Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing
Authors: Xiaolu Liu
Yicong Li
Qiyuan He
Jiayin Zhu
Wei Ji
Angela Yao
Jianke Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Image-to-3D generation
Textured 3D Morphing
Primary area: generative models
TL;DR: We propose Interp3D, a correspondence-aware interpolation framework designed for smooth and plausible 3D morphing with both geometric fidelity and texture details.

Abstract: Textured 3D morphing seeks to generate smooth and plausible transitions between two 3D assets, preserving both structural coherence and fine-grained appearance. This ability is crucial not only for advancing 3D generation research but also for practical applications in animation, editing, and digital content creation. Existing approaches either operate directly on geometry, limiting them to shape-only morphing while neglecting textures, or extend 2D interpolation strategies into 3D, which often causes semantic ambiguity, structural misalignment, and texture blurring. These challenges underscore the necessity to jointly preserve geometric consistency, texture alignment, and robustness throughout the transition process. To address this, we propose Interp3D, a novel training-free framework for textured 3D morphing. It harnesses generative priors and adopts a progressive alignment principle to ensure both geometric fidelity and texture coherence. Starting from semantically aligned interpolation in condition space, Interp3D enforces structural consistency via SLAT (Structured Latent)-guided structure interpolation, and finally transfers appearance details through fine-grained texture fusion. For comprehensive evaluations, we construct a dedicated dataset, Interp3DData, with graded difficulty levels and assess generation results from fidelity, transition smoothness, and plausibility. Both quantitative metrics and human studies demonstrate the significant advantages of our proposed approach over previous methods. Source code is available at https://github.com/xiaolul2/Interp3D.

Published as a conference paper at ICLR 2026
INTERP3D: CORRESPONDENCE-AWARE INTERPOLA- TION FOR GENERATIVE TEXTURED 3D MORPHING
Xiaolu Liu Yicong Li Qiyuan He Jiayin Zhu
Wei Ji Angela Yao? Jianke Zhu 1.4
State Key Lab of CAD & CG, Zhejiang University National University of Singapore
Nanjing University *Shenzhen Loop Area Institute
Source
Morphing
Target
Source
Textured 3D Morphing
Target
Low-Quality Texture
MorphFlow
Interp3D (Ours)
Smooth Transition
Failure Transition
DiftMorpher
Interp3D (Ours)
Consistent Attributes
Incomsistent Appearance
FreeMorph
Interp3D (Ours)
Structure
Structure Collapse
Interp3D (Ours)
Fine-Grained Appeorance
Texture Blurriness
AID-O
Figure 1: We propose Interp3D, training-fres approach for continuous and plausible textured 3D morphing, consistently surpassing prior approaches with better structure fidelity, plausible appear- ance, and transition smoothness. Zoom in to check the details.
ABSTRACT
Textured 3D morphing seeks to generate smooth and plausible transitions between two 3D assets, preserving both structural coherence and fine- ine-grained appearance. This ability is crucial not only for advancing 3D generation research but also for practical applications in animation, editing, and digital content creation. Exist- ing approaches either operate directly geometry. limiting them to shape-only morphing while neglecting textures, or extend 2D interpolation strategies into 3D, which often causes semantic ambiguity, structural misalignment, and texture blur- ring. These challenges underscore the necessity to jointly preserve geometric con- sistency, texture alignment, and robustness throughout the transition process. To address this, we propose Interp3D, a novel training-free framework for textured 3D morphing. It harnesses generative priors and adopts progressive alignment principle to ensure both geometric fidelity and texture coherence. Starting from se- mantically aligned interpolation in condition space, Interp3D enforces structural consistency via SLAT (Structured Latent) guidded structure interpolation, and fi- nally transfers appearance details through finegrriieed texture fusion. For compre- hensive evaluations, we construct dedicated dataset, Interp3DData, with graded difficulty levels and assess generation results from fidelity, transition smoothness, and plausibilityy. Both quantitative metrics and human studies demonstrate the sig-
"Corresponding authors.

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Published as a conference paper at ICLR 2026
nificant advantages of our proposed approach over previous methods. Source code is available at
INTRODUCTION
Textured 3D morphing aims to generate smooth and consistent transitions between two 3D as- sets (Lipman et al., 2022; Yang et al., 2025). The core is to preserve natural evolution while avoiding semantically meaningless artifacts or abrupt changes. By integrating both structure and appearance evolution, coherent transitions are essential to support generative tasks, including animation (Ren ct al., 2024), editing (Alimohammadi et al., 2025), and motion tasks (Miao et al., 2025; Nag et al., 2025). In practice, textured 3D morphing is also crucial for effects visualization and filmmaking, such as character or biological evolution in games and morphological changes, enabling realistic and immersive visual narratives that meet the demand for continuous variations.
Nevertheless, achieving fidelity and plausible transitions remains challenging. Prior studies have partially addressed this issue. One major paradigm is traditional deformation-based approaches, which operate on point clouds or meshes (Yan et al., 2007; Eisenberger et al., 2021) to establish explicit geometric correspondences, and then compute deformation trajectory to generate interme- diate states (Aydmhilar & Sahillioglu, 2021; Vyas et al., 2021; Zhan et al., 2024). However, relying on strict geometric alignment and consistent topology, these approaches are restricted to shape-only interpolation and neglect textures, often leading to ambiguous matching and unnatural deformations.
More recently, generative models have been widely developed (Sauer et al., 2023; Podell et al., 2023) as the basis prior for morphing in 2D. For image morphing, interpolations on noises (Shen et al., 2024), attention mechanisms (Zhang et al., 2024; Alimohammadi et al., 2025), and conditional fea- tures (He et al., 2024; Wang & Golland, 2023) have been investigated to achieve semantic texture transitions, offering an inherent ability to generate and create visually detailed surface appearances. Extending these ideas to 3D generally follows two routes: (1) morphing in image space to guide the 3D process (Sun et al., 2024), or (2) adapting 2D strategies to 3D generative models (Yang et al., 2025). However, both are essentially 2D-native: the former suffers from view inconsistency and error accumulation, while the latter ignores structural correspondences and struggles with scale or topology variations. Consequently, they often produce distorted or implausible transitions with blurred geometry and unstable textures, just as shown in Figure 1.
Considering the above limitations, our key insight to couple gen- erative priors with reliable 3D correspondences for faithful morph- ing. However, such a combination is non-trivial, particularly when structural and semantic gaps cause unstable correspondence. This motivated us to advocate a progressive three-stage alignment prin- ciple. Taking the morphing from Mario to Bread in Figure 2 an example, semantic alignment acts as a high-level planner, estab- lishing a conceptual map that ensures Mario's head matches with Bread's 's head, instead of its belly. Structural aligument then reg- ularizes the deformation between matched parts, as semantic cor- respondence alone cannot handle large shape gaps, which require smooth and scale-consistent deformation to avoid collapse. Finally, based on the aligned structure, texture alignment transfers materials and re-synthesizes details to ensure the consistency between their outfit, avoiding blurry blends or texture popping.
Inter States
Semantic Misalignment
Unstable
Structure
Texture Hlurring Ours
Figure/ 2: Artifacts caused by missing correspondence alignments.
Motivated by the above discussions, we propose Interp3D, a training-frre free approach that instantiates the progressive alignment principle based on generative priors for textured 3D morphing. Interp3D refines the source-target correspondences across three perspectives. Firstly, the 2D condition level, we establish the semantic-aligned condition interpolation to ensure that the global context of the source and target is meaningfully matched. Then, we leverage the strong generative prior of the structured latent features from the 3D foundation model to maintain the geometric correspondences for structure generation, where the fused attention interpolation with dynamic patch matching is applied to promote coherent structural evolution. Finally, retrieve the source and target features at their corresponding locations to apply weighted fusion so that reasonable appearance and fine details can be transferred at the texture level. Such progressive strategy allows Interp3D effectively
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address the semantic ambiguity, geometric inconsistency, and texture blurring during the generation process, producing morphing trajectories with both structural fidelity and textural coherence.
For comprehensive evaluations, construct dedicated dataset. Interp3DData, with graded diffi- culty levels to assess from the fidelity, transition smoothness, and plausibility perspectives. Experi- mental results show that our Interp3D achieves consistently superior performance in both structural and perceptual quality. Our main contributions can be summarized as follows:
(1) We analyze the challenges of existing textured 3D morphing paradigms and highlight the neces- sity to couple correspondence with the 3D generative priors for structural and textural consisteney.
(2) We propose Interp3ID, training-free, correspondence-aween morphing framework that integrates progressive alignment into the 3D generation process, preserving faithful morphing through three stages: Semantic, Structural, and Texture alignment.
(3) We curate Interp3DData, a benchmark dataset for textured 3D morphing categorized into three difficulty levels, on which our method achieves superior performance over prior baselines, especially on the most challenging hard cases.
2 RELATED WORK
3D Morphing. Morphing in 3D aims to generate smooth and consistent transitions between dif- ferent shapes and textures. Traditionally, geometric methods operate directly on 3D representations through explicit interpolations or deformation (Tam et al., 2012; Yan et al., 2007), which can be broadly divided into manifold-based and deformation field approaches. The former (Heeren et al., 2012; Kilian et al., 2007) formulates interpolation as geodesic paths on a recovered shape manifold, while the latter (Eisenberger et al., 2019; 2021) directly estimates transformations between shapes under isometric assumptions. which are limited by the need for strict vertex correspondence and of- ten struggle with shapes with varying topology. Based neural radiance fields, MorphFlow (Tsai et al., 2022) formulates volumetric interpolation via optimal transport to synthesize view-consistent intermediate states. Later, interpolation in the latent feature space of generation model provides way to achieve morphing (Achlioptas et al., 2018; Groueix et al., 2018). More recent works ex- tend this idea beyond geometry. L4GM (Ren et al., 2024) interpolates RGB frames for 4D dynamic reconstruction at the image level. Yang et al. (2025) further introduces the first regenerative 3D mor- phing method built upon generative 3D models. Later, Yin et al. (2025) achieves interpolation via optimal transport barycenter on condition features. Inspired by these advances, we enable flexible generation of intermediate results with richer structural details and more realistic textures.
3D Generative Models. Initially, Generative Adversarial Networks (GANs) (Wu et al., 2016; Gao al., 2022) are used for 3D generation with simple structures. Later approaches rely on diffusion models (Ho et al., 2020) with different 3D representations, such point clouds (Nichol et al., 2022), meshes (Liu et al., 2023), Radiance Fields (Hong et al., 2024), and 3D Gaussians (Lan et al., 2025). Previous Score Distillation Sampling (Poole et al., 2022; Lin et al., 2023; Zhu et al., 2025) distills 3D information from 2D diffusion models. In spite of the encouraging results, these methods often suffer from limited fidelity and inefficient optimization. For high quality and efficient 3D) generation, recent methods encode 3D data into a compact latent space with Variational Autoencoders (VAEs) and train diffusion models on these latents for scalable generation (Chen et al., 2025a; Lan et al., 2024). Among them, Trellis (Xiang et al., 2025) constructs a versatile structured latent space that can be decoded into various 3D representations. We utilize Trellis as the 3D diffusion prior, which interpolation.
contains structure and semantic latent space for correspondence-aveee
Interpolation in Generative Models. Interpolation in generative models has been widely explored for the tasks of morphing (Lin et al., 2025; Cao et al., 2025; Shen et al., 2024) and editing (Alimo- hammadi et al., 2025) in image and video generations (He et al., 2025: Liao et al., 2025). Previ- ously, latent space interpolation has been studied in early generative models such as GANs (Fish et al., 2020; Park et al., 2020) and VAES (Kingma& & Welling, 2014), but their limited generalization restricts them to cases with simple semantics or highly similar structures. Recent advances in diffu- sion models have greatly improved interpolation for image morphing. For instance, AID (He et al., 2024) introduces attention-basee based interpolation. IMPUS (Yang et al., 2024) and DiffMorpher (Zhang et al., 2024) refine text embeddings with LoRA fine-tuning (Hu et al., 2022) for smoother transi- tions. Later, FreeMorph (Cao al., 2025) achieves tuning-free image morphing with controllable
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Semantic-Aligned
SLAT--Guded Strueture Interpolation
Inter
Condition Interpolation
Targer
SLAT Patch Matching
Source Semantic-Alignee
B
B
Origin Allention
Condition Interpolation
@
3D Structure Generation
DINOv2
DINOv2
SLAT-Guided
Fused Attention
Attention
Structure Interpolation
PSLAT
Cos,
x
n2
X
SLAT Generation
c. Fine-grained Texture Fusion
Texture Fusioo
SLAT Decoder
Source
Slim
x(1
X(1 a
G
G
normed
Framework Overview
Component Designs
Figure 3: Pipeline Overview. The left presents the overall framework. The right highlights com- ponent designs. Based on the 3D generation prior, the interpolation is progressively enhanced from three aspects: (a) semantic-Aligned Condition Interpolation, (b) SLAT-Guided Structure Inter- polation in structure generation, and (c) Fine-Grained Texture Fusion for appearance refinement.
slerp interpolation. Despite extensive explorations 2D space, interpolation in 3D space remains underexplored. in which the requirements for structure and semantic alignments are less explored in 2D morphing.
PRELIMINARIES
TRELLIS Structure. We adopt the pre-trained TRELLIS (Xiang et al., 2025) as our 3D genera- tive prior, which encodes unified Structural Latent (SLAT) representation for versatile decoding. Guided by embedded image conditions c, TRELLIS operates in two diffusion stages: (1) Stage- Structure Generation, it predicts the of active voxel positions (p. 1 E {0, 1,...,N 1}3. where denotes the grid resolution and is the number of active voxels, (2) then in Stage-2 SLAT Generation, building on the geometry, it recovers the texture-aat aware SLAT feature (Zv, Po)
where each latent Ze E I encodes fine finegrained appearance at voxel Pe-
Finally, the SLAT decoder maps z its corresponding 3D Gaussians G. Both generative stages are built upon rectified flow transformers (Lipman et al., 2022), which iteratively denoise latent codes into clean structural and textural representations.
Attention Interpolation. In generative transformers, latent tokens are projected into query (Q), key (K), and value (V) spaces within each block to calculate attention. In tasks generative morphing, attention-based interpolation is widely adopted (Zhang et al., 2024; Shen et al., 2024) to fuse the features from the source and target during the generation process. Given the i-th sample with interpolation ratio Og E [0, 1]. the interpolated attention is defined as:
InterpAttn( Q;, K;, Vi) Aun(Q., (1 a;)K, + a,K,, (1-a;)V + a.V)),
(I)
where K; and V; are replaced by the interpolated source and target (Ks, K,, V., V/). This formula- tion is a direct linear combination of source and target features within the attention mechanism.
4 METHODOLOGGY
Given the source and target image prompts I. and I, our goal is to generate the sequence of textured 3D assets (9.) with smooth and plausible transitions, in which L represents the length of the sequence. Go is generated from the source I,, and Gr, is from the target Tr. As shown in Figure 3,
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Interp3D is designed to progressively enforce the aligned interpolation from three complemen- tary perspectives. Firstly, we establish the correspondences between the source and target condition embeddings (Section 4.1) with semantic-aligned condition interpolation. Then, for plausible struc- ture learning, the SLAT-Guuded Structure Interpolation (Section 4.2) extends this alignment into 31) structure space. Finally, the Fing-Grained Texture Fusion (Section 4.3) bidirectionally aggregates source and target appearance details, ensuring coherent and realistic surface appearance.
4.1 SEMANTIC-ALIGNER CONDITION INTERPOLATION..
In morphing tasks, interpolation in the condition space is often used guide the denoising process in diffusion models. While effective in 2D settings, this naive strategy casily ignores semantic correspondences in 3D generation, leading feature mixing and visual artifacts. To enable faithful morphing, it is thus crucial to enforce semantic alignment between source and target conditions.
Given the input source and target images I, and It the embedded conditions c, and c are ex- tracted by DINOv2 (Oquab et al., 2024), which provides strong representation ability and semantic information to serve as a reliable basis for correspondence estimation. Let C, = {cs.s and
C = { MM denote the patch-level embeddings with M tokens each. We compute patch-level cosine similarities and formulate the correspondence estimation as an assignment problem:
M (Ca.s (1,((k) ||c. [|
π arg max TEPM
(2)
where x* denotes the optimal one-to-one mapping, and PM the set of all possible patch permu- tations over M patches. The target embeddings are then re-permuted according to π (-), which yields semantically aligned representation consistent with c..
On top of this alignment, we perform the corresponding interpolation with the ratio coefficients {a, E [0, 11 The interpolation is performed as token-wise convex combinations of matched pairs, allowing each patch evolve smoothly toward its semantic counterpart and providing more reliable guidance for the morphing:
= as) ( + a x (k)
This alignment ensures that interpolation is performed between semantically consistent target token pairs. As a result, the intermediate conditions remain plausible and provide reliable guidance for the morphing trajectory, substantially reducing inconsistencies and preventing category-level mismatches from the outset.
4.2 SLAT-GUIDED STRUCTURE INTERPOLATION
While condition interpolation e; provides semantically aligned guidance, it is limited single-view 2D semantics and thus is hard to capture the spatial correspondence required for the intermediate 3D structural generation. To address this limitation, we introduce SLAT features from the source and target generation process as geometric guidance during the 3D generation process. As ver- satile representation from structures and appearance, SLAT enables the construction of dynamic patch-level correspondences between the source and target, providing a principled mechanism for structure-aware interpolation.
Dynamic Patch Correspondence. As the denoising process follows a coarse-to-five progression (Alimohammadi et al., 2025) early denoising steps predominantly recover global structural layouts, whereas later steps refine fine- fine-grained details. Motivated by this, we design dynamic patch corre- spondence mechanism that adapts its granularity across timesteps.
Specifically, we densify and project the sparse source and target SLAT features JSLAT and J into the same grid resolution as KV maps in stage-1 of structure learning. The grids are partitioned into
patches with side length 8; at denoise step l. Thus, the number of grid patches can be G N
We then compute the cosine similarity between patch features rather than individual tokens. Only pairs with similarity above the threshold To retained for correspondence estimation, while the remaining unmatched patches are left to maintain the origin permutation. Formally, the optimal correspondence x can be obtained by:
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G
' = arg max
Σ
sim A
(4)
TEPG
pag=1
(SLAT are the corresponding SLAT patch features at source and target patch cell Ja. gt. As denoising proceeds and the embeddings become increasingly reliable, the patch size 8t is progressively reduced, ensuring robust coarse alignment in early stages and finer in later stages, adapting dynamically to the fidelity of the learned representations.
where fap, J..qq
Given the estimated correspondence π" (-), we construct the permutation matrix Pa at the KV space in the transformer of the structure generation process, which then applied the target geometric K2, V22 maps to achieve the alignment with the source K,, V,:
K R V2500 : Pa- (K? V,,00).
(5)
This operation ensures that the source and target attention tokens are permuted accordingly to the guidance of the SLAT feature, providing the structure alignment for fused attention.
Fused Attention Interpolation. As illustrated the Preliminary, the basic attention interpolation simply replaces the K, 1/2 pairs with an interpolated version, ignoring the continuity with the gen- erated intermediate states and the requirement for reliable source target alignment.
Inspired by (He et al., 2024), we design the correspondence-wvare fused attention interpolation based on the repermuted KV maps. For the self-attention in each transformer layer, we con- catenate the K,, Vi, which is constructed from the i-th interpolated condition with the aligned K (s.t): Via.e from the source and target to perform the fused outer-interpolated attention with Qi. which can be formulated as:
Qi : (1 o..) SelfAutn(Qe
K K goo
/ge0
12000
ta; * SelfAttn (Q; k2
k3000
1,000
1/800
This fused attention with SLAT--uuided permutation enables each query to integrate the aligned structural information from both source and target conditions while preserving its own spatial cues, towards a more reasonable and structurally coherent 3D structure alignment.
4.3 FINE-GRAINED TEXTURE FUSION
Based on the SLAT-guided structure learning, we further refine the texture representations to ensure smooth and consistent appearance transitions. As direct interpolation or binding textures to vox- els often causes color distortion blurring, especially when the source and target objects exhibit significant texture differences. Moreover, structural discrepancies between the source and target 3D objects result in different numbers of active voxels, making it impractical directly adopt 2D interpolation strategies for texture learning based on different structures.
To address these issues, we introduce the fine-grained texture fusion strategy. The key idea to perform bidirectional weighted aggregation of texture features from both source and target, which enables robust and consistent texture fusion regardless voxel count differences.
Concretely, for each projected j-th token K from the i-th morphing process, we identify its most relevant counterparts from the source and target generation by selecting the features with the highest similarity, which can be formulated as follows:
m arg max sim (Ktx K (..) nt" arg max sim [K
1,67 (tn)
Here m² and n² represent the indices of the source and target tokens that are most similar the e-th token in Kiex
With the matched indices, each intermediate token updated through weighted aggregation of source, target, and its own feature. This approach ensures that the intermediate tokens not only inherit information from both endpoints but also retain their evolving identity during morphing, rather than collapsing into a simple linear blend. To maintain numerical stability and avoid feature magnitude drift, we further apply & normalization:
K 8,0
Rex 1,0
1,00
where Ketex = (1 - az) K-tex + az K + tex
(8)

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Morphing Sequence
Target
Source
Morphing Sequence
Target
MorphFlow
Din- Morpher
FreeMorph
CIC
AID-I
AID-
Interp3D
(Ourg)
Figure 4: Qualitative Results. Our Interp3D achieves smooth and plausible 3D morphing.
The same update scheme is applied to the value tokens Viex ensuring consistency between keys and values throughout fusion. By jointly combining information from both source and target while retaining the intermediate feature, this design prevents collapsing toward either endpoint. Together, these choices allow each token to progressively shift toward source-target consensus while preserv- ing its own structural cues, resulting in coherent texture alignment across varying voxel resolutions.
EXPERIMENTS
5.1 IMPLEMENTATION DETAILS
We utilize the publicly available Trellis (Xiang et al., 2025) as our 3D diffusion prior, which consists 25 denoising steps for stage-1 structure generation and stage-2 latent construction, separately. The grid resolution is set to N 64, and the SLAT dimension C = 8. Our approach can be integrated with this pipeline in training-free manner. dynamic patch correspondence, the maximum cube size is set to 43, which is exponentially reduced as denoising progresses. Following AID (He et al., sample non-uniform interpolation
2024), we adopt the Beta distribution with parameters 3 coefficients. It places more emphasis intermediate interpolation states, leading stable and balanced transition results. All experiments are conducted on a single NVIDIA RTX A5000 GPU.
5.2 EVALUATION BENCHMARKS
Dataset Construction. For comprehensive evaluations, we construct Interp3DData, a dataset con- sisting of 57 pairs categorized into three difficulty levels, including easy, medium, and hard, which are collected from the Objerverse-Xi dataset (Deitke al., 2023), TRELLIS repositories! and the Sketchfab website?. Each set contains 19 pairs Categories include humans, objects, buildings, cartoon characters, and others. All selected cases comply with copyright and license requirements for research use. The difficulty levels are determined by geometric complexity, textural richness,
'https://githubs "https://sketchff

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Easy
Mid
Hard
Average
Method
FID|
PPL.
LPIPS
FID
PPL.
LPIPS|
FIDI
PPL
LPIPS
FID|
PPL.
LPIPS!
MorphFlow
101.36
2.79
0.111
107.57
2.96
0.156
105.71
2.92
0.187
104.88
2.89
0,151
DiffMorpher
160.93
4.18
0.088
177.45
4.16
0.113
170.26
4.92
0.183
169.54
4.42
0.128
FreeMorph
114.01
5.36
0.120
124.02
6.16
0.160
135.69
5.31
0.217
124.57
5.61
0,166
AID-I
84.35
2.92
0.072
84.64
3.10
0.104
94.64
3.57
0.159
87.88
3.20
0.112
AID-O 77.65
2.75
0.068
83.62
2.78
0.092
81.81
3.24
0.145
81.03
2.92
0.102
Interp3D (Ours)
70.79
2.42
0.059
83.58
2.37
0.079
82.54
2.62
0.119
78.97
2.47
0.086
Table I: Quantitative evaluation Interp3DData. We report FID, PPL, and LPIPS across three
difficult levels and the average. Lower scores indicate better consistency and transition smoothness. The best is highlighted in bold.
and the structural discrepancy between the source and the target. We generate 7-frame morphing sequences systematically assess the quality of transitions under varying levels of difficulty.
Evaluation Metrics. For comprehensive quality assessment, we evaluate the generated 3D) se- quences from three complementary aspects, including fidelity, transition smoothness, and plausi- bility. For fidelity, we employ the Fréchet Inception Distance (FID) (Heusel et al., 2017), which evaluates the distributional alignment between generated interpolations and the source source-target data. To obtain a stable estimation, we render 1,000 views for the two selected intermediate frames, along with the corresponding source and target groups For transition smoothness and consistency, we adopt Perceptual Path Length (PPL) (Karras et al., 2020) and the averaged Learned Perceptual Im- age Patch Similarity (LPIPS) (Zhang et al., 2018). PPL accumulates the normalized perceptual distances along the trajectory capture transition regularity, and LPIPS quantifies local coherence between adjacent frames. Both metrics are averaged over 64 rendered views.
Beyond quantitative metrics, include user studies in which participants provide ratings fidelity, smoothness, plausibility, and overall quality, offering a more comprehensive evaluation.
5.3 QUANTITATIVE EVALUATIONS
Baseline Selection. As few methods are designed for textured 3D morphing, we select baselines from three representative directions, each with publicly available implementations:
(1) MorphFlow (Tsai et al., 2022) an optimization method on neural radiance fields, which interpo- lates volumetric representations via optimal transport to enable niewcconsistent textured morphing.
(2) AID-I/O (He et al., 2024) basic investigation of inner and outer attention fused interpolation in 2D diffusion models. We extend its techniques into the 3D generation domain.
(3) DiffMorpher/ FreeMorph (Zhang et al., 2024; Cao et al., 2025) are image morphing methods that perform interpolation in 2D diffusion models. We integrate 2D morphing outputs into 3D generation pipelines, where the morphed images are served as inputs to synthesize sequential 3D shapes.
All these selected baselines establish solid foundation for fair and comprehensive comparison with the most relevant existing methods.
Evaluation of Fidelity and Transition Smoothness. We evaluate fidelity and transition smoothness using FID, PPL, and LPIPS. As shown in Table 1, MorphFlow yields the 104.88 FID, reflecting poor vol- umetric generation quality. Despite its un- usually low PPL and LPIPS, these val- ues mainly result from diminished tex- ture variation and oversimplifted outputs, rather than genuinely smooth or coherent transitions. DiftMMrpphrr and FreeMorph, which couple 2D morphing results, also perform poorly, producing PPL value 4.42 and 5.61 respectively due to inconsistencies when feeding 2D outputs to 3D models. When apply AID-I/O on 3D diffusion models, they achieve more stable geometry but still fail preserve texture fidelity. leading higher LPIPS. Conherently with the visual examples in Figure 4, our method achieves 78.97 FID score and 0.086 LPIPS, demonstrating the necessity of employing the combination of progressive alignment with 3D generative priors.
Method
Fidelity Smoothness? Plausibility Overall
DiffMorpher
2.35%
1.57%
1.96%
1.96%
FreeMorph
9.02%
12.16%
10.20%
10.46%
AID-O 16.86%
12.94%
18.43%
16.08%
MorphFlow 17.65%
23.14%
11.37%
17.39%
Interp3D (Ours)
54.12%
50.20%
58.04%
54.12%
Table 2: User study on Interp3I against baselines. Higher values indicate stronger transition fidelity, smoother morphing, and better visual plausibility.
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Easy
Hard
Average
Method
FID
PPL.
LPIPS]
FID
PPL.
LPIPS.
FID,
PPL|
LPIPS]
FID
PPL.
LPIPS!
Initial Condition Interp.
79.14
3.02
0.074
86.87
3.25
0,109
90.64
3.47
0.157
85.55
3.25
0.113
Semantic Align.
75.09
2.75
0.067
86.82
2.98
0.101
88.63
3.24
0.146
83.51
2.99
0.105
Structure Interp.
73.81
2.67
0.066
84.64
2.61
0.088
86.42
3.21
0.143
81.62
2.83
0.098
Texture Fusion
70.79
2.42
0.059
83.58
2.37
0.079
82.54
2.62
0.119
78.97
2.47
0.086
Table 3: Ablation study the progressive correspondence-awant component design across different difficulty levels. "Semantic alin." and "Structure interp." denote our semantic-aligned condition interpolation and SL.AT-guided structure interpolation modules. Best results are highlighted in bold.
User Study. To evaluate perceptual plausibility and visual preference, we conducted user study with 30 volunteers. Each participant was asked assess 15 morphing cases by selecting the most convincing results among different methods. The proportion of selections for each method is ported in Table 2. The evaluation focused on three aspects: transition smoothness, structural con- sistency, and textural plausibility of the generated sequences. Interp3D is preferred by volunteers with overall probability 54.12% for its plausible transition sequences and preserving fine- fine-graing grained appearance details in intermediate states.
5.4 VISUALIZED EVALUATIONS
Source
Target
Figure 1 and Figure 4 showcase representative morphing results. Interp3D produces smooth and semantically coherent transitions, with well- preserved geometry and fine-grained textures. contrast, baselines often exhibit artifacts such as blurred textures, structural collapse, or in- consistent intermediate frames. These qualita- tive comparisons highlight the advantage of our correspondence-wware progressive design in gen- erating visually plausible morphing trajectories. Additional visualizations are provided in the sup- plementary materials.
A: Initall Condition
Interpolation
Sementic Alignelllee Condition Interpelation
D: Structure Interpolation Texture Fusion
Tarpel
Source
Target
5.5 ABLATION STUDY
In Table and Figure 5, we analyze the effec- tiveness of designs through both quantitative metrics and visualization. Based on Table 3, each progressively aligned component brings consis- tent improvements across all difficulty levels. Se- mantic alignment improve the consistency, es- picially for ease cases with +4.06 FID reduced, highlighting its role in establishing meaningful correspondences. Structure interpolation further imporove the fidelity. Finally combining fine-gras fineggrained texture fusion yields the best overall perfor- mance, especially for hard cases, with +0.59 PPL promotion and +0.024 LPIPS.
wio Structure Interpolation Afler Siructure Interpolation
Figure 5: Visualized analysis for the effects of each component design.
In Figure 5 for the red rabbit-to-tiger case, the semantic-aligned condition interpolation effectively corrects semantic mismatches and sharpens structural boundaries. shown in the below example, incorporating SLAT-guided structure interpolation strengthens structural consistency and alleviates custure-induced ambiguities, effectively handling cases like the structural gap between sofa and chair or the blurred hand poses in the minions transition.
CONCLUSION
In this work, we propose Interp3D, correspondence-await interpolation framework for gener- ative textured 3D morphing. We design progressive scheme to enforce alignment throughout the generative process, from semanticall--aligned condition interpolation to SLATT-guided struc-

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Published as a conference paper at ICLR 2026
ture interpolation, and finally fine-grained texture fusion. This ensures both geometric fidelity and detail-pue preserving texture transitions. For evaluation, we constructed the dataset Interp3DData across difficulty levels, with performance assessed in consistency, transition smoothness, and plausibility. Results demonstrate that Interp3I outperforms existing baselines, delivering more coherent and el- egant 3D transitions. We hope that work can inspire future research in the field of 3D generation and beyond. A promising future direction lies in handling cases that share little semantic relevance, building reliable correspondences for plausible transitions under such conditions remains sig- nificant challenge.
ETHICS STATEMENT
This work focuses on generative 3D morphing with textured outputs. All experiments are conducted on publicly available datasets, which contain 3D assets released under appropriate usage terms. Our method does not involve personally identifiable information, sensitive data.
For the user study, volunteers were recruited with informed consent. studies were carried out in accordance with ethical guidelines to ensure participants were treated fairly and respectfully, with no personally identifiable information collected. The user feedback was used solely for research purposes.
Our method is intended for academic research in areas such as animation, creative design, and inter- active editing. We acknowledge that generative techniques could be misused to produce misleading or harmful content, and therefore we emphasize responsible use of our models and results in align- ment with community ethical standards.
REPRODUCIBILITY STATEMENT
We are committed ensuring the reproducibilityy of our work. All datasets used in this paper are publicly available, and detailed dataset sources are provided in the appendix. The model architec- tures, dataset settings, and experiment configurations are documented the main text supple- mentary materials. We provide the Interp3DData here, and our code is publicly available.
ACKNOWLEDDEMEEN
This work is supported by the National Natural Science Foundation of China under Grant No.62376244. It is also supported by the Information Technology Center and State Key Lab of CAD&CG, Zhejiang University. We would like to acknowledge that computational work involved in this research is partially supported by NUS IT's Research Computing group using grant number NUSREC-HPC-0000.
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A APPENDIX
In this Appendix, we provide more details on our implementation, experiments, visualization, and analysis as follows:
A.1: Generalization Ability Analysis.
A.2: Implementation Details and Baseline Methods.
A.4: Analysis Interp3DDatta
A.3: More Detailed Ablations.
A.5: More Qualitative Results.
A.6: Application and Future Analysis.
A.7: Failure Case Analysis.
A.1 INTERP3D GENERALIZATION ABILITY ANALYSIS.
Interp3D is designed as model-agnostic concept. The key design is that progressive 3D correspon- dence modeling guided by native feature cues from the source and target generation process, which be applied for different 3D generation models.
To validate this, we implemented Interp3D across two additional 3D generation baselines, LN3Diff (Lan et al., 2024) and 3DTopia-XL. (Chen et al., 2025b). Based on the baselines, first perform semantic-awarc condition interpolation, and then instantiate the structural correspondence using each model's native geometric embeddings (e.g., LN3Diff's 3D latent tokens, 3DTopia's primitive-level descriptors). These features provide stable geometric cues that let our structural alignment module plug without modifying the backbone architectures. Finally, we apply our texture-refinement on their generative pipelines to complete the progressive alignment.
As shown Figure both approaches produce smooth and plausible morphings, confirming that Interp3D is principled, architecture design rather than model-specific enhancement.
LN3Diff
Source
Target
Source
Target
Source
Target
3oopia--XI
Source
Target
Source
Target
Source
Target
Figure 6: Visualization of Interp3D based on different 3D generation baselines.
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A.2
IMPLEMENTATION AND BASELINE METHODS
A.2.1 PSEUDO CODE
To better illustrate the overall workflow of Interp3D, we provide pscudocode description of the en- tire pipeline. This step-by-step outline highlights the progressive alignment across semantic, struc- tural, and textural levels, making the design and execution of our method clearer.
Algorithm 1 Interp3D Framework with Progressive Diffusson Steps
Require: Source prompt L. target prompt 4, interpolation ratios {=:) L-1
Ensure: Morphing sequence S (g. =
1: Extract embeddings cs, C₁ with DINOv2
2: Estimate semantic correspondences x² by Eq.
3: Semantic-Alignee Condition Interpolation
Interpolate aligned embeddings for a, by Eq.
4: for each interpolation ratio a; do
5: for diffusion step 1 to T do
6: SLAT-Guided Structure Interpolation
Project SLAT features into KV grid resolution
Partition into patches, estimate dynamic correspondences by Eq.
Permute target KV maps by Eq. 5
Fuse attention with interpolated queries by Eq. 6 Fine-Grained Texture Fusion
7:
Find the most similar source/target tokens by Eq. Aggregate and normalize by Eq. 8
8: end for
9: Decode updated features into 3D Gaussians G;
10:
end for
Output: Morphing sequence {9:) 1-1
A.2.2 EVALUATION DETAILS
We adopt three widely used metrics to evaluate the quality and consistency of textured 3D morphing.
FID (Fréchet Inception Distance). The Fréchet Inception Distance (FID) (Heusel al., 2017) is a widely adopted metric for evaluating generative models. measures the distance between the feature distributions of generated samples and real data, extracted by pretrained Inception network. lower FID score indicates that the generated outputs are closer to real samples in distribution.
In our textured 3D morphing task, the reference distribution is constructed from the source and target renderings, while the generated distribution is formed by 2 randomly selected intermediate states. This design evaluates whether the interpolated sequence lies within the perceptual manifold spanned by the endpoints, penalizing trajectories that drift away from both the source and target. For each state, we render 1000 images from multiple viewpoints compute stable feature statistics. FID is then averaged across all intermediate steps, providing an overall measure of fidelity and consistency of the morphing trajectory.
LPIPS (Learned Perceptual Image Patch Similarity). LPIPS (Zhang et al., 2018) measures per- ceptual similarity between generated and reference renderings. using the pretrained VGG backbone as the feature extractor. For each case, render 64 intermediate frames and compute the averaged LPIPS across all adjacent image pairs the sequence to evaluate the transition smoothness, then average over all frames.
PPL (Perceptual Path Length). PPL evaluates the smoothness of interpolation trajectories by quantifying the perceptual difference between consecutive samples. Based on StyleGAN's defini- tion (Sauer et al., 2023), which computes expected local smoothness under infinitesimal perturba- tions, here adapt PPL to 3D morphing by normalizing the cumulative perceptual distance along the interpolation path with respect to the source- target perceptual distance. For each morphing tra- jectory, 64 rendered frames sampled, the frame-to-frame perceptual distances summed along
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the path, and the overall average PPL across 64 views is reported. Smaller values indicate smoother and more consistent transitions.
A.2.3 BASELINE METHOSS
MorphFlow (Tsai et al., 2022). MorphFlow addresses the task of multiview image morphing, where the goal is to generate intermediate renderings between ensuring cross-vied view consistency. It is based on volumetric scene representation that jointly models geometry and appearance for each imput set. The morphing process is formulated as an optimiza- tion that combines rigid transformation with aptimal-transpor interpolation under the Wasserstein metric. In our experiments, we firstly use BlenderNeRF to obtain multi-view images together with their camera annotations, and generate the morphing results using the authors" open-source implementation 4 with default parameter settings.
of multiview images while
DiffMorpher (Zhang al., 2024). DiffMorpher tackles the problem of smooth image interpo- lation with diffusion models, whose latent spaces are otherwise unstructured and unsuitable for morphing. It captures the semantics source and target images by fitting separate LoRAs and terpolates both the LoRA parameters and latent noises to generate coherent intermediate results. In our experiments, we use the open-sourced implementations to generate the sequential images first, then the 3D generation model follows to lift the images into 3D sequences.
Forph (Cao et al., 2025). FreeMorph is tuning-free approach for instant image morphing, aiming to generate realistic and directional transitions between two images. It enhances diffusion models with guidance-aware spherical interpolation, which blends the key and value features in self- attention, and introduces a step-oriented variation trend to ensure controlled and consistent transi- tions. In experiments, we the open-sourced implementations to generate the sequential images first, then the 3D generation model follows to lift the images into 3D sequences.
AID (He al., 2024). AID (Attention Interpolation of Diffusion) improves conditional interpo- lation in diffusion models by directly modifying attention operations. The fused inner- imer-interpolated attention (AID-I) fuses and interpolates keys and values before attention:
Atto Attoin(Qs: a.) = Att(( Qi [(1 - -a,),, + aa,K, Ki), [[1 Vv.V + a,V, vil).
The fused outer-interpolated attention (AID-O) fuses and interpolates the outputs of two attentions:
Attmgat (Qa;)) (1 a.) Attn Q+ [K,, K,), [V,, Vil)
a; Att(( Qi, [K,, K2), [V, Vil)-
In our experiments, we apply the open-source implementations' both the AID-I and AID-O with basic condition interpolation in the structure generation process.
A.3
MORE DETAILED EXPERIMENTS.
A.3.1 EVALUATION SEMANTIC AND STRUCTURE FIDELITY
We introduce two additional metrics that evaluate the 3D structural fidelity and semantic coherence:
P-KID (Point-KID). We extract PointNet (Qi et al., 2017) features from 3D point samples of the generated intermediate shapes and the source/target shapes, and then compute a Kernel Inception Distance between these distributions. Lower P- K-KID indicates that the intermediate shapes stay closer to the structural manifold spanned by the endpoints, thus reflecting better 3D geometric fidelity.
ffusion
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CLIP-Dis/ CLIP-Sim. CLiP-based distance and similarity scores that quantify semantic alignment among the transition process. We use CLIP image features (Radford et al., 2021) to measure the av- eraged adjacent- frame distance (CLIP-Dis, lower is better) and cosine similarity (CLIP-Sim) higher hetter), capturing whether semantic evolution is smooth and coherent.
As shown in the Table 4 below, evaluate both method and prior morphing approaches under these new metrics on the whole Inter, and Interp3D consistently achieves better semantic continuity and geometric stability, confirming that our improvements extend beyond visual quality.
Method
P-KID +
CLIP-Dis
CLIP-Sim 1
DiffMorpher
0,6796
0.1253
0,8747
FreeMorph
0.5352
0.1034
0.8966
AID-I
0,4857
0,0651
0,9349
AID-O
0,5060
0.0603
0.9397
Interp3D (Ours)
0.3961
0.0529
0.9471
Table Quantitative evaluation on semantic and structure fidelity metrics.
Easy
Mid
Hard
Average
A
D
FID PPL. LPIPS.)
FID PPI. LPIPS.
FID
PPI.) LPIPS.)
FIDJ, PPI. (PP.ILLPP LPIPS.
79.14 3.02
0.074
86.87 3.25
0.109
90.64 3.47
0.157
85.55 3.25
0.113
75.09 2.75
0.067
86.82 2.98
0.101
88.63 3.24
0.146
83.51
2.99
0.105
77.55 2.79
0.068
86.13 2.81
0.093
81.28 3.26
0.140
81.65 2.95
0.102
77.37 2.68
0.065
84.89 3.03
0.101
86.71 3.16
0.144
82.99 2.95
0.104
V
73.81 2.67
0.066
84.64 2.61
0.088
86.42 3.21
0.143
81.62 2.83
0.098
V
/
75.58 2.49
0.061
86.89 2.81
0.095
85.55 2.94
0.134
82.67 2.75
0.097
70.79 2.42
0.059
83.58 2.37
0.079
82.54 2.62
0.119
78.97 2.47
0.086
Table 5: Detailed Ablation study on component designs represented as A-D. Left side shows which components are enabled (V). Best results in bold.
A.3.2 DETAILED ABLATIONS ON COMPONENT DESIGNS
We conduct detailed ablation verify the effects each component design numerically. Table presents the ablation results with four components: (A) initial condition interpolation, (B) semantic- aligned condition interpolation, (C) SLAT-guided structure interpolation, and (D) fine-grained tex- ture fusion. It can be seen that the semantic-aligned condition interpolation contributes lot to reducing the FID score, which means that it helps to improve the consistency of intermediate states with the source and target. Besides, the employment of fine-gn aine-grained texture fusion produces lower LPIPS, reflecting its ability to refine local appearance details and enhance perceptual similarity.
A.3.3 GAIN FROM THE BETA DISTRIBUTION.
As analyzed in AID (He et al., 2024) for 2D, linear interpolation with uniformly distributed interpo- lation coefficient a; doesn't 't yield uniformly spaced distribution with smooth transitions, which is also revealed during our experiments in 3D generation. Thus, we apply the concave Beta(5, 5) dis- tribution with more shrinkage the midpoint of the start and the end. As shown in the figure below, Beta sampling yields more smoothly transformed intermediate shapes than uniform sampling.
A.4 INTERP3DDATA
Source and Collection. Our dataset, Interp3DData, is built from Objerverse-Xi XI. dataset (Deitke al., 2023), TRELLIS repositoress and the Sketchfab platform". We carefully filtered assets based
'nttps://githon http/microoofooo /TREELIS "https://sketchf"
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R
0.0 a 0.347
0.431
0.500 R= 0.569
0.653
1.0
Beta (5,5) Distribution
Uniform Distribution
0.0
0.167 0.333 0.500 0.833
Figure 7: Visualized comparison between beta and uniform distribution for az..
on quality, completeness, and license compliance for research usage. Each pair is selected repre- sent a meaningful semantic morphing scenario (c.g.. two characters, two vehicles, two architectural forms), avoiding trivial rescalings or duplicates.
Difficulty Levels. To better capture the challenges in 3D morphing, we divide the dataset into three difficulty levels:
Eass: Source and target have similar geometry and topology, or simple texture and appearance.
Medium: Moderate geometric discrepancy or richer texture differences.
Hard: Large variations or rich texture appearances, where both geometry and fine-grained textures differ drastically.
Each level contains 19 souree-target pairs, for total of 57 pairs.
As shown in Figure below, present range of visualizations from collected Interp3IDData to enhance understanding of the dataset and the distinctions among its different levels.
Easy Cases
Target
Target
Mid Cases
Source
Turget
Source
Target
Source
Target
Targel
Hard Caases
Target
Target
Target
Figure Examples of difficulty levels in Interp3DData.
A.5 MORE QUALITATIVE RESULTS.
Figure 10 presents more qualitative comparisons among Interp3D with previous approaches, where our method produces smoother and more coherent transitions, better preserving both structural fi- delity and appearance details. As presented in Figure 11 and Figure 12, our method produces smooth and coherent transitions across a wide range of effectiveness of our progressive alignment design in maintaining both structural fidelity and appear- ance consistency. Notably, the generated intermediates preserve fine details while avoiding abrupt artifacts, demonstrating the robustness of approach under diverse scenarios.
pairs. These results further validate the
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A.6 APPLICATIONS AND FUTURE ANALYSIS
Content Creation and Design. Interp3D can be directly applied to digital content production, where artists and designers often require smooth transformations between objects. For example, in animation and gaming, our framework can generate intermediate 3D assets between two key designs, reducing the manual effort of modeling transitional frames. In film production and advertising, morphing between different product shapes or styles allows creative visual effects without building separate 3D assets from scratch. Similarly, in AR/VR applications, smooth transitions between avatars virtual objects can enhance immersion and user experience.
Industrial and Interactive Applications. Beyond creative fields, Interp3D supports practical in- dustrial needs. In product prototyping, it allows designers to explore continuous shape and texture variations between initial concepts and final products, providing stakeholders with clear visual- ization of design evolution. In c-commerce and virtual try-on systems, morphing between different product configurations (c.g., furmiture colors, car models, or clothing textures) enables interactive previews for customers. In human-computt interaction, semantically aligned morphing serve as an intuitive interface for adjusting 3D content along interpretable trajectories, such as gradually modifying size, style, appearance.
A.7 FAILURE CASES ANALYSIS.
We do observe failures in extreme settings. As shown in Failure Case of Figure 9, the source and target are semantically and structurally too far apart. In such cases, no reliable correspondence can be established, and the morph tends to collapse toward one endpoint, resulting in abrupt transitions and sudden changes rather than a smooth trajectory.
In Failure Case 2,
we show an -ittr--distribution example (e.g., sketched, flat 2D-like objects), where TRELLIS itself fails to reconstruct meaningful 3D geometry. Here, the morphing process degrades into collapsed geometry heavy blur, since the 3D prior cannot provide a stable latent manifold to interpolate on.
Failure Case I: Far apart semantic and structure
Sudden Change
Target
Failure Case Generation bottleneck for OOD data
Source
Generation
Target
Figure 9: Failure case visualizations.
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Published as a conference paper at ICLR 2026
MorphFlow
DiflMorher
FreeMorph
AID-I
AID-O
Interp3D (Ours)
MorphFlow
DiffMorher
Free
AID-I
AID-O
Interp3D (Ours)
Figure 10: Qualitative comparisons of Interp3D with previous baselines.
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Figure 11: More qualitative results of Interp3D, which achieves fidelity and smooth transitions.
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Published as a conference paper at ICLR 2026
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Figure More qualitative results of Interp3D, which achieves fidelity and smooth transitions.
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Reviews:
[{"review_id": "KMq8qOVjAS", "reviewer": "Reviewer_BzE3", "summary": "This paper tackles textured 3D morphing, aiming for smooth and plausible transitions between 3D assets that preserve both structure and appearance. The authors argue existing methods fail by either ignoring texture or by extending 2D strategies, which causes semantic and structural errors.\n\nThe paper proposes Interp3D, a training-free framework built on a \"progressive alignment principle.\" This 3-stage process involves:\n\n1. Semantic-Aligned Condition Interpolation: Matches semantic patches using DINOv2 features.\n\n2. SLAT-Guided Structure Interpolation: Uses structured latents (SLAT) from a 3D model (TRELLIS) to guide geometric correspondence.\n\n4. Fine-Grained Texture Fusion: Aggregates texture features to preserve detail.\n\nA new dataset, Interp3DData, was built for evaluation. Results show Interp3D outperforms baselines in fidelity, smoothness, and plausibility.", "questions": "1. How generalizable is Interp3D? How dependent is it on the TRELLIS SLAT representation?\n\n2. The failure case (Fig. 10) points to the DINOv2 matching as a bottleneck. Could this be improved with different 2D features (e.g., CLIP) or a 3D-native correspondence search?\n\n3. Regarding Sec 4.2: What is the specific value for the threshold $\\tau_0$ and the schedule used for decreasing the patch size $s_t$?\n\n4. What is the performance gain from using the Beta(5, 5) distribution for sampling compared to standard linear interpolation?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper clearly identifies a specific technical problem (artifacts in 3D morphing) and proposes a logical, multi-stage framework that effectively solves it.\n\n2. The training-free nature is a significant practical advantage, showing how to guide a generative prior using feature-space manipulation.\n\n3. The evaluation is comprehensive, using quantitative metrics (FID, PPL, LPIPS) and a user study (Table 2) to prove its superiority over baselines.\n\n4. The creation of Interp3DData is a useful, albeit minor, contribution to this specific sub-field.", "weaknesses": "1. The primary weakness is the limited scope and perceived significance of the task itself. 3D morphing is a relatively niche problem. The proposed solution, while effective, feels more like a clever engineering trick or application built on top of TRELLIS, rather than a novel, generalizable research contribution. The work is highly incremental.\n\n2. The method seems tightly coupled to the TRELLIS model's SLAT representation. It's unclear if this principle generalizes to other 3D models.\n\n3. The paper admits failure when semantic gaps are huge (Fig. 10), suggesting the initial DINOv2 patch matching is a bottleneck.\n\n4. The \"dynamic patch correspondence\" (Sec 4.2) is vague. The paper lacks sensitivity analysis for the patch size $s_t$ schedule and the similarity threshold $\\tau_0$.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TkvkvvwK9B", "reviewer": "Reviewer_qzgJ", "summary": "Interp3D addresses this by coupling generative priors with reliable 3D correspondences through a progressive alignment principle.\n\nSLAT-Guided Structure Interpolation: Enforces geometric consistency by maintaining structural correspondences using SLAT features from a 3D foundation model.\n\nFine-Grained Texture Fusion: Transfers appearance details by retrieving and fusing source and target features at corresponding locations, ensuring coherent and realistic surface appearance.", "questions": "Will the Interp3DData data be open-sourced?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper presents a clear and detailed description of the Interp3D framework, including the three stages of alignment and the specific techniques used at each stage. The pseudocode provided in the appendix further enhances the clarity of the implementation.\n\nThe creation of the Interp3DData dataset provides a valuable resource for the research community.\n\n\nThe method is shown to be robust across a wide range of source–target pairs, including those with geometric and textural differences.", "weaknesses": "Current metrics (FID, PPL, LPIPS) focus on visual quality but lack assessment of semantic coherence and structural fidelity. \n\nThe method heavily relies on attention interpolation of an existing method Trellis3D, which lacks novelty. It's rather than revealing the findings of  Trellis3D.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4lSAaRAtzL", "reviewer": "Reviewer_69AW", "summary": "The paper proposes Interp3D, a training-free framework for textured 3D morphing that leverages the generative prior of TRELLIS. The method enforces correspondence progressively at three levels: (1) semantic-aligned condition interpolation in 2D token space, (2) SLAT-guided structure interpolation during 3D structure generation with fused attention and dynamic patch matching, and (3) fine-grained texture fusion for appearance transfer. The authors also curate Interp3DData and evaluate with FID, PPL, LPIPS plus a user study, showing consistent improvements over previous methods.", "questions": "Failure cases: Do you observe failures when the source and target are semantically or structurally very far apart, or when TRELLIS reconstruction itself fails? Please include concrete examples, diagnostics, and whether the morph collapses or exhibits flickering/blur.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The method cleanly exploits TRELLIS to realize training-free 3D morphing, turning a strong generative prior into a controllable morph pipeline.\n\n2. A dedicated benchmark (Interp3DData) and quantitative comparisons demonstrate that Interp3D surpasses prior SOTA on multiple metrics.\n\n3. The paper is clearly structured. The presentation is clear and easy to follow.\n\n4. Results are visually compelling—fewer geometric failures and significantly less texture blur than baselines.\n\n5. User study included: Human preference indicates better fidelity, smoothness, and overall plausibility for Interp3D.\n\n6. The paper computes alignment between condition tokens and generation-time tokens and then performs correspondence-aware attention interpolation, which is principled and well motivated.", "weaknesses": "Actually, I do not think this paper has an obvious weakness. My concern is about the manual hyperparameter sensitivity: The pipeline exposes numerous hand-tuned knobs (e.g., grid patch size and step-wise schedules in SLAT-Guided Structure Interpolation). It is unclear how robust these settings are across diverse source–target pairs or whether per-pair tuning is required.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vx9ahCKChj", "reviewer": "Reviewer_vr3g", "summary": "This paper proposes Interp3D, a novel and training-free framework for generative textured 3D morphing. The key insight is to address the critical challenge of 3D correspondence misalignments, a root cause of semantic ambiguity, structural distortion, and texture blurring in prior works. To this end, the authors introduce a progressive alignment framework that performs correspondence-aware interpolation across three levels: semantic (in condition space), structural (guided by SLAT features), and texture (via fine-grained fusion). Experiments on a newly constructed benchmark, Interp3DData, demonstrate that Interp3D outperforms existing baselines in terms of structural fidelity, transition smoothness, and visual plausibility.", "questions": "1. In the semantic-aligned condition interpolation, the target condition tokens are permuted to match the source's semantic layout. While this benefits intermediate frames, could this permutation inadvertently harm the fidelity of the final generated target (when α=1)? At this extreme, one would expect an unaltered target output, but the permuted condition might distort it. Could the authors discuss or provide analysis on this potential issue and whether the alignment strategy is adjusted near the endpoints?\n2. Given the potential for conflicting guidance between the different alignment modules (e.g., semantic vs. structural), could the authors provide further ablation studies that not only add modules progressively but also examine the performance of individual modules and other combinations? This would more directly validate the synergy of the proposed pipeline.\n\nI would like to raise my score if above concerns are properly solved.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Practical training-free formulation. The work presents a training-free method that effectively leverages pre-trained generative priors. This practical approach demonstrates strong performance, achieving superior results over existing baselines without the need for costly fine-tuning.\n2. Contribution to the field via benchmarking. A key strength is the introduction of the Interp3DData benchmark. By categorizing the 3D morphing problem into distinct difficulty levels, the paper provides a valuable and standardized framework for evaluation, which facilitates future comparisons and advances in the field.\n3. Clear motivation and well-structured methodology. The paper is clearly structured, with a well-articulated motivation that pinpoints the problem of 3D correspondence misalignments. The proposed three-stage progressive alignment framework is a logical and direct response to these identified challenges, making the technical contributions easy to follow.", "weaknesses": "1. Limited generality and model dependency. The entire proposed pipeline is intrinsically tied to the specific architecture and latent representations of the Trellis model. While this is a valid choice for a proof-of-concept, it raises questions about the generalizability of the method. The approach's strong dependency on Trellis-specific components (e.g., the SLAT representation) makes it non-trivial to adapt Interp3D to other 3D generative frameworks, thereby limiting its immediate applicability and scope.\n2. Insufficient analysis of module interactions. The paper introduces three progressive alignment components but does not thoroughly investigate the interplay between them. For instance, it remains unclear whether the semantic alignment in the condition space and the subsequent SLAT-guided structure alignment always work in harmony or could potentially introduce conflicting signals in some cases. A more detailed ablation study, perhaps with counterexamples, is needed to demonstrate that this progressive strategy is consistently synergistic and does not lead to contradictory guidance during the generation process.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)