FullPart: Generating each 3D Part at Full Resolution
Authors: Lihe Ding
Shaocong Dong
Yaokun Li
Chenjian Gao
Xiao Chen
Rui Han
Yihao Kuang
Hong Zhang
Bo Huang
Zhanpeng Huang
Zibin Wang
Dan Xu
Tianfan Xue
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Generation
Diffusion Model
Part Generation
Primary area: generative models
TL;DR: A novel 3D part generation framework.

Abstract: Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares a global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality.
In this paper, we propose \textit{FullPart}, a novel framework that combines both implicit and explicit paradigms.
It first derives the bounding box layout through an implicit box vector-set diffusion process, a task that implicit diffusion handles effectively since box tokens contain little geometric detail.
Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method—even small ones—is generated at full resolution, enabling the synthesis of intricate details.
We further introduce a center-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintaining global coherence. Moreover, to tackle the scarcity of reliable part data, we present \textit{PartVerse-XL}, the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-art results in 3D part generation. 
Code, model, and dataset are available at https://fullpart3d.github.io.

Published as a conference paper at ICLR 2026
FULLPART: GENERATING EACH 3D PART AT FULL RESOLUTION
Lihe Ding Shaocong Dong², Yaokun Li', Chenjian Gao Xiao Chen' Rui Han Yihao Kuang" Hong Zhang'. Bo Huang'. Zhanpeng Huang", Zibin Wang Dan Xu Tianfan Xue CUHK MMLab; "HKUST; "SenseTime Research; *Chongging University; SCII under InnoHK (d1023, tfxue) euhk. edu. (sdongae, danxu]@cae ust.hk
ABSTRACT
Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality. In this pa- per, propose FullPart, novel framework that combines both implicit and explicit paradigms. It first derives the bounding box layout through an implicit box vector-set diffusion process, task that implicit diffusion handles effectively since box tokens contain little geometric detail. Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method-even small ones-is generated full resolution, enabling the synthesis of intricate details. We further intro- duce a cunter-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintain- ing global coherence. Moreover, to tackle the scarcity of reliable part data, we present PartVerse-XL the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-ard results 3D part generation. Code, model, and dataset are available at
Figure 1: FullPart achieves high-quality part-based 3D generation.
"Equal contribution 1 'Coresponning authors

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INTRODUCTION
Part-based 3D generation and manipulation have wide applications in virtual reality, gaming, robotics, and digital content creation. While recent neural 3D) generation methods have demon- strated impressive results in 3D object synthesis (Zhang et al., 2024 Xiang et al.] 2025 Li et al.) 2025| Zhao et al., 2025), the majority of methods did not provide detailed part decomposition, which essential for downstream tasks such as texture mapping, animation, physical simulation, or fine- grained editing processes. Ideally, a more useful 3D generation framework shall also capture the compositional nature of real-world objects. It shall decompose the generated object into semanti- cally meaningful parts, and users can manipulate each part independently while global coherence can be maintained automatically.
To achieve this, researchers recently proposed part-level synthesis methods, primarily focusing on two paradigms. One paradigm employs implicit latent part-based representations (Lin et al., 2025 Chen et al. 2025b Tang et al., 2025). where each part corresponds to an independent set of latent tokens jointly generated by a shared model. This approach benefits from end-to-end training that simultaneously learns both part geometry and spatial layout, simplifying the training pipeline. How- ever, this implicit representation i) suffers from insufficient part details due to the limited query res- olution when decoding part vecsets and ii) cannot precisely model spatial mappings, making it sub- optimal for texture generation, multi-modal applications, or precise 3D editing. Another paradigm by Yang et al. (2025b) explicitly defines part layer using bounding boxes, and generates detailed voxel structures within each box. While this approach is good for layout modeling, it is challenging to fine detail generation and maintain the global structural coherence, particularly when handling complex or intricately connected components. Besides, all existing approaches share a critical lim- itation: both solutions force all parts to share single global representation space, which limits the resolution allocated to each part and results in poor details of small but complex 3D parts.
In this work, we present two key insights: i) while implicit representations struggle with fine part details, they are well-suited for generating layouts that contain only bounding box information with- out geometric detail; and ii) explicit representations should allocate an isolated full-resolution space to each part; otherwise, small parts may occupy only a few voxels, resulting in degraded quality.
Based on the these observations, we present FullPart, novel 3D part generation framework that first derives layouts (bounding boxes) from implicit vecset generation and then generates each part at full resolution with explicit representation. In this way, we combine both implicit and explicit paradigms while addressing their respective limitations. Specifically, FullPart follows three-stage generation process: (1) layout generation by representing bounding boxes with latent vecsets; (2) dividing each box into an isolated 3 grid and generating coarse 3D part structure with full resolution; and (3) refinement of textured meshes based on the coarse structural foundation.
One remaining challenge of this design is to maintain part coherence when assigning each part to an isolated full-resolution grid. Since each part is generated in its own fixed grid space (641 in our setup), there is a resolution mismatch problem for tokens the boundary of two neighboring parts with diffcrent sizes: tokens (voxels) from the larger part contain fewer details and tokens from the smaller part contain more finer details. This may result in artifacts in overlapping regions when stitching two parts together. Moreover, since tokens from different parts correspond to voxels of varying spatial sizes within the global coordinate system, directly applying attention mechanisms to exchange information between part tokens would result in scale misalignment. To address these issues, we propose a specialized center-corner encoding mechanism. For each part, we calculate the positional embeddings of all eight corners and inject them as well as the centers into all tokens in this part. In this way, each token is aware of its actual spatial extent in the voxel grid, and based on which, diffusion will learn to stitch different parts smoothly. Additional analysis also shows this also makes finetuning easier, which we will further discuss in Section 3.3 With this design, the generated 3D parts can be smoothly stitched together, shown in Figure
Furthermore, to support high-quality part generation, we introduce PartVerse-XL, the largest and most comprehensively annotated 3D part dataset, consisting 40K objects and 320K parts, with as- sociated part-aware texture descriptions. The reason we built this new dataset is that existing 3D datasets either have no 3D part labeling or very few objects have part labels, or the label quality is not high enough. Some 3D model metadata contains part information from artists' modeling pro- cesses, these annotations are often incomplete and lack semantic consistency (e.g., some artists may
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treat object skin as a separate part). Therefore, selected 40K objects from Objaverse-XI -L (Deitke jet al. 2023), and create a high-quality part annotation, using mesh pre-segmentation followed by human refinement. We will release this dataset to benefit future research in part generation.
Through extensive experimentation, we demonstrate that FullPart achieves superior performance in both part-level fidelity and global structural coherence compared to existing approaches, with particular strength in generating plausible geometries for occluded and small parts.
In summary, contributions are: i) We propose FullPPar, a novel part-level 3D generation frame- work that combines implicit layout representation with explicit part structure generation, enabling precise detail control; ii) We enable each part to be generated in an isolated full resolution while maintaining the global part coherence and preventing violation of the foundation model's estab- lished knowledge by proposing a center-corner encoding strategy: iii) We present PartVerse-XL, large-scale numan-annotated part dataset containing 320K high-quality parts with part-aware textual descriptions, addressing the searcity of reliable part-level 3D training data; iv) We demonstrate state- of-the-art performance in part-level 3D generation across multiple metries, with particular strength handling complex part interactions and generating plausible occluded geometries. Code, model, and dataset are available at https: :ttp:://ful part3d. gi t.ub.ii...
2 RELATED WORK
2.1 3D GENERATION
Early 3D generation focused on category-specific or image-to-3l approaches with limited object diversity (Poole et al. 2022 Wang et al. 2023 Lin et al. 2023: Dong et al.) 2024 HHng et al.) 2023 Liu et al. 2023a Shi et al., 2023: Ding et al. 2024). This paradigm shifted with 3D-native diffusion models operating directly in 3D space. CLAY (Zhang al. 2024) established foun- dational transformer-baa
architecture for direct 3D generation. Recent advances (2024-2025) significantly improved fidelity and control: TRELLIS (Xiang al. 2025) introduced sparse voxel- based structured latent representations for precise geometry; TripoSG (Li et al., (025) leveraged SDF representations with rectified flow for speed-quality trade-offs; Direct31) (Wu al. 2025)
enabled high-resolution text-to-3D generation; and Hunyuan3I (Zhao et al. (2025) integrated multi- modal understanding for text-guided manipulation. However, these frameworks generate monolithic shapes without explicit part decomposition, limiting fine-grained editing.
2.2 PART GENERATION
The need for part-aware 3D generation has motivated several research directions with different 3D representations and learning paradigms. Early part-aware approaches typically relied on category- specific annotations and auto-encoder-style architectures. such as SPAGHETTI [Hertz et al. (2022) (implicit representation) and Neural Template Hui et al. (2022) (implicit and explicit), or fo- cused on diffusion-basee based part generation, including SALAD Koo al. (202)) (implicit) and Diff- Facto (Nakayama et al. 2023) (implicit). However, these methods suffer from limited generalizabil- ity across diverse object categories. Subsequent works such as Part123 Liu et al. (2024) and Part- Gen Chen al. (2025a) used SAM Kirillov al. (2023) for multi-view segmentation, yet remained constrained by segmentation quality and limited patch information. Recent contemporaneous works have made significant strides toward -prrpose part generation. CoPart (Dong al.) (025) represented a 3D object with multiple contextual part latents and simultancously generated coherent 3D parts. HoloPart (Yang al. (2025a) segmented objects into parts and then completed the seg- ments into full 3D parts. PartCrafter (Lin et al., 2025) introduced an implicit latent representation approach where each part corresponds to an independent set of latent tokens generated by shared model. AutoPartGen (Chen et al., 2025b) automated part decomposition through a learnable part proposal mechanism. OmniPart (Yang et al., (0255) adopted explicit voxel-based representations with bounding boxes to define part layouts. BANG (Zhang et al. 2025) represented 3D objects as smooth sequence of exploded states and generated parts by generative exploded dynamics.
However, all existing part-aware generation methods share critical limitation: they force parts to share a single global representation space. In implicit representation approaches, this means small parts receive insufficient representation capacity in the shared latent space. In voxel-based meth- ods, this results in small parts occupying only a tiny fraction of a shared NxNxN grid, leading to extremely low effective resolution for those components. This fundamental bottleneck has not been
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TEXT
Layout Gen (Implicit)
Coarse Structure (Explicit / Full Res)
Texture Mesh Gen (Explicit / Full Res)
mMG
(+]
Center-comnrr Encoding
(c)
Intra-Part Attn
Intra- Part Atti
Encocing
VerSet
Inter-Fart Attn
Intra-
Diffusion
Attn
Contee-camer
Attn
Attn
dxcode
Intra -Part
Intra -aart
Feoidligg
Full-res grid partition
DIT Block
DIT Block
FullPart framework. FullPart comprises three sequential stages: (a) layout generation using implicit vecset diffusion, (b) generating each part full-resolution grid with explicit voxel representation, and (c) refining coarse part structures to texture meshes.
Figure 2:
adequately addressed by prior work, which is precisely where our framework makes its key contri- bution by treating each part as full-resolution independent object during the generation process.
2.3
DECOMPOSABLE 3D GENERATION
Decomposable generation is crucial for 3D scene and avatar synthesis. Frankenstein Yan et al. (024) encoded input into a compact triplane and generated semantic-compositione 3D scenes or avatars via triplane diffusion process conditioned on a 2D semantic layout. Layou Feng et al. 2023) predicted layout parameters (e.g., bounding boxes, categories) from an LLM and directly retneved objects. CAST Yao et al. (2025) achieved component-alignes 3D scene generation from single RGB image by VLM scene analysis, pose-aware generation module, and physical constraint refinement. StdGEN He et al. (2025) generated 3D avatar implicit fields through semantic-awan large reconstruction model and further employed multi-layer semantic surface extraction scheme together with a refinement module to obtain the decomposable meshes.
METHODOLOGY
We formally define our part-aware 3D generation problem as follows: given a conditioning input (typically single-view RGB image or text prompt), our goal is to generate structured 3D object 1K {o consisting of K semantically meaningful parts, where each part o; is represented as a
O = textured mesh with explicit geometric and topological properties. Unlike monolithic 3D generation approaches, our framework explicitly models the compositional nature of objects through a hierar- chical generation process that first establishes part layouts (bounding boxes) using implicit vecset diffusion (Li et al. 2025) (Figure 2 (a)), then generates coarse part structures within boxes by rep- resenting each part in an explicit full-resolution voxel grid (Figure (b)), and finally refines coarse voxels into detailed meshes with textures (Figure 2 (c)). In the remainder of this section, we provide a comprehensive description of each component.
3.1 PRELIMINARY: 3D OBJECT GENERATION FRAMEWORKS
Our methodology builds upon two predominant paradigms in 3D generation: implicit latent repre- sentations for layout generation and explicit voxel-based representations for part structure genera- tion. We briefly review these approaches and establish the foundation for our part-aware extension.
Implicit Representations. Following 3DShape2 VecSet (Zhang al. 2023), a 3D object can be represented as vecset-a set of latent tokens T {t,))=1 E RRMXD where M is the number of tokens and t; E IRD is the j-th token with feature dimension D. The decoder transforms these tokens into signed distance field (SDF) representation ò R3 + R. where the zero level set defines the object surface: S = (x E R²(Ø(x) = 0}. This implicit representation enables high- fidelity geometry generation but lacks explicit spatial partitioning for part manipulation.
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Explicit Representations. TRELLIS (Xiang et al. 2025) introduces structured latent representa- tion for 3D objects through sparse voxels. Given a 3D asset, encodes geometric information into set of active featured voxels C:
C (c/c; = (f,, (f) GERD, Pa E 10, NN N
where P, denotes the positional index of active voxel in the N x N x N grid, and f, represents the feature vector capturing local geometry and appearance. The active voxels pa define the coarse structure, while f, encodes fine details.
Attention Mechanisms for Part Generation. Building upon the previous works like CoPart (Dong al. 2025), we introduce two attention mechanisms critical for part-aware generation:
Intra-Part Attention: For part k, given its token set Ts E RMxxD, intra-part attention limits the self-attention computation the tokens of part k:
Q = WTTN: K2 = WATA: V& = W.T,
(2)
where Q K3 V are query, key, and value projections of Tg.
Inter-Part Attention: Given all part tokens T = [ Tx] E RKMxD, inter-part attention computes self-attention between all tokens:
Q W,T, K= WAT: =
(3)
where Q, K, V are projections of the concatenated token set T.
These attention mechanisms enable our framework to balance intra-part detail generation with inter- part structural coherence. The flexibility of transformer architecture allows seamless adaptation of full-object generation models to part-level synthesis while preserving pre-trained model priors.
3.2 LAYOUT GENERATION
Our layout generation module produces set of bounding boxes that define the spatial arrangement of object parts. Rather than treating boxes as abstract parameters, we represent each box by as minimal triangular mesh (a cuboid with 8 vertices and 12 faces), where the collection of these meshes forms a coarse "blocky" representation the object, reminiscent of Minecraft-style models. By representing bounding boxes meshes, we obtain semantic representation aligned with the latent space of the vecset diffusion model, thereby leveraging its strong prior for effective layout generation.
Formally, given a conditioning image or text prompt, we generate a set of K' bounding box meshes k= where b = (Vk, fz.) is the k-th box mesh with cuboid vertices V E g8x3 and face
B = [bx K
indices f E N12x3
To encode these box meshes, we utilize the VAE from TripoSG (Li et al. 2025 which maps each box MM latent tokens: Tç VAE E MxD. 1= K"
To make the network distinguish tokens from different boxes, we inject box ID embeddings en (k) e D to the corresponding token t E IRD (tokens from box k): t tt + (k).
Additionally, leverage the powerful priors of the foundation model, we retain global branch in the original vecset diffusion model, which is tasked with predicting the holistic object structure. This global branch provides essential semantic guidance for the layout generation process. To enable the network to distinguish its tokens from part-specific box tokens, we assign an identifier of 0 to the tokens To E R MMMX X in the global branch.
During training, for objects with K < K' actual boxes, we pad the token sequences with zero vectors for the remaining K' K boxes. The complete token sequence that represents the box layout for the DIT input is: Tan from the global branch. Our DiT employs a hybrid attention strategy: intra-part attention operates within each box's 's token set (T,, 3 for & = 0, 1....,
(To, Ti...., Tx: E (K +1)MXD, where To is the tokens
".....,'), K'), while inter-part attention operates across all box tokens. This enables the model to learn both local-specific characteristies and global structural relationships.
At inference time, sample the latent tokens Tai using diffusion process, then decode them back to box meshes via the VAE decoder: B {VAEaee ( K- Finally, due potential deformation
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vovels shared global grid Figure 3: Illustration of our 3D part representation. Our model generates each part at isolated full resolution (c), which contains more fine details than the previous sharing global voxel grid strategy (b). Also, tokens from different parts represent varying spatial extents, e.g., head and body in (d).
[a] loyout
full-res vout
zoom
and incorrect shapes with the decoded meshes B, we recalculate their own bounding boxes B', and retain those with high IoU between corresponding B and B' as layout inputs for subsequent stages.
3.3 3D COARSE STRUCTURE GENERATION
Given the bounding boxes from the layout generation, our coarse structure generation stage creates detailed part geometries within the layout-defined regions. Instead of sharing global voxel grid (Figure 3 (b)), we generate each part within its own dedicated N x N N voxel space at full resolution, enabling precise detail generation regardless of part size, as shown in F
To generate each part at a full resolution, we normalize it to the canonical space [-1, 1]3 and define a binary occupancy grid: V E {0, 13,",, where V& [ar, y, =] = 1 if occupied, and i denotes the k-th part. This normalization ensures that even small parts utilize the full resolution of the voxel grid, overcoming the resolution limitations of shared-grid approaches. However, when each part is defined in its own full-resolution grid, the tokens from different bounding box regions represent varying spatial extents. This discrepancy becomes problematic when exchanging information be- tween parts. For example, a token from a big part (c.g., upper body in Figure (d)) may represent an absolute voxel size several times larger than a token from a small part (c.g., head in Figure (d)) leading to token misalignment.
To address this, we introduce center-corner encoding mechanism that embeds the absolute spatial context of each voxel. For a voxel at position u = (I, y. 2) in the normalized grid of part k we compute its 8 corresponding corners in the global object space: {uğ uy T(u', bx) where T(,bk) is the transformation to global coordinate using box by and uy the i-th corner of in global coordinate. Following the foundation model that encodes integer positions in the global coordinate, we partition global space into a super-high resolution grid (2048 x 2048 x 2048) and find the integer coordinates of all eight corners [[u']] Jimn and the center [ug]. At this stage, we have obtained the center and eight corner coordinates for each voxel of every part. Although different voxels may represent different spatial extents, their corner and center positions now expressed within a unified positions using the pre-trained positional embedding layer directly.
global coordinate system. This allows us to encode these
One remaining consideration is that our model is based on a pre-trained 3D generator, whose po- sitional encoding layers were only trained under low resolution of 64 64 64. Fortunately, previous research has shown that positional encoding can be effectively extrapolated during fine- tuning (Liu et al. 2023b). Therefore, we simply inject the positional embeddings of one center and eight corners for each token: th = Cpoc ([,))) [)= + E Spox (Ln,)) + Ga(k), where Cpes() is the positional embedding layer and ca(() is an additional embedding layer to inject part ID informa- tion. This center-corner embedding is added to each voxel token, providing explicit spatial context that enables the model to understand part relationships despite the normalization. Furthermore, our strategy requires no modification to the pre-trained model's 's architecture, thus it can better utilize its foundational priors. Similar to the layout stage, we include a global branch (k = 0) that processes the entire object structure. The complete token sequence is: T.n To. T ..... Tx:].
Our DiT architecture employs the same hybrid attention mechanism as in the layout stage, allowing it capture both fine ine-gnained part details and global structural coherence. The diffusion process generates the voxel tokens conditioned on the input image or text prompt, which is incorporated through cross aoss-ntttntion layers following TRELLIS's conditioning strategy.

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3.4 REFINEMENT
The refinement stage enhances the coarse voxel structures with detailed geometry and textures, adapting TRELLIS's s second stage (Xiang et al. 2025) for part-aware generation. For each part k, obtain feature vectors F E RL4xDT for the occupied voxels Pk via TRELLIS's s VAE encoder, where Dy is the feature dimension.
The VAE encoder embeds the tokens with both structure and projected multi-view features (from Dino-v2 by Oquab et al. (023)). ). A key challenge is handling occlusions between parts. To address this, we normalize and render each part independently. obtaining tokens with part-specific image features, as the denoising target during training. However, during inference, our model only requires a single global image or text as a conditional input, making practical for real-world applications.
The diffusion model generates the feature vectors F& on all occupied voxels Ps: at full resolu- tion. These features are then decoded into textured meshes using TRELLIS's decoder: O Decoder(F,, k. Ps). The final object is assembled from the individual part meshes: Ô U Ö₁.
3.5
OPTIMIZATION Loss
All stages of our framework are trained using Conditional Flow Matching (CFM) Lipman et al. 2023 objective, i.e., for a given stage with token representations x, we have the training objective:
Limm ( Ex,,e, [[lvo(x, t) (c - -c--x))l?)
where eg is the diffusion noise, and Vy is the predicting vector field.
4 PARTVERSE-XL DATASET
To support large-scale part-aware 3D generation, we introduce PartVerse-XL, an expanded and re- fined extension of PartVerse (Dong et al. 2025). It contains 40K high-quality 3D objects from Objaverse-XL XLL (Deitke et al., 2023), yielding 320K semantically consistent, textured parts across over 200 categories, each paired with descriptive caption. This scale and diversity significantly surpass prior benchmarks and enable robust training of models requiring fine-grad fine-grained part semantics and geometry.
We construct PartVerse-XX XL. via two-stage pipeline. First, we apply an automated pre-segmentation algorithm that fuses geometric priors (e.g., mesh connectivity, UV seams) with semantic cues from SAM-2 (Kirillov et al., (023) and Samesh (Tang et 2024), deliberately producing over-segmented outputs for easier human correction. Second, expert annotators refine the segments using a Blender- based mol-merging or splitting components to ensure semantic clarity, structural symmetry, and texture preservation while discarding low-quality or ambiguous assets.
For each part, we generate textual caption by rendering multi-view images of both the full object and the isolated part. We select the view with maximal part-whole visibility overlap, overlay a bounding box around the part, and feed the composite image a vision-language model. Captions are generated to accurately describe shape, appearance, material, and part-object relationships (e.g., "a cylindrical metallic handle attached to the right side of a coffee mug").
Par establishes a new standard in scale, semantic fidelity, and multimodal alignment for part-level 3D generation. More details of our dataset can be found in the supplementary material.
5 EXPERIMENTS
This section presents our experimental results. All experiments of our model utilize the Verte XL training set (40K objects, 320K parts), and we also construct a dedicated test set of 100 objects. More details, results, and analysis can be found in the supplementary materials.
5.1 IMPLEMENTATION DETAILS
We train FullPart three sequential stages on 8 NVIDIA A100 GPUs. The layout generator (Stage 1) is trained for 96 hours using Adam (8) 0.9, B2 0.999) with batch size 64. Stages 2 (coarse voxel generation) and (mesh refinement) each train for 144 hours with batch size 8, leveraging pre-trained TRELLIS Xiang al. (2025) weights. The maximum part count is clamped to Kmax = 30, and all parts are generated in isolated full resolution 64 grids. During inference, apply non-maximum suppression (NMS) with IoU threshold 0.7 to eliminate redundant boxes. Besides, we take 100 manually selected untrained objects as the test set. More details can be found in the supplementary material.
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Input Image
Ours
OmniPart
PartCrafter
Figure 4: Comparison with state-of-the-an 3D Part generators. Our method can generate more detailed and reasonably divided parts.
An old fashisned phanograph.
robot in green ancient bronge artifocts style.
Input Image
Ours-Image
TRELLIS- 15-amage (baselina)
Direct3D-82
Oure-Text
TRELLIS- Text (baselime)
Figure 5: Comparison with the state-of-the-art 3D generators.
5.2 RESULTS
5.2.
COMPARISON WITH STATE-OF-THE-ART PART GENERATORS
We compare FullPart against two leading part-aware generators: PartCrafter Lin et al. (2025), and Omnipart Yang al. (2025b). Figure demonstrates that FullPart achieves superior geometric fidelity and structural coherence, particularly for intricate assemblies (e.g.. articulated robot arms) and occluded regions (e.g., chair undersides). While PartCrafter produces fragmented parts due implicit token entanglement, and Omnipart suffers from voxelization artifacts in small components (c.g., thin chair legs), FullPart preserves fine details through dedicated per-part full-resolution grids.
5.2.2 COMPARISON WITH FULL-OBJECT METHOODS
We compare FullPart against its foundational model TRELLIS Xiang et al. (2025) and the state-of- the-art monolithic 3D generator Direct3D-S2 Wu et al. (0225). As these full-object approaches lack part decomposition capability, they inherently suffer from global grid sparsity, leading to significant detail loss in fine-graid fine-grained regions (e.g., robotic head features in Figure 5). In contrast, FullPart's part-aware architecture preserves high-fidelity details through localized high-resolution generation.
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Table Quantitative comparison on PartVerse- XL. test set ("-" denotes not applicable).
Method
F-Score
CD
Part-CD +
ULIP-Score
TRELLIS Xiang et (2025)
0.71
0.16
0.21
HoloPart Lin al. (2025)
0.68
0.21
0.15
PartCrafter Chen ct al. (2025b)
0.63
0.42
0.13
Omnipart Yang et al. 20256)
0.77
0.15
0.42
0.22
FullPart (Ours)
0.81
0.11
0.36
0.24
(a)
(b)
(c)
(d)
Input w/o Corner Enc w/o Human Anno w/o Per-part Grid
FullPart Baseline
Figure 6: Comparison with different model settings under identical training budgets: (a) encoding, (b) using metadata-derivel structural information without manual annotations, (c) all lay- out boxes constrained single voxel space, causing each box may only occupy small number of voxels, and (d) normal setting with all things.
5.2.3 QUANTITATIVE EVALUATION
Table 1 reports metrics the 100-object test We evaluate: (i) Global fidelity with threshold of 0.1 (F-Score), (ii) Global mesh chamfer distance (CD), (iii) Part mesh chamfer distance when use same layout boxes (Part-CD), and (iv) 3D Semantic alignment (ULIP Score Xue et al.) (2023). FullPart outperforms all baselines in part-level and full-level metrics, proving its strength in part- level detail and global coherence. Note that Part-CD is not applicable to the first three methods due to their lack of bounding box-cnnditiened part generation capability.
5.3 ABLATION STUDIES
Center-corner Encoding Ablation. To validate our center-comer encoding strategy, we ablate the generator by replacing center corner coordinates with only center coordinates. Figure (a) shows that this variant fails to model part interactions (e.g., misaligned chair legs). This confirms that explicit location and scale information critical for capturing spatial relationships between parts.
Impact of Human-post Annotations. We train FullPart on two data variants: (i) raw metadata (artist-provieed part labels), and (ii) PartVerse-XL with human-refined annotations. As Figure (b) (d) illustrates, metadata-only training produces semantically incorrect parts due to the noise in part labels, while training on human-ane numan-annotated data yields coherent, functionally meaningful parts.
Per-Part Full- Resoluonon Grid Ablation. We compare our dedicated N (N = 64 in our setting) per-part grids against global grid baseline where all parts share the global N space. Figure 6 reveals severe detail degradation in small parts under the global grid, as they occupy only small number of voxels. Our method maintains consistent resolution across all parts, achieving uniform detail generation regardless of relative size.
5.4 APPLICATION
The FullPPart framework exhibits strong flexibil- ity, enabling a range of controllable 3D editing applications through manipulation of the lay- out boxes. Specifically, users can intuitively edit part-level geometry by adding, deleting, or modifying the shape and position of bounding boxes each operation directly influences the corresponding generated part while preserving the integrity of the rest the object. As example where two accessories added to rifle and
Part Stretch Boxcs
Adding Donca
Figure 7: Editing applications.
lustrated in Figure 7 we demonstrate the barrel is elongated. After the initial generation, we modify the layout boxes by inserting new boxes and stretching existing ones, then re-run inference. For unchanged parts, bypass the dif- fusion sampling process by directly injecting their clean latent tokens (along with the appropri- ate noise level corresponding to the current inference timestep) into the DiT fixed inputs. This

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strategy ensures consistent regeneration of unedited components while allowing efficient, localized updates highlighting FullPart's suitability for interactive, part-aware 3D content creation.
6 CONCLUSION
We introduced FullPart, a part-aware 3D generation framework that integrates implicit and explicit representations. By treating each part as a full-resolution object, our method overcomes the resolu- tion bottleneck of shared voxel grids. We also introduce Verseers large-scale annotated part dataset to advance future research in 3D part generation.
ACKNOWLEDGGMEENT
The work is supported in part by the Early Career Scheme ofthe Research Grants Council (RGC) of the Hong Kong SARunder grant No. 26202321, ITF PRP/046/24FX, SAIL ResearchProject, HKUST-Zeekr Collaborative Research Fund, and TUHK-CUHKSZ-C 1+1+1 Joint Collaboration Fund No. 4760964. We also gratefully acknowledge the support of SenseTimm.
REFERENCES
Minghao Chen, Roman Shapovalov, Iro Laina, Tom Monnier, Jianyuan Wang, David Novotny, and Andrea Vedaldi. Partgen: Part-level 3d generation and reconstruction with multi-view diffusion models. In CVPR, 2025a.
Minghao Chen, Jianyuan Wang, Roman Shapovalov, Tom Monnier, Hyunyoung Jung, Dilin Wang, Rakesh Ranjan, Iro Laina, and Andrea Vedaldi. Autopartgen: Autogressive 3d part generation and discovery. arXiv preprint arXiv:2507. 13346, 2025b.
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. In NeulPS, 2023.
Lihe Ding, Shaocong Dong, Zhanpeng Huang. Zibin Wang, Yiyuan Zhang, Kaixiong Gong, Dan Xu, and Tianfan Xue. Text-to-3d generation with bidirectional diffusion using both 2d and 3d priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5115-5124, 2024.
Shaocong Dong, Lihe Ding, Zhanpeng Huang, Zibin Wang, Tianfan Xue, and Dan Xu. Interac- tive3d: Create what you want by interactive 3d generation. In CVPR, 2024.
Shaocong Dong, Lihe Ding, Xiao Chen, Yaokun Li, Yuxin Wang, Yucheng Wang, Qi Wang. Jae- hyeok Kim, Chenjian Gao, Zhanpeng Huang, et al. From one to more: Contextual part latents for 3d generation. In ICCV, 2025.
Weixi Feng, Wanrong Zhu, Tsu-jui Fu, Varun Jampani, Arjun Akula, Xuehai He, Sugato Basu, Xin Eric Wang, and William Yang Wang- Layoutgpt: Compositional visual planning and gen- eration with large language models. Advances in Neural Information Processing Systems, 36: 18225-18250 2023.
Yuze He, Yanning Zhou, Wang Zhao, Zhongkai Wu, Kaiwen Xiao, Wei Yang, Yong-Jin Liu, and Xiao Han. Stdgen: Semantic-decomposel 3d character generation from single images. In Pro- ceedings of the Computer Vision and Pattern Recognition Conference, pp. 26345-26355 26355, 2025.
Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine- Horknngg and Daniel Cohen-Or. Spaghetti: Editing implicit shapes through part aware generation. ACM Transactions Graphics (TOG), 41(4):1- 20, 2022.
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint (td::2/11004400) 2023.
Ka-Hei Hui. Ruihui Li, Jingyu Hu, and Chi- Wing Fu. Neural template: Topology- aware reconstruc- tion and disentangled generation of 3d meshes. In CVPR, 2022.
10

---

Published as a conference paper at ICLR 2026
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander Berg, Wan-Yen Lo, et al. Segment anything. In Proceed- ings of the IEEE/CVF international conference computer vision, pp- 4015-4026, 2023.
Juil Koo, Seungwoo Yoo, Minh Hicu Nguyen, and Minhyuk Sung. Salad: Part-level latent diffusion for 3d shape generation and manipulation. In ICCV, 2023.
Yangguang Li, Zi-Xin Zou, Zexiang Liu, Dehu Wang. Yuan Liang, Zhipeng Yu, Xingchao Liu, Yuan-Chen Guo, Ding Liang, Wanli Ouyang, et al. Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models. arXiv preprint arXi::2502.06608 2025.
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming- Yu Liu, and Tsung-Yi Yi Lin. Magic3d: High-resolution text-to-3d content creation. In CVPR, 2023.
Yuchen Lin, Chenguo Lin, Panwang Pan, Honglei Yan, Yiqiang Feng. Yadong Mu, and Katerina Fragkiadaki. Partcrafter: Structured 3d mesh generation via compositional latent diffusion trans- formers. arXiv preprint Stti::2506005577, 2025.
Yaron Lipman, Ricky TQ Chen, Heli Cen-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. In ICLR, 2023.
Anran Liu, Cheng Lin, Yuan Liu, Xiaoxiao Long, Zhiyang Dou, Hao-Xiang Guo, Ping Luo, and Wenping Wang. Part123: part-aware 3d reconstruction from single-view image. In ACM SIG- GRAPH 2024 Conference Papers, 2024.
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov. and Carl Vondrick. Zero-I-to-3: Zero-shot one image 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 2998-9309, 2023a.
Xiaoran Liu, Hang Yan, Shuo Zhang, Chenxin An, Xipeng Qiu, and Dahua Lin. Scaling laws of rope-based extrapolation. arXiv preprint arXiv:2310.05209 2023b.
Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su. Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object under- standing, 2018,
George Kiyohiro Nakayama, Mikaela Angelina Uy, Jiahui Huang, Shi-Min Hu, Ke Li, and Leonidas Guibas. Difffacto: Controllable past-based 3d point cloud generation with cross diffusion. In CVPR, 2023.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193. 2023.
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209. 14988, 2022.
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308. 16512, 2023.
George Tang, William Zhao, Logan Ford, David Benhaim, and Paul Zhang. Segment any mesh: Zero-shot mesh part segmentation via lifting segment anything to 3d. arXiv e-prints, pp. arXiv- 2408, 2024.
Jiaxiang Tang, Ruijie Lu, Zhaoshuo Li. Zekun Hao, Xuan Li, Fangyin Wei, Shuran Song, Gang Zeng, Ming- Yu Liu, and Tsung- Yi Lin. Efficient part-level 3d object generation via dual volume packing. arXiv preprint arXiv:2506.09980 2025.
Peng Wang. Shuai Bai, Sinan Tan, Shijie Wang. Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge, et al. Qwen2-vl: Enhancing vision language model's 's perception of the world at any resolution. arXiv preprint arXiv:2409. /2/9/, 2024.

---

Published as a conference paper at ICLR 2026
Penghao Wang, Yiyang He, Xin Lv, Yukai Zhou, Lan Xu, Jingyi Yu, and Jiayuan Gu. Partnext: A next-generation dataset for fine-grained and hierarchical 3d part understanding. arXiv preprins arXiv:2510.20155. 2025.
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Pro- lificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. NeulPS, 2023.
Shuang Wu, Youtian Lin, Feihu Zhang. Yifei Zeng, Yikang Yang, Yajie Bao, Jiachen Qian, Siyu Zhu, Xun Cao, Philip Torr, et al. Direct3d-s2: Gigascale 3d generation made easy with spatial sparse attention. arXiv preprint arXiv:2505. 02.55055.7722. 174/2, 2025.
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In CVPR, 2025.
Le Xue, Mingfei Gao, Chen Xing, Roberto Martín-Martin, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, and Silvio Savarese. Ulip: Learning unified representation of language, images, and point clouds for 3d understanding. In CVPR, 2023.
Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen, Weixuan Sun, Taizhang Shang, Weizhe Liu, Tian Chen, Xiaqiang Dai, Chao Ma, et al. Frankenstein: Generating semantic-compostionna 3d scenes in one tri-plane. In SIGGRAPH Asia 2024 Conference Papers, pp. 1-11, 2024.
Yunhan Yang, Yuan-Chen Guo, Yukun Huang, Zi-Xin Zou, Zhipeng Yu, Yangguang Li, Yan- Pei Cao, and Xihui Liu. Holopart: Generative 3d part amodal segmentation. arXiv preprint 00e::2004.79433 2025a.
Yunhan Yang, Yufan Zhou, Yuan-Chen Guo, Zi-Xin Zou, Yukun Huang, Ying-Tian Liu, Hao Xu, Ding Liang, Yan-Pei Cao, and Xihui Liu. Omnipart: Part-aware 3d generation with semantic decoupling and structural cohesion. arXiv preprint arXiv:2507006165, 2025b.
Kaixin Yao. Longwen Zhang, Xinhao Yan, Yan Zeng, Qixuan Zhang, Lan Xu, Wei Yang, Jiayuan Gu, and lingyi Yu. Cast: Component-aligned 3d scene reconstruction from an rgh image. ACM Transactions on Graphics (TOG), 44(4):1-19, 2025.
Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset. A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 42(4) 2023.
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-qualig 3d assets. ACM Transactions Graphics (TOG), 43(4):1-20, 2024.
Longwen Zhang, Qixuan Zhang, Haoran Jiang, Yinuo Bai, Wei Yang, Lan Xu, and Jingyi Yu. Bang: Dividing 3d assets via generative exploded dynamics. ACM Transactions on Graphics (TOG), 44 (4):1-21, 2025.
Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation. arXiv preprint arXiv:2501. 2025.
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APPENDIX
A IMPLEMENTATION DETAILS
A.1 NETWORK ARCHITECTURE
We fine-tune all three- models starting from pre-trained holistic 3D generators. Specifically, the layout generation model is initialized from TripoSG Li et al. (2025), where we replace the original tokens with our proposed box tokens and inject box ID embeddings to adapt the model for layout generation. Similarly, the coarse structure generation model and the mesh refinement model are initialized using Stage and Stage TRELLIS, respectively.
In all three stages, convert half of the DiT blocks to inter-part attention blocks, while the remain- ing blocks retain intra-part attention. To preserve global semantic context and stabilize fine-tuning, maintain a holistic generation branch (assigned part ID=0), which helps prevent significant de- viation from the pre-trained weights. Furthermore, conditional inputs-suce as image or text-are are incorporated following the original pre-trained architecture via additional cross-attention blocks. Notably, in each block, all part tokens attend to all condition embeddings.
A.2 TRAINING AND INFERENCE DETAILS
Owing to GPU memory constraints, we set the maximum number of parts per object to 30 during training. Ror each object sample, we sort its parts by the bounding box sizes and choose the top 30 largest parts during training. Despite this limitation, our framework supports the generation of objects with more than 30 parts through a sequential sampling strategy during inference. The process is as follows: first, we sample an initial set of 30 parts alongside global part that captures the overall shape. Then,
subsequent sampling round, we replace the global tokens with the noisy version of the previous denoised global token at each timestep. maintaining the global structure unchanged, and sample new 30 parts. We follow TRELLIS Xiang et al. (2025) to render conditional images for each object and randomly choose one during training. To improve the quality of conditional generation, we adopt common practices from prior work: condition tokens are randomly dropped with probability of 0 during training, and classifier-free guidance with scale of 3.5 is applied at inference. We also apply a bounding box augmentation strategy in the training the coarse structure generation model to make the model robust imperfect bounding box input.
B PARTVERSE-XI DATASET
To support large-scale, high-fidelity part-aware 3D generation, we present PartVerse-XL, a sig- nificantly expanded and refined version of the earlier PartVerse Dong et al. (2025) dataset. Fig- ure. shows some examples in our dataset. PartVerse-XL, comprise- 40K high-quality 3D objects sourced from Xbjaverse-XI. Deitke et al. (2023), yielding total of 320K semantically consistent and cexture-preserving parts, each accompanied by a detailed part-level textual description. This scale and serssstt-spannnnng over 200 object saregoores--usstattiaae benchmarks such as PartNet Mo et al. (018) and enables robust training of generative models like FullPart that demand both geometric precision and semantic grounding at the part level.
surpasses existing peve-
The construction of PartVerse-XX XL follows two-stage pipeline that combines automated pre- segmentation with rigorous human refinement, ensuring both scalability and annotation quality.
Automated Pre-segmentation with Semantic Priors. We begin by leveraging intrinsic modeling cues present artist-created 3D assets, such as mesh connectivity, UV layout boundaries, and ma- terial assignments. To align these low-level cues with high-level semantics, we integrate 3D-aware segmentation framework built upon SAM-2 Kirillov et al. (2023) and Samesh Tang et al. (2024), en- hanced with geometric priors specific to procedural 3D modeling workflows. This hybrid approach produces an initial over-segmented partition of each object, deliberately erring on the side of finer granularity. Segmennggtnnttoon is preferred because it provides annotators with atomic building blocks that can be reliably merged, whereas under-segmentation often leads to irreversible loss of part boundaries.
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Figure 8: Data examples from Part
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Human-in-the-Loww Refinement. All pre-segmented results undergo careful manual curation ing a custom Blender-base based annotation interface. Annotators first filter out unsuitable assets such as those with excessive topological complexity, non-manifold geometry, or ambiguous part seman- tics. They then refine the segmentation by: (1) merging fragments that belong to the same functional or visual unit (e.g., the back and seat of chair), and (2) splitting regions that conflate distinct com- ponents (e.g., separating armrests from chair legs). The refinement protocol emphasizes semantic clarity, structural symmetry, and compatibility with downstream generation tasks. Critically, tex- tures and material properties are preserved throughout this process, ensuring that each extracted part remains visually coherent and renderable.
Part-Aware Textual Captions. To enable vision-languee conditioning in part-level generation, we generate descriptive captions for every part. For each object-part pair, we render multi-view RGB images of both the full object and the isolated part. We then identify the view that maximizes the visible overlap between the part and its context within the whole object. A composite image is formed by overlaying bounding box around the part in the full-object render. This image is fed to a state-of-the-art vision-langee laigu-la model (Qwen2.5-VL. Wang et al. (2024) in our setting) to produce natural language description that captures the part's ' shape, appearance, material, and functional or spatial relationship to the parent object.
Part XL not only provides order-of-magnituee magnitude increase in scale over prior part datasets but also establishes a new standard for semantic consistency, texture fidelity, and multimodal align- ment ment-making making uniquely suited for training and evaluating next-generation part-aware 3D genera- tive models.
C MORE RESULTS AND ANALYSIS
C.I MEMORY AND EFFICIENCY ANALYSIS
We benchmark the training and inference memory consumption of our model across varying part numbers and present comparison with prior part generators in Table. [ All tests are conducted on the same NVIDIA A100 GPU. Common part numbers are typically below 10. PartCrafter and CoPart incapable of handling objects with more than 16 and 8 parts, due the fact that their training data was explicitly limited to part counts below these thresholds. The results demonstrate that the memory overhead introduced by our method is acceptable. This efficiency can be attributed to the integration of modern attention mechanisms, specifically FlashAttention, which are highly optimized for processing long sequences. We also adopt gradient checkpointing to reduce memory during training.
Table 2: Memory usage w.r.t different part counts.
16
30
PartCrafter Lin et al.
(2025)
(infer)
8.1G
10.3G
OmniPart Yang
20256
(infer)
12.7G
13.4G
14.3G
CoPart Dong ct al.
2025
(infer)
42.2G
Ours (infer stage-1)
7.8G
10.1G
14.3G
Ours (infer stage-II)
10.4G
15.8G
17.9G
Ours (infer stage-III)
14.1G
20.3G
36.3G
Ours (train stage-I)
21.7G
24.5G
30.4G
Ours (train stage-Il)
23.7G
28.0G
35.9G
Ours (train stage-III)
28.2G
36.2G
45.0G
We benchmark the inference latency against existing part generators, as shown in the Table. All tests are conducted on the same NVIDIA A100 GPU. PartCrafter achieves the fastest infer- ence speed while suffering from more artifacts due to its one-stage framework. OmniPart exhibits minimal sensitivity to part quantity due to its shared global coordinate space design, where token count does not increase significantly with the addition of parts. In contrast, our method allocates full-resolution grid to each part, resulting in a linear growth in token count as the number of parts increases. However, this design choice represents deliberate trade-off between generation quality and computational efficiency. The dedicated grid space for each part is fundamental to our frame- work's ability to generate high-fidelity details for small and intricate components- capability that
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shared-space approaches inherently compromise. Despite the linear growth in token count, modern optimization techniques like FlashAttention and efficient memory management prevent latency from scaling proportionally; for instance, with 8 parts (a common configuration for everyday objects), FullPart maintains a practical inference time of 55 seconds, despite preessing several times more tokens than OmniPart. This latency profile remains acceptable for applications prioritizing quality over real-time interaction, such as content creation pipelines and offline asset generation. Future work will explore hierarchical token compression strategies further improve inference efficiency while preserving our quality advantages.
Table 3: Inference latency w.r.t. different part counts.
8
16
30
PariCrafter Lin al. 2025
10s
26s
OmniPart Yang el al. 20225
17s
21s
25s
CoPart Dong el al. 2025
46s
Ours (Slage-1)
10s
25s
42s
Ours (stage-Il)
22s
83s
181s
Ours (stage-IlI)
17s
69s
131s
Ours (stage-I II III)
55s
187s
370s
C.2 EVALUATION ON PARTNET AND PARTNEXT
To further validate the generalizability of our method, we constructed additional test set compris- ing 100 random objects from PartNet Mo et al. (2018) and 100 random objects from PartNeXt Wang al. (2025). As shown Table. FullPPart consistently outperforms previous methods.
Table 4: Quantitative comparison on PartNet/PartNeXt test set.
F-Score
CD
Part-CD ULIP-Score 1
TRELLIS Xiang et al, (2025
0.75
0.15
0.24
PartCrafter Lin ct 2025)
0.55
0.51
0.10
OmniPart Yang 2025b)
0.76
0.13
0.46
0.20
Ours
0.77
0.12
0.42
0.25
C.3 MORE QUALITATIVE RESULTS AND COMPARISONS
We show extensive qualitative comparisons with PartCrafter Lin al. (025), OmniPart Yang al. (20255), and CoPart Dong et al. 22225) in Figure. 9 The results reveal that PartCratter tends to generate parts that are either coalesced or disjointed, with the latter often appearing as floating artifacts during spatial decoding (e.g.. the generated chair and hat have holes and floaters). This issue stems from use of an implicit vecset representation for direct part generation, where individual part tokens are easily influenced by others, leading to increased artifacts. OmniPart, on the other hand, struggles with fine-grained part generation due to its use of a shared global coordinate space and insufficient spatial resolution. For example, OmniPart omits the surface details of small parts in the first and second results in Figure.
For CoPart, caption the input image with VLM Wang al. (2044) and use the predicted caption to generate parts. The results show that the quality the textured part mesh generated by CoPart is limited, directly employs holistic 3D generator for refinement without part-specific tuning. In contrast, FullPart achieves superior results through dedicated part-data fine-tuning and a carefully designed part alignment strategy.
We also provide additional in-the-wild qualitative tests in Figure. 10 strate the strong generalization capability of FullPart.
The qualitative results demon-
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In-the-wild
In-the-wild
PartNext
PartNext
PartNet
x
PartNet
Input Image
OmniPart
PartCrafter
CoPart (text input)
Figure 9: More comparisons with state-of-the-an 3D Part generators.
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Figure 10: More in-the-wild results of FullPart.
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Replace hat
Delete crown
Delete bag
Rotate arm
(a) Ours
Texture
Texture
Edit
Edit
Geometry Edit
Geometry Edit
(b) CoPart
(c) Ours
Figure 11: Editing results and comparison.
Image
Sample First Batch
Sample
Figure 12: Sequential sampling for super long part sequence generation.
C.4 LONG SEQUENCE SAMPLING RESULTS
In Figure.
12 we show generation results for objects with over 30 parts using our sequential sam-
pling strategy mentioned in appendix A.2 By maintaining a global branch throughout the process, our approach ensures the coherence and quality of the newly generated parts.
C.5 MORE DETAILS AND RESULTS OF THE EDITING APPLICATION.
Thanks to our hybrid pipeline, users can edit specific parts by modifying their bounding boxes and re-running the coarse generation and refinement stages. Given assembled object, we first encode each part into its latent tokens. To regenerate a selected part while preserving others, we leverage the inherent inpainting capability of diffusion models. Specifically, we initialize the target part tokens from Gaussian noise. At each denoising step t, we replace the noisy tokens of unchanged parts by adding noise at timestep their clean tokens. This creates an inpainting setup where contextual information from the unchanged parts gradually and naturally guides the regeneration of the target part. The regenerated tokens for the selected parts are then inserted or replaced according to the updated box layout. This approach offers significant flexibility. If we want other unselected parts to also change coherently, we can start from a specific step in the sampling process (e.g., t=0.5) and stop replacing the sampled latent with the noised "clean latent". This enables the subsequent denoising process to make subtle adjustments to the other parts. Optionally, we can opt to keep the unselected parts entirely unchanged, achieving perfect preservation of the unmodified regions.
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We also demonstrate additional editing operations- gperations-including incluuding part replacement, rotation, and dele- tion in Figure. 11 (a). We provide comparisons with prior part-based generative methods Dong et al. (025 in Figure. IT (b) and (c). The results confirm that FullPart achieves competitive editing performance.
C.6 QUANTITATIVE ABLATION RESULTS
We further validate the effectiveness of our designs through quantitative ablation studies. The sults in Table. cllearly demonstrate the critical importance of the carefully designed center-corner encoding mechanism in enabling the model to accurately perceive the actual size of each part. More- over, we observe a significant performance drop when directly using part labels from raw meshes without human annotation. Finally, allocating full-resolution grid each part is shown to enhance the quality of the generated parts.
Table 5: Quantitative ablation results on Perteerse- XL test set.
F-Score f
CD |
Part-CD
ULIP-Score f
w/o Corner Enc
0.65
0.36
0.55
0.13
w/o Human Anno
0.72
0.20
0.48
0.17
w/o Per-part Grid
0.76
0.17
0.40
0.21
Full model
0.81
0.11
0,36
0.24
C.7 DISCUSSION OF THE LAYOUT GENERATION
Previous methods directly predict the bounding boxes (layout) parameters (x, y, z, w. h, d). For ex- ample, LayoutGPT uses LLM to predict box parameters and then retrieve objects based on boxes for 3D scene generation. OmniPart also trains an auto-regressive model to predict part box parameters. In contrast, we regard each bounding box as box mesh, and fine-tune an implicit vecset diffusion model to generate box meshes and then compute the boxes from the extracted box meshes. this way, FullPart can inherit the priors from the pre-trained vecset diffusion model and turn the accurate box parameter regression task into a more robust diffusion-basee generation process.
C.8 DIFFERENCE WITH COPART
The first major difference lies in the part representation. CoPart Dong et al. (2025) represents 3D parts using combination of implicit vecset geometric latents and image latents. In contrast, Full- Part represents each part with explicit voxel-based latents, which allow texture information to be directly encoded into the voxel tokens-similar to TRELLIS. Due to the constraints of its implicit representation, the part geometric tokens in CoPart share global coordinate space, leading in- sufficient resolution for fine- greeggraied parts. In contrast, FullPart assigns each part an independent full-resolution grid, thereby enabling detailed geometrie modeling.
Second, the generation stages of CoPart and FullPart differ significantly in their objectives and de- signs: i) In the first stage, CoPart requires users to provide part bounding boxes input. These boxes are injected into a dual-branch DiT to generate part-centric multi-view images (via ControlNet) and coarse part shapes (via rross-attention). In contrast, FullPart eliminates the need for manual box input by automatically generating boxes through a box diffusion process. It then employs a compact single-branch branch DiT generate coarse voxel-based parts. Notably. thanks to our explicit represen- tation, each voxel latent is intrinsically defined within its corresponding box, eliminating the need for the additional architectural mechanisms used CoPart to inject box information. ii) In the second stage, CoPart feeds the coarse shapes and multi-view images into pre-trained holistic 3D object generator to obtain textured part meshes. Notably, CoPart treats each part as an independent whole object and directly employs this holistic generator without any fine-tuning data. CoPart claim that the generator can produce textured part meshes when initialized with both coarse shapes and multi-view images. In contrast, FullPart's 's refinement stage is fine-tuned on part data and incorporates a center-corner encoding mechanism to carefully align different parts, thereby achieving more coherent and higher-quality part generation.
part-specific
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box ambiguity
Input Image Boxes
Failure
Success
Figure 13: Typical failure case due to box ambiguity.
C.9
USER STUDY
We conducted a user study involving 73 participants from 10 professional backgrounds. Participants were asked select the best result based global coherence or assembly structural plausibility. The results demonstrate that FullPart outperforms all previous methods in both global quality and part-level coherence.
Table 6: User Study (% preference).
Method
Global coherence
Part structural plausibility
PartCrafter Lin et al
2025 )
7.3
2.7
OmniPart Yang et al 20256
38.8
21.0
CoPart Dong et al. Ours
2025
5.0
16.0
48.9
60.3
D LIMITATIONS AND FAILURE CASE ANALYSIS
We observe two primary limitations. First, the model occasionally generates darker textures, which we attribute to the dark render in the data processing stage: our optimization of render settings for speed came at the cost of quality. Specifically, we lowered critical ray tracing parameters, including the number of light path bounces and the overall sampling rate. These simplifications limited how effectively light could bounce and accumulate in the scene, leading to an underexposed and less accurate image. Second, typical failure case is shown in Figure. [3] Because the box's 's structure is ambiguous, the model gets confused about the final design, which results in failures like generating an unnecessary padding layer. In addition, our computational cost increases linearly with the number of parts. We plan to explore ways to accelerate the inference in future work.
E
USE OF LARGE LANGUAGE MODELS
During the writing of this paper, we employed large language model (GPT-4) solely to improve the clarity and fluency of the original text we had drafted. The model was used as tool for language polishing and did not contribute to the conceptual development, technical execution, experimental analysis, or scientific conclusions presented in this work
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Reviews:
[{"review_id": "UaJ0149YGM", "reviewer": "Reviewer_2Ufk", "summary": "This work introduces FullPart, a novel framework for high-resolution part-aware 3D generation. This framework overcomes the resolution and coherence limitations of previous approaches by combining both implicit and explicit paradigms. Specifically, FullPart consists of three stages: (1) implicit layout generation for spatial arrangement; (2) explicit structure generation at full resolution for fine geometric details; (3) refinement for realistic appearance. Besides, the center-corner encoding mechanism is proposed for global part coherence. To support this framework, this work further introduce PartVerse-XL, the largest human-annotated 3D part dataset with semantically consistent part labels and textual descriptions. Experiments show that FullPart achieves state-of-the-art performance in both part- and object-level evaluations, outperforming existing methods such as PartCrafter, OmniPart, and TRELLIS.", "questions": "The sequential sampling strategy for generating over 30 parts appears promising. Could the authors provide results to demonstrate whether the structural coherence or overall geometric quality diminishes as the number of parts increases?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The proposed per-part full-resolution generation paradigm is technically sound and yields satisfactory results. As shown in Figures 4 and 5, this method can improve the generation performance of both part-level and object-level. Besides, the proposed center-corner encoding mechanism utillizes a unified positional encoding strategy to preserve global spatial coherence across parts of different scales, effectively addressing the spatial discrepancy between different parts.\n\n2. This work presents the largest human-annotated 3D part dataset, including 40K high-quality 3D objects and 320K semantically consistent 3D parts. The scale and diversity of this dataset will facilitate future research on part-aware 3D generation.\n\n3. The paper provides quantitative and qualitative performance comparisons as well as a comprehensive ablation analysis. Experimental results (Table 1, Figure 4, Figure 5) demonstrate the superior performance of FullPart compared to previous part-level and object-level 3D generation methods.\n\n4. The paper is clearly written and well-organized, making it easy to follow.", "weaknesses": "1. Generating each part within a full-resolution voxel grid substantially increases memory usage and computational costs, particularly for complex objects with numerous parts. The paper does not include an analysis of computational efficiency or stress testing in this regard.\n\n2. The test set is limited to 100 manually selected untrained objects, which may not comprehensively evaluate the model's generalization capability and overall performance.\n\n3. Quantitative ablation analysis results are missing.\n\n4. This paper lacks a discussion of its limitations and failure cases.", "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": "KPGa6ScDe0", "reviewer": "Reviewer_YbuT", "summary": "This paper presents FullPart, a framework for part-level 3D object generation in which each part is produced at full resolution. The main contribution lies in the integration of implicit and explicit representations:\n1. An implicit vecset-based diffusion model learns the layout of parts,\n2. An explicit voxel-based representation generates complete high-resolution 3D parts, and\n3. The resulting coarse 3D parts are refined into textured meshes.\n\nThe authors also introduce PartVerse-XL, a new dataset for part-level 3D generation. Compared with PartCrafter and OmniPart, FullPart demonstrates superior geometric fidelity and structural coherence. Because each part is generated at full resolution, the overall 3D objects contain richer details than those produced by TRELLIS or Direct3D-S2. Ablation studies confirm the effectiveness of Center-Corner Encoding, human-refined annotations, and the Per-Part Full-Resolution Grid.", "questions": "1. The Related Work section could be expanded to discuss a broader range of part-level 3D generation methods, emphasizing the types of representations they employ (e.g., implicit, voxel-based, point-based, or hybrid).\n2. Add a section on the use of large language models (LLMs).", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The combination of implicit and explicit representations is well-motivated and effectively leverages the strengths of both paradigms.\n2. The construction of the PartVerse-XL dataset is a valuable contribution that can advance research in part-level 3D generation.\n3. The proposed Center-Corner Encoding mechanism is conceptually simple yet empirically effective.", "weaknesses": "1. Section 2.2 discusses mainly recent (2025) part-level 3D generation works. The related work could be more comprehensive by including earlier implicit Tri-plane–based approaches, such as Frankenstein [Yan et al., 2024] and StdGen [He et al., 2024].", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1COm4Km5PS", "reviewer": "Reviewer_TxpB", "summary": "This paper proposes FullPart, a 3D part generation framework integrating implicit and explicit paradigms. It first generates bounding box layouts via implicit box vecset diffusion, then creates each part in an independent full-resolution voxel grid, and uses a center-corner encoding strategy to maintain global coherence. It also introduces PartVerse-XL, a large human-annotated 3D part dataset. Experiments show FullPart achieves state-of-the-art 3D part generation results.", "questions": "1. Is there any innovation in the layout generation stage? Can you introduce how prior work handles layout generation and what are the differences compared to your method?\n2. How does the computational cost (in terms of training time, inference time, and memory usage) of FullPart compare to existing 3D part generation methods that use explicit voxel representations?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. It identifies the problem that the resolution given to small parts is limited in 3D part generation by explicit voxel representation. And solves this problem by generating each part in an independent full-resolution voxel grid.\n2. It creatively proposes a new positional embedding strategy to maintain global coherence in part generation with independent voxel grids.\n3. The paper introduces PartVerse-XL, a large human-annotated 3D part dataset, which is a valuable resource for the research community.\n4. The figures are clear and attractive, effectively illustrating the framework and results.", "weaknesses": "1. It looks like generating each part in full-resolution voxel grids will significantly increase the computational cost of training and inference. However, the paper does not provide any analysis or discussion on the computational efficiency of FullPart compared to existing methods.\n2. The evaluation is only conducted on the PartVerse-XL dataset, which is introduced by the authors themselves. It would be more convincing if the authors could also evaluate their method on other established 3D part generation datasets (or synthesize from 3D part understanding datasets) to demonstrate its generalizability.", "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": "vTKCIoDaCX", "reviewer": "Reviewer_a2W1", "summary": "FullPart: Generating each 3D Part at Full Resolution presents a novel framework for part-based 3D generation that addresses a critical limitation in existing methods: the resolution bottleneck for small parts. The authors identify that prior work, whether using implicit latent tokens or explicit voxel grids, forces all parts to share a single global representation space. This causes small but complex parts to be allocated insufficient resolution, leading to a loss of geometric detail.\n\nFurthermore, the authors introduce PartVerse-XL, a large-scale, high-quality dataset of 40K 3D objects and 320K human-annotated parts, to address the scarcity of reliable training data for part-level generation. Extensive experiments show that FullPart achieves state-of-the-art performance, particularly in generating plausible geometries for small and occluded parts.", "questions": "1. Significant Architectural Similarity to Preceding Work\n2. Insufficient Qualitative Comparison with Key Part-Based Baselines\n3. Under-Exploration of the Editing Application", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Introduction of a High-Quality, Large-Scale Dataset: PartVerse-XL\n\nThe creation and release of PartVerse-XL is a significant contribution that addresses a major roadblock in the field.\n\nAddressing a Critical Data Scarcity: The paper correctly identifies that existing 3D datasets lack high-quality, semantically consistent part-level annotations. Artist-created metadata is often noisy and inconsistent, hindering the training of robust models. The two-stage pipeline of automated pre-segmentation followed by expert human refinement ensures both high quality and semantic consistency. This is a costly but necessary step to create a reliable benchmark. PartVerse-XL provides a foundational resource that will likely accelerate future research in 3D part generation, much like how datasets like ImageNet propelled 2D vision. By training and evaluating on this dataset, FullPart establishes a strong, reproducible baseline for the community.\n\n2. A Well-Designed and Effective Hybrid Pipeline\n\nThe core pipeline of FullPart is a thoughtfully engineered solution that effectively combines the best of two worlds. The pipeline is cleverly designed to use the right representation for the right task:\n\nStage 1 (Implicit for Layout): Bounding boxes contain minimal geometric detail but require an understanding of global structure and inter-part relationships. Implicit diffusion models are perfectly suited for this \"conceptual\" generation task.\n\nStages 2 & 3 (Explicit for Geometry): Generating high-fidelity geometry demands precise spatial control. By allocating a dedicated voxel grid to each part, FullPart bypasses the fundamental resolution constraint of shared-grid methods. The ablation study in Figure 6(c) powerfully demonstrates the dramatic improvement this brings, especially for small parts like thin chair legs.", "weaknesses": "1. Significant Architectural Similarity to Preceding Work\n\nWhile the pipeline is effective, its core structure bears a strong resemblance to contemporaneous work, particularly Co-Part (Dong et al., 2025), which may dampen its perceived novelty.\n\nParallels with Co-Part: Co-Part also proposes a multi-stage, diffusion-based framework for part generation that utilizes a layout stage followed by geometry generation. Both methods employ transformer architectures with intra-part and inter-part attention mechanisms to maintain coherence.\n\n\n2. Insufficient Qualitative Comparison with Key Part-Based Baselines\n\nThe experimental section, while including quantitative metrics, lacks the qualitative depth needed to fully convince the reader of its superiority over other part-aware generators.\n\nSparse Visual Comparisons: Figure 4 provides only a limited set of visual comparisons against PartCrafter and OmniPart. To truly showcase its advantages, the paper needs a more extensive gallery of side-by-side comparisons, especially on challenging cases.\n\nFailure Cases of Baselines: It would be highly informative to include examples where methods like OmniPart fail dramatically (e.g., a chair where the legs are voxelized into blobs) and show how FullPart succeeds.\n\nMissing Baseline: Given the noted similarity, a direct qualitative comparison with Co-Part is crucial but is absent. Showing a visual comparison would allow for a direct assessment of whether the full-resolution grid leads to tangibly better results than Co-Part's approach.\n\nUser Study for Coherence: A user study evaluating the global coherence and structural plausibility of the assembled objects compared to other part-based methods would provide stronger evidence than metrics like Part-CD alone.\n\n3. Under-Exploration of the Editing Application\n\nThe editing application presented in Figure 7 is promising but is only briefly mentioned. The paper would be strengthened by a more thorough exploration of this capability. For instance, it could demonstrate more complex edits like part replacement, rotation, or deletion, and quantitatively evaluate the preservation of unedited parts. How does this editing capability compare to other editable part-based generators? A discussion or comparison on this front would highlight a key practical advantage of the proposed pipeline.", "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)