Title: Wukong's 72 Transformations: High-fidelity Textured 3D Morphing via Flow Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D morphing

3D interpolation

Rectified flow
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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)?\n\nLIMITATIONS:\n- 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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\n\nQUESTIONS:\n- 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\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nsee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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?\n\nQUESTIONS:\nNo 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}