underlying 3D generator (Trellis)?**\n\n**A4:** Our method is designed to be modular and works with different encoders and generators. We tested both CLIP (for text prompts) and DINOv2 (for image prompts), and observed consistent performance. For 3D generators, we implemented our pipeline on both Trellis, TripoSG (Appendix Table A1), and GaussianAnything (Rebuttal Table \"**Quantitative comparison with GaussianAnything as backbone**\"); results show that our interpolation strategy consistently improves morphing quality across both backbones, confirming low sensitivity to the specific pretrained components.\n\n**Weakness 1:Limited novelty in core technique: Using Wasserstein barycenters for shape or texture interpolation is not new.**\n\n**A5:** We thank the reviewer for pointing out the rich history of optimal transport (OT) methods in shape and style interpolation. Indeed, works such as Solomon et al. (2015) and Kolkin et al. (2019) have shown the power of OT and Wasserstein barycenters in both explicit geometric spaces (e.g., meshes, voxel grids) and 2D image features.\n\nOur contribution is not simply the use of barycenters, but in adapting OT within a conditional 3D latent generative process:\n* We operate not on mesh vertices, but on condition token distributions from multimodal encoders (CLIP / DINOv2) inside a flow-based 3D generative pipeline—a fundamentally different domain with different constraints.\n* Prior OT-based morphing methods assume static geometric domains or shallow features. In contrast, our barycenter interpolation drives high-resolution textured 3D synthesis, integrated with recursive dynamics.\n* Furthermore, our method is training-free, leveraging generative priors while avoiding the need for curated training pairs\n\n**Weakness 2: Heuristic design choices: The recursive initialization strategy and similarity-guided interpolation heuristics, though empirically effective, lack theoretical justification or ablation on robustness. The texture fusion rule based on cosine similarity threshold feels ad hoc. How sensitive is the result to this threshold?**\n\n**A6:**  Recursive initialization is motivated by practical instability in latent barycenter optimization, particularly in high-dimensional latent token spaces. Linear initialization often falls into non-smooth or semantically invalid regions, as shown in our ablation (Fig. 6 and 8). The recursive method promotes trajectory continuity—a concept inspired by gradient flows on Wasserstein space.\n\nWe conduct quantitative evaluations with different threshold τ values and present the results below:\n\n| Threshold τ | FID↓ | PPL↓ | V-CLIP↑\n| ----------- | ----------- | ----------- | ----------- | \n| 0.2     | 4.54  | 2.94 |  0.88 |\n| 0.3     | 4.20  | 2.91 |  0.90 |\n| 0.4     | 4.17  | 2.91 |  0.91 |\n| 0.6     | 4.25  | 2.92 | 0.90 |\n| 0.8     | 4.49  | 2.93 | 0.87 |\n\nWe observe that the performance is robust across a reasonable range of thresholds (0.2-0.8). We set a default threshold 0.3 in our evaluation.\n\n**Weakness 3: Dependency on pre-trained components: The success of WUKONG is highly dependent on the quality and generalizability of the pre-trained encoder and 3D generator (e.g., Trellis). It is unclear how well the method generalizes to out-of-distribution prompts or categories not seen during Trellis’ training.**\n\n**A7:** We appreciate the reviewer’s concern. We agree that the quality and bias of pretrained components (e.g., CLIP, DINOv2, Trellis) inevitably affect generalization, especially for out-of-distribution or long-tail categories. That said, this dependency is inherent to all current prompt-driven 3D generation frameworks, including prior works like 3DRM, which also rely on a pretrained generator (e.g., GaussianAnything). Our method introduces no additional trainable components, which makes it lightweight, modular, and fully compatible with future generative backbones. As newer and more generalizable models emerge, our morphing framework can benefit directly without retraining.\n\nTo test real-world generalization, we applied our method to the Headspace dataset containing high-quality real 3D face scans. Even though Trellis was not trained on such data, our method achieved strong results:\n\n* **FID: 3.97, PPL: 2.88, V-CLIP: 0.91** —outperforming both MorphFlow and 3DRM under the same evaluation protocol (see \"Quantitative results on Headspace dataset\").\n\nThese results demonstrate that despite its reliance on pretrained components, WUKONG exhibits robust generalization to real 3D data and is well-positioned to support broader domains as pretrained models continue to improve.\n\n**Limitations 1: The proposed method performs well under curated prompts and well-aligned source/target categories, but might struggle with topologically dissimilar or fine-grained prompt changes.**\n\n**A8:** Please refer to A3 for a more detailed discussion on our method's ability to handle topologically dissimilar shapes/cross category objects. In practice, our method has successfully managed significant structural differences in several cases, such as “Elephant” to “Pyramid” and “Digger” to “Pirate Ship” (Figure 3). These examples demonstrate our method’s robustness to topological changes.\n\n**Limitations 2: The morphing pathway is driven purely by token interpolation in feature space, which may lack semantic control (e.g., preserving pose vs. identity).**\n\n**A9:** While the core of our method relies on token interpolation in feature space, we designed this approach with the goal of maintaining both structural coherence and texture fidelity across morphs. The selective interpolation strategy enables us to preserve key features (e.g., identity) while controlling texture blending more accurately.\nHowever, we agree that pose preservation is a critical concern in 3D morphing. We plan to explore introducing pose priors or a pose encoder to anchor the morphing process.\n\n**Limitations 3: Heavy reliance on pre-trained backbones makes it hard to decouple where the quality improvements come from—WUKONG's algorithm vs. the generator or encoder.**\n\n**A10:** Our core contributions lie in the novel morphing strategy (e.g., Wasserstein barycenter-based interpolation, recursive initialization, and selective texture interpolation). These algorithms provide significant performance improvements even when the backbone is swapped. To clarify this, we implemented \"Ours*\" on the GaussianAnything backbone (the same generator used by 3DRM), and showed that our method outperforms 3DRM even when both models use the same backbone (Rebuttal Table **\"Quantitative comparison with GaussianAnything as backbone\"**), demonstrating that the improvements come from our morphing strategy rather than just the backbone itself.\nWe will clarify these contributions further in the revised version of the paper by:\n\n* Including additional ablations to isolate the impact of our algorithm versus the pretrained encoder and generator.\n* Providing comparative performance when using different backbones to demonstrate the generality of our approach."}]

Author rebuttal:

Meta-review: This paper has received consistent positive ratings of 4, 4, 4, and 5. Reviewers highlighted the novelty of the problem definition, the quality of visual results, the completeness of the work, and the clarity of writing as its main strengths. Concerns raised included the method largely building on existing work and the lack of experiments on real-world data. In the rebuttal, the authors addressed these concerns by demonstrating the difficulty of the task, which convinced the reviewers. Given the consistent positive evaluations and reviewers’ satisfaction with the responses, I see no reason to overturn their acceptance recommendation. Therefore, I recommend acceptance.
Decision: Accept (poster)