UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections
Authors: Zeyu Cai
Ziyang Li
Xiaoben Li
Boqian Li
Zeyu Wang
Zhenyu Zhang
Yuliang Xiu
Venue: ICLR 2026 Poster (2026)
Keywords: 3D clothed human reconstruction
image-based reconstruction
human digitization
SMPL
multi-view diffusion model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: We present UP2You, the first tuning-free solution for reconstructing high-fidelity 3D clothed portraits from extremely unconstrained in-the-wild 2D photos. Unlike previous approaches that require "clean" inputs (e.g., full-body images with minimal occlusions, or well calibrated cross-view captures), UP2You directly processes raw, unstructured photographs, which may vary significantly in pose, viewpoint, cropping, and occlusion. Instead of compressing data into tokens for slow online text-to-3D optimization, we introduce a data rectifier paradigm that efficiently converts unconstrained inputs into clean, orthogonal multi-view images in a single forward pass within seconds, simplifying the 3D reconstruction. Central to UP2You is a pose-correlated feature aggregation module PCFA, that selectively fuses information from multiple reference images w.r.t. target poses, enabling better identity preservation and nearly constant memory footprint, with more observations. Extensive experiments on 4D-Dress, PuzzleIOI, and in-the-wild captures demonstrate that UP2You consistently surpasses previous methods in both geometric accuracy (Chamfer-15\%$\\downarrow$, P2S-18\%$\\downarrow$ on PuzzleIOI) and texture fidelity (PSNR-21\%$\\uparrow$, LPIPS 46\%$\\downarrow$ on 4D-Dress). UP2You is efficient (1.5 minutes per person), and versatile (supports arbitrary pose control, and training-free multi-garment 3D virtual try-on), making it practical for real-world scenarios where humans are casually captured. Both models and code will be released to facilitate future research on this underexplored task.

Published as a conference paper at ICLR 2026
UP2YOU: FAST RECONSTRUCTION OF YOURSELF FROM UNCONSTRAINED PHOTO COLLECTIONS
Zeyu Cail.2 Ziyang Lii Xiaoben Li Boqian Li
Zeyu Wang*
Zhenyu Zhang Yuliang Xiu
Westlake University
Naajjngg University
3 The Hong Kong University of Science and Technology (Guangzhou)
Shared Corresponding Author
Input
Orthogonal View Generation
High-Quality Textured Mesh
UP2You Rectify
UPuYou Reconstruct
Unconstrained Photos
Figure 1: Overview of UP2You. Our method reconstructs high-quality, textured 3D clothed portraits from unconstrained photo collections. It robustly handles highly diverse making them compatible with traditional reconstraction algorithims.
inpuls rectifying them orthogonal multi- view images corresponding normal mups.
ABSTRACT
We present You, the first tuning-free free solution for reconstructing high-fidelity 3D clothed portraits from extremely unconstrained in-the-wild 2D photos. Unlike previous approaches that require "clean" inputs (e.g.. full-body images with mini- mal occlusions, well-calibrated cross-view captures), UP2You directly processes raw, unstructured photographs, which may vary significantly in pose, viewpoint, cropping, and occlusion. Instead of compressing data into tokens for slow online text-to-3D optimization, we introduce a data rectifier paradigm that efficiently converts unconstrained inputs into clean, orthogonal multi view images in single forward pass within seconds, simplifying the 3D reconstruction. Central to You is pose-correlated feature aggregation module (PCFA), that selectively fuses infor- mation from multiple reference images w.r.t. target poses, enabling better identity preservation and nearly constant memory footprint, with more observations. We also introduce a arrceiver-based multi-reference shape predictor, removing the need for pre-captured body templates. Extensive experiments on 4D-Dress, Puzzle1OI, and in- wnlthe-wild captures demonstrate that UP2You consistently surpasses previous methods in both geometric accuracy (Chamfer- 15% P2S-18% on PuzzleIOI) and texture fidelity (PSNR-2) 1 LPIPS-46% on 4D-Dress). UP2You is effi- cient (1.5 minutes per person), and versatile (supports arbitrary pose control, and training-fres free multi-garment 3D virtual try, making it practical for real-world scenarios where humans are casually captured. Both models and code will be released facilitate future research this underexplored task.
INTRODUCTION
Reconstructing 3D clothed humans from unconstrained photo collections, like the personal albums (Fig. 2-Left), is a challenging and largely unexplored research frontier. Unlike prior tasks such single-image 3D reconstruction [23, 48, 65, 92, 93], monocular video-based reconstruction [18, 28, 35], or multi-view 3D reconstruction [55, 64, 101], this problem is distinguished by the highly unstructured nature of the input: appearance information is present but scattered across photos

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where subjects are often partially captured or occluded, and camera as well as body poses rarely synchronized. As a result, establishing accurate 2D-to-3D correspondences is extremely difficult, with the help of most advanced off-the-shelf human-centric estimators (i.e., camera, body pose, landmarks, geometric cues, etc). In contrast, traditional 3D reconstruction algorithms typically assume "clean captures" (i.e., full-body capture with simple poses, synchronized cameras, etc), where well-aligned 2D-to-3D correspondences can be readily established using the estimators above.
Two potential strategies to address above challenges: 1) Data Compressor: Crop and group photos into local and global patches (e.g.. head, full-body) [102], or segment input photos into multiple assets (e.g., garments, hair, face, accessories) [94]. then compress these patches or assets into learnable tokens, and finally assemble them as text prompt to generate 3D humans via text-to-3D techniques [62]: 2) Data Rectifier: Convert the incoming "dirty or incomplete captures" into clean and complete ones, e.g., orthogonal orbit views with canonical poses, which are easier to reconstruct with traditional 3D reconstruction algorithms. Essentially, the data compressor operates mainly at the representation level, without substantially improving the generative model's ability to ensure 3D consistency and identity preservation - a limitation noted in PuzzleAvatar [94] as "unpredictable hallucination."" The data rectifier, however, refines not only the input data but also the generative model's 's prior, via continued training on synthetic multi-viww renderings of humans, enabling more consistent 3D reconstruction in terms of both identity and viewpoint, from unconstrained photographs. YPuYou falls in the second category, as shown in Fig. 2.
3D clothed
PuzzleAvatar [94] is the representative of the first strategy, it first "'dcomposs"" the unconstrained pho- los into multiple asset soups, all of which are linked with unique learned tokens via DreamBooth [70], then it "compose" these assets into 3D full-body representation via score-distillation sampling (SDS) [62], where the 3D reconstruction task is reformulated as a text-to-3D task, bypassing ex- plicit canonicalization. However, this process takes hours since both DreamBooth fine-tuning and SDS-based optimization are time-consuming and unstable, see Fig. 2. Additionally, ground-trute SMPI.-X meshes are needed for initialization, as predicting shape parameters from unconstrained photo collections is non-trivial. Regarding the second strategy converting inputs into orthogonal orbit views- - some attempts [23, 48, 60] have been made. However, these methods are restricted to single-image inputs and cannot fully leverage the multiple unconstrained photos. Essentially, these methods act more as "data inpainters" [84]- - synthesizing unseen views from seen capture- rather than as "data rectifiers" that unify the messy observations into structured output. Designed mainly for constrained inputs (i.e., a single image with bull-body coverage), these methods cannot handle unconstrained photos or scale up the reconstruction accuracy with the number of inputs.
To the best of our knowledge, YP2 is the first work to unlock the "data rectifier" strategy on unconstrained photo collections, directly transforming raw unconstrained photo collections into orthogonal views while faithfully preserving subject identity. This is not trivial extension of prior arts, as it 1) requires effectively aggregating information from multiple unconstrained inputs, which may vary significantly in terms of body poses, camera viewpoints, croppings, and occlusions; 2) must be efficient enough to process varying numbers of input photos (ranging from one to dozens) without incurring significant computational overhead; and 3) needs to overcome the dependency on ground-truth body shapes, which are often unavailable real- scenarios.
Specifically, You aggregates ReferenceNet features [27], extracted from unconstrained photos according to body poses, via the proposed Pose-Correlated Feature Aggregation (PCFA) module. This module implicitly learns correlation weights between unconstrained reference images and target pose conditions (i.e., SMPL-X normal maps). Guided by these correlation maps, PCFA uses an optimized t topk strategy selectively aggregate the most informative image features for generating each orthogonal view. As result, the memory footprint remains nearly constant regardless of the number of input photos, enabling effective and efficient information fusion.
To get rid of the dependence on ground-truth body shapes, we design a shape predictor based on perceiver structure [34, 46] to regress SMPL-X shape parameters directly from unconstrained photo collections. Lastly, with another MV-Adapter [32] to generate multi-view normal maps, followed by mesh carving and texture baking [48], UP2You reconstructs high-quality textured meshes from unconstrained photos in 1.5 minutes. We evaluate our generation results on PuzzleIOI, 4D-Dress, and self-collected in- in-the-wor wild datasets. Our method surpasses other state-of-the-ee approaches in both geometric accuracy (Chamfer- 15% P2S-18% on PuzzlelOI) and texture fidelity PPSNR-21%1
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Personal Tokens
DreamBooth
SDS Optimization
hours
Weights
Rectifier
Reconstruction
seconds
minute
Personal Uncomstrained Photo Collections
Orthogomal View
Images Normals
Figure Paradigm differences
previous works and UPZYou. Top: Previous works like PuzzleAvatar [94] AvatarBoeth [102]
compress unconstrained photos into implicit personal tokens and DreamBooth weights [70] through line tue-ttning then generate 3D humans via SDS optimization [70]. Bottom: UP2You directly rectifies unconstrained photo collections orthogonal view images normals, reconstracts textured human meshes, achieving superior quality while reducing processing time from hours to 1.5 minutes.
LPIPS-46%. on 4D-Dress), while also demonstrating flexibility and superior generalization for single-image reconstruction, and enabling 3D virtual try-on application, all without extra training.
Our main contributions w.r.t. the prior arts are as follows:
Efficient. As Fig. 2 shows, unlike previous DreamBooth SDS paradigm (>4 hours), You acts as "data rectifier" instead, to directly generate "clean" multi-views from "dirty" unconstrained inputs in one forward pass (<15 secs). It can process one, several, dozens of photos with nearly constant memory footprint. The full pipeline, including multi-view normal generation plus mesh carving and texture baking, completes in 1.5 minute.
Effective. Thanks to the PCFA module, which selectively aggregates the most informative regions from the reference images for synthesizing target views, YP2 significantly outperforms prior SOTAs (PuzzleAvatarr, AvatarBooth, PSHuman) in both geometry accuracy and texture fidelity, and delivers consistent shape and identity regardless of input forms or pose conditions. Notably, the reconstruction quality even scales up with more unconstrained inputs, echoing the principle of The More You See in 2D, the More You Perceive in 3D [21].
Versatile. PuzzleAvatar requires A-posed body template with ground-truth shape for 3D initialization, while YPu is flexible to random pose control, directly regresses body shapes from unconstrained photos, and inherently supports multi-garment 3D virtual try-on, for free.
RELATED WORK
2.1 3D CLOTHED HUMAN RECONSTRUCTION
The field of 3D clothed human reconstruction has been extensively studied over the past few decades. Early methods primarily focused on reconstructing human geometry and texture from dense multi- view image captures [35, 52, 61]. Subsequent research has broadened the scope to include full-shot monocular video inputs [18, 19, 28, 90], enabling more flexible and accessible data acquisition. Recent advances in generative models, particularly diffusion models [25, 42, 69, 81], and the emergence of SDS-based 3D) human generators [30, 44, 50, 53, 86, 86), have further propelled the field. An increasing number of vidco-based human reconstruction approaches now leverage learned generative priors to address common challenges in real-world video captures, such as occlusions [19, 59], view inconsistencies [36], and poor texture details [83].
Such generative priors, learned from large-scale datasets, play a more crucial role for the inherently ill-posed problem of 3D human reconstruction, especially when the input data is sparse or incomplete. The most sparse input format is a single image [23, 31, 33, 48, 65, 67, 71, 72, 92, 93, 107]. In essence, it can be regarded as "conditional generation" problem [31], since large portions of the geometry such the unseen backside and occluded regions must be plausibly inferred or synthesized from the visible pixels. Building on this "reconstruction as conditional generation" paradigm, numerous works have further advanced the field [3, 15, 48, 105]. Apart from multi-view posed captures, full-shot monocular video, and single image, numerous works have sought to expand the range of input modalities, for example, by incorporating dual front-back captures [38, 55] or multi-vien view unposed full-body images [29, 66, 97, 101, 108] to improve reconstruction fidelity and completeness.
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Despite these advances, existing methods still fall short of handling truly "unconstrained" photos those with partial views, occlusions, extreme camera viewpoints, dynamic body poses, and inconsistent aspect ratios. Accurately estimating body shape [12, 43, 45, 89, 98, 104] from such unconstrained photo collections is nearly impossible. Moreover, given multiple "dirty" reference images, image-based HMR methods often fail to deliver consistent results. This inconsistency manifests as significant variations in the predicted body shapes for the same subject some reconstructions may appear unnaturally thin, others excessively fat, and some may completely fail, especially in cases of partial or occluded inputs. As shown in Tab. 2 and Fig. 7, it becomes challenging to determine which, if any, of the predicted shapes truly represent the subject.
In contrast, You addresses these data format constraints by functioning as comprehensive "data rectifier," " directly transforming unstructured or "dirty" inputs into orthogonal "clean" views, with consistent 3D and identity, that can be seamlessly utilized for robust 3D reconstruction.
2.2 UNCONSTRAINED PHOTOS TO 3D
Most real-world data is inherently unstructured, presenting significant challenges for 31) recon- struction tasks that require reliable spatial correspondences. The earliest work in "Unconstrained Photos to 3D" can be traced back to Photo Tourism [80], which reconstructs 3D scenes from large collections of Internet photos. Recent advances in neural rendering and generative models have further advanced this field, enabling more robust and realistic 3D reconstructions from unstructured image collections [10, 49, 87]. However, these methods primarily focus on rigid objects or scenes and cannot be directly applied to 3D clothed human reconstruction, which involves highly articulated and non-rigid structures. A critical open question is how to effectively extract and aggregate identity features from unconstrained photos not only for general objects [41, 103, 109], but especially for dynamic humans- - and reproduce them in a 3D-oonisstent manner. Several works on subject-driven image generation [2, 4, 13, 14, 16, 40, 70, 73, 85]. well as ID-consistent 2D human portrait generation [9, 63, 75, 84, 95], are discussed in the Sup.Mat. (Appendix B). However, these methods primarily designed for 2D image generation and lack the mechanisms to ensure cross-view consistency or the precise latent feature aggregation necessary for high-fidelity 3D reconstruction.
The most relevant works addressing this challenge are PuazteAvatar [94] and AvatarBooth [102]. Both first employ few-shot personalization [4, 70], as Total Selfie [9] and RealFill [84], to distill identity information from unconstrained photos into customized diffusion model, as unique tokens. Subsequently, guided by these unique tokens, they utilize Score Distillation Sampling (SDS) [7, 37, 62, 100] to optimize neural-based 3D representation [56, 76]. In short, the entire pipeline of these methods can be summarized as "unconstrained photos + personalized diffusion models with learned specialized tokens + SDS-based based Text-to-DD". However, fine-tuning diffusion models and optimization- based SDS methods are extremely time-consuming. Moreover, these fine-tuning approaches act as form of lossy compression: the strong priors of diffusion models often override subject-specific features, leading loss of identity and fine-grained details, or introducing unpredictable hallucinations. In contrast, YP2 is a tuning- free method that faithfully reconstructs 3D humans from unconstrained photos in just 1.5 minutes, while well preserving human identities.
METHOD
Our objective is reconstruct high-quality textured mesh from unconstrained photos with unknown camera parameters and human poses. To this end, we first generate orthogonal full-body images from the unconstrained inputs, conditioned on SMPL-X normal maps that contain both camera and pose information (Sec. 3.1). Next, we utilize these orthogonal multi-view RGB images to generate corresponding multi-view normal maps, which serve as geometric cues for detailed mesh reconstruction (Sec. 3.2). To handle in-the-wild images without SMPL-X annotations, we further introduce a body shape estimator capable of inferring human body shape by integrating information from a handful of unconstrained photos (Sec. 3.3).
3.1 ORTHOGONAL. MULTI- VIEW IMAGES GENERATION
To tackle orthogonal multi view image generation from unconstrained photo collections, we adopt MV-Adapter [32] as our backbone (introduced in Appendix C). MV-Adapter integrates ReferenceNet
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π
Stape (Sez
ReferenceNa R
Curclltii Sags (Soc
Feztre Sclection (Sez 3.1)
Pose Guide
MVDifission Model
Besderinx
Notso
Noral Maps Gonemation (Sor
Images Gonernt Nommlls N
Figure Pipeline UP2You.
unconstrained input photos we predict SMPL-X shape parameters (Sec. 3.3) initialize the SMPL-X mesh with predefined pose and expression parameters, We then generate orthogonal view images V based on and SMPL-X normal rendering with the proposed PCFA method-predice predict correlation maps and select most informative features (Soc. 3.1). Finally, we produce multi-view normal maps N from P and V. and reconstruct the linal textured mesh (Sec. 3.2).
R [27] as the reference image encoder and incorporates raymaps into the diffusion UNet as view conditions, enabling the synthesis of six orthogonal views. For our task, orthogonal SMPL-X normal maps as view conditions. Unlike the original MV-Adapter, which handles only single-image inputs, our approach extends it to process multiple unconstrained photos.
As shown Fig. 3, given N unconstrained reference images I [I1 ..., Iv) ) of person in the same outfit, our goal is to synthesize M orthogonal target views V = (V1...., Vw } each conditioned on corresponding SMPL-X normal map To extract the most informative features
Query Proj. W.O.
O.
P = [P, for each target view, we introduce the fose-Correlatee Feature Aggregation (PCFA) module, which predicts correlation maps AM C= { Cy Ji=1 between reference and target views (see Fig. 4). Based on C, PCFA select features for each target viewpoint for the generation of orthogonal views V.
Trandoema illocks 'I'
Comelitio Map
BAL
X
Meaz & AugPool
Pose Encoder
Key Proj W.X"
Using all reference features for ortho-view generation is computationally intensive, as memory usage grows with the number of unconstrained references. How- ever, many reference pixels are irrelevant for given target view (e.g., back-view references for front-view synthesis). Therefore, we adaptively determine each reference's 's contribution based on the target pose to reduce computational cost.
Correlation Map Prediction.
Target Poac P.
DINO Fealuecs Xtel
Figure 4: Pose-Dependent Correlation Map. Correlation is colored as Highor + Lower.
To achieve this, disentangle human -speaiicc identity features from viewpoint correlation information in the unconstrained reference inputs. Drawing inspiration from [26, 39], predict correlation maps for reference images conditioned target poses, as illustrated in Fig. 4. For each target pose P,, i (1, 2, ..., M), we estimate a correlation map that indicates the pixel-wise relevance of each reference image for generating the corresponding view. Specifically, we employ a pose image encoder grose and DINOv2 [57] model gref to extract features from the target pose image and all reference images: X
Epose (Pt) and X = g (I), where ofe represents the concatenation of all DINOv2 outputs (X.ef J- Subsequently, we feed both Poose and e into transformer block T that comprises layers of self-attention and cross-attention, where X.ee functions as the query, key, and value in self-attention operations, and as the query in attsnntttnnion operations, while x serves both key and value in cross- coss-attention operations. Through T, an output feature O, T(X,os XTe) that integrates reference information relevant to the target pose is produced. We derive the image correlation map C' by computing the attention map between O and Xte..
A' W,O, x Wi.
(I)
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C' = C1 CN.....Nii (mean
Here, Wg and W3 are learnable projection matrices applied to O, and X respectively. The resulting Nhw captures the relevance between the target pose and reference features.
attention map A' R To obtain the final reference correlation scores, we compute the mean along the first dimension of A' using mean(.) R Nhw + Now, In this context, is the token number of O, and w denote the height and width of x and d is the feature dimension of W; O and Wixere, We further apply AvgPool to smooth the predicted correlation map and ReLU to suppress negative values.
The correlation maps of PCFA are based on fine-grained semantic correlation between target bodies and DINO features of references. Unlike previous methods [26, 39] that depend landmark similarity, our correlation map encodes richer outfit details, enabling more accurate reconstruction.
Feature Selection. The predicted correlation maps enable PCFA to selectively aggregate the most informative reference features for each target view. Specifically, we utilize ReferenceNet R. as the reference image encoder to extract multi-scale reference features F {F1, F 2,,... F where is the number of layers. For each target pose P; and the reference feature F; E IR NS& at layer k, Interpp((") that
we first interpolate the corresponding correlation map C' E NNu to get Č' aligns with the spatial dimensions of F&- Here S2 denotes the spatial size of Fx, and Interp:() R ggNhu. + R S, denotes the interpolation operator.
We then select the most relevant reference features F for view P, based on Č'. Specifically, we employ the topk selection strategy to obtain the selected indices of Fx:
[K Kgg .... k,s, sort(topk (C'): 7Sx)),
where [kj, ki.,.., ..., k,,, are the indices of the selected features, topk(-) returns the top 7S, indices, and y controls the proportion of features retained. To preserve spatial order, we apply sort(-). Using these indices, we extract the selected reference features F), R75exe;
F FR [kk
kg kGs.) Ce(k4, k ky
Given the aggregated reference features F {F F F....., F2 H we synthesize the orthogonal multi-view images as V = Dr (F,, prsb where Desb is our multi-view image generation model and (rsb(() (+) is the pose guider that encodes the pose condition into Deb
3.2 NORMAL MAP GENERATION AND MESII RECONSTRUCTION
For multi-view reconstruction (MVS) [51, 54, 91], we generate multi-view clothed normal maps N from the generated images V, conditioned on target poses P. and reconstruct the mesh using both V and N.
R
Normal Map Generation. To ensure multi-view con- sistency and provide strong geometric cues for nor- mal map generation,
Nuse
follow [93] and incorporate SMPL-X normal renderings as additional conditions. As Fig. shows, we also adopt MV-Adapter the backbone of clothed normal generator Daormal We utilize the generated orthogonal RGB views V as ref- erence inputs, and employ the pose guider panormll (-)
GeneuRed Nomaa's
Figure S: Normal Map Generation Pipeline. The main input difference with the generated multi-view orthogonal images V. insteud of unconstrained inputs I.
incorporate multi-view pose conditions. The multi-view clothed normal maps are then generated
via N Duomal (V, Pnoomal
( (P)).

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Puzzkk01
4D-Dress
in-lee-wild
SSIMT
LPIPS.
Chanleri
P2S.
Normal
LPIPS,
Chamferi
Noemal
CLIP-T
DINO
AvstarBooth
16,879
0.860
D.1544
6,635
6,097
D.027
18.156
0,850
0,1718
6,846
6.978
0.0311
0.878
a619
PuzzleAvvaar
21.664
0,0339
3,294
3,165
00150
21,576
0.1081
1956
0.0170
0.977
Chus. (lingge)
23,895
0.926
B.05455
25,848
0.920
0.8576
0,972
0.932
Uurs (Mesh)
0.940
0,0474
2.724
2,605
0,0115
0,0654
0,0122
Table I: Quantitative Comparison with Baselines. UP2You achieves the best texture fidelity, geometry accurncy and perception similarity,
Mesh Carving and Texture Baking, Starting from the initial SMPLX mesh, we refine mesh details using the generated N and project per-vertex colors from V. following PSHuman [48]. To better preserve hand geometry, we replace the hand region with that from the initial mesh as in ECON [93], and then perform texture baking using the generated multi-view RGB images.
3.3 MULTI-REFERENCE SHAPE PREDICTOR
The initial SMPL-X mesh is critical to the entire You pipeline, as provides the pose condition P for multi-view generation and serves the basis for mesh reconstruction. SMPL-X mesh T e R10754x3 are defined as T(B 8, v), where 8,0,00 sb are shape. pose. and expression parameters respectively. While the target pose and expression of the SMPL-X template can be predefined (e.g. T-pose or A-pose with neutral expression), the body shape parameters must be estimated from unconstrained input images. Existing shape predictors [23, 48, 65] are typically designed for single-image scenarios and struggle to effectively leverage multiple unconstrained references.
To address this limitation, reference shape predictor, S, as illustrated in Fig. 6. The prediction process is formulated as ,gerd S(T, X
introduce multi-
where gered denotes the predicted shape parameters, are learnable query tokens, and X are DINOv2 fea- tures extracted from the reference images. Our shape pre- dictor S employs a perceiver-style architecture [34, 46] that can use query tokens to effectively aggregate multi- view information. The prediction head is a lightweight transformer, similar to the camera head design in [87].
Shape Parammtiess jiodd
DINO Features X
Shape Hend
Peneriver Blocks
Leurnable Shape
Figure Multi-reference Shape Predictor.
Overall, through the shape predictor, multi-view image normal generator, and mesh carving texture baking steps, YPu generates textured 3D humans from unconstrained photo inputs. See the detailed flowchat in Sup.Mat." 's Appendix D.5.
EXPERIMENTS
4.1 SETTINGS
Dataset. We train our multi-view image generation, normal map generation, and shape prediction models on the THuman2.1 [99], Human4DiT [74], 2K2K [20], and CustomHumans [24] datasets. For evaluation, we use the PuzzlelOI [94] and 4D-Dress [88] datasets as test sets. To further validate our approach, we collect an in-the-wild (in-the-wild) dataset comprising 12 distinct identities. Details on dataset selection and processing procedures are provided in Appendix D.2.
Baselines. We comprehensively compare YPu with 1) album-to-human reconstruction methods, including PuzzleAvatar [94] and AvatarBooth [102]. Since single-view reconstruction is a special case of the unconstrained setting, we also include the leading 2) single-view method, PSHuman [48], in our comparisons. To ensure fair evaluation and isolate the impact of pose estimation errors, we provide ground truth SMPL-X parameters for all baseline methods. 3) For shape prediction, we present the first approach estimate SMPL-X shape parameters from multiple unconstrained inputs. We compare our shape predictor with two single-input methods: Semantify [17], which is specifically designed for shape prediction, and PromptHMR [89], a state-of-the-a human mesh recovery method. Unless stated otherwise, results on PuzzleIOI and 4D-Dress use 12 reference images. 4) For in-the-wild, we use all available references (8-12) for each identity. Additional model and training details are in Mur. 's Appendices D.1 and D.3.
Metrics. For PuzzlelOI and 4D-Dress (with textured 3D GT), we report geometric metrics (Chamfer, P2S, Normal map L2) and image quality metries (PSNR, SSIM, LPIPS). For in-the-wild, we perceptual similarity (CLIP-I, DINO) between generated and frontal reference. Shape prediction is assessed by vertex-to-vertex (V2V) distance on all datasets. More details in Sup.Mar. 's Appendix D.4.
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Inputs
Ours
AvalarBooth
PuzzleAvatar
GT
Higure 8: Qualitative Comparisons on Puzzle1O1 and 4D-Dress. See more 360-degree results in Map.Mat,' video.
4.2 COMPARISONS
Quantitative Results. The quantitative results in Tab. show that UP2You consistently surpasses all baselines across both 2D and 3D evaluation metrics on the PuzzlelOI and 4D-Dress datasets. Importantly, YPu also achieves strong perceptual quality scores the in-the-wild dataset, demonstrating its robustness and effectiveness in real-world unconstrained scenarios.
For single-view reconstruction, Tab. 3 shows that You out- performs PSHuman on all 2D and 3D metrics. This is expected, as single front-view view input special case of the unconstrained multi-view scenario for which UP2You is designed. Training on the more challenging unconstrained task enables our model to generalize well and excel in the simpler constrained setting.
Semuaniity
As shown in Tab. and Fig. 7, our shape predictor outperforms single-view methods [17, 89], achieving more accurate and consistent results. Single-input baselines show high variance and instability, especially with partial input failed detections. Leveraging multiple inputs, our method delivers more robust shape prediction, with performance further improving more unconstrained references are used. Furthermore, Table also shows that the perceiver transformer architecture is better than simple MLPs for the shape predictor.
Promyd
Figure Shape Prediction Error Map-
Semsnityy
PrompiIMR
References
Mean]
Varā
Mean]
Varl
PSNRT
Chamter]
Nonual,
5.819
11.066
5.706
9.661
17445
8004
7.427
PSHumin
Mesh
34.134
0.905
011895
2.79)
2.926
0.0199
0U0096
9.403
8.275
12
11.097
6.507
9.387
19.48
8.336
7.399
Table 3: Comparison of Single-Image based Reconstruction.
Table 2: V2V(.) Comparions of Shape Prediction Reuslts.
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Impots
Ours
AvatarBooth
PuzzloAvatar
Reference
Iopuc
Figure Qualitative Comparisons in-the-wild Data.
Figure 10: UP2You PSHluman.
Qualitative Results. The qualitative comparisons in Fig. 8 and Fig. 9 show that You achieves high-fidelity, ffference-faithfie 3D reconstructions with strong realism and detail preservation. In contrast, baselines like AvatarBooth and PuzzleAvatar often fail to capture fine facial details and produce blurrier, less realistic results with poor subject-specific consistency. Figure 10 shows single- view 3D human reconstruction comparisons. Our method generalizes well single-view inputs, producing visually comparable results to PSHuman, but with more accurate limb reconstruction due consistent multi-view guidance. More visual comparisons and results are Appendices E. and G.
Feature Aggregation
Image Encoder
PuzzicIOl
4D-Dress
Conz.
sum
topk
DINOv2
LPIPS
0.0545
X
17.412
0,864
0.1227
19.614
0.876
0.1098
X
V
X
X
X
X
20.545
0.893
0.0949
23.366
0.901
0.0791
C.
X
x
v
X
X
20.167
0,889
0.1022
23.412
0.904
0.0794
D
X
x
v
x
V
V
X
20.152
0,891
0.0976
23.405
0.903
0.0801
E
X
x
/
x
/
X
19,744
0.886
0.1415
23.393
0.904
0.0813
Table Ablation Studies our orthogonal view image generation model.
4.3 ABLATION STUDIES
Multi-View Image Generation. In Tab. 4, we analyze our multi- view image generation model on the PuzzlelOI and 4D-Dress datasets. For feature aggregation, we compare simple averaging (A), concatenation (B), and our proposed PCFA, which achieves the best results. We also test a weighted sum strategy (C) after correla- evaluate
tion map prediction. For reference feature extraction, CLIP (D), DINOv2 (E), and ReferenceNet. Quantitative and visual results (Appendix F.1) show design outperforms all alternatives.
Figure 11: Predicted Correlation Maps See dynamic illustration of correlation maps Sup, video.
Correlation Maps. Our correlation map prediction module identifies and prioritizes key regions in reference images based on the target pose. As shown in Figure 11, visualizations for front- and back-view targets confirm that our maps effectively select the most relevant areas for view generation. This targeted focus improves generation quality and reduces GPU memory usage by retaining only the most informative features. More visual results are shown in Appendix E.2.
Number of References. In Tab. quality improves as more uncon- strained references are used. PCFA module efficiently selects informative features, keeping GPU memory us- age low, unlike direct concatenation, which increases memory linearly.
Ours
Concar
PSNR
SSIMI
LPIPS
GPUL
PSNR
SSIM1
LPIPS]
GPUL
r
24.159
0.912
0.0680
18.65
22.759
0.897
0.0894
18.02
0.901
24.33
refs
25.646
0.918
0.0592
20.16
23.362
0.901
0.0796
30.89
refs
25.848
0.920
0.0576
20.88
23.366
0.901
0.0791
37.96
Table S: Mulli-Viees Generation with Different Number of References.
Robustness to Inputs & Conditions. The generated human identity remains consistent across different target poses and reference combinations, with detailed discussions and results presented in Sap.Mar.' 's Appendix F.2 and Appendix F.3. Notably, You effectively handle subjects with loose clothing and complex target poses, as demonstrated in Fig. 24 of Appendix F.2.
Image Encoder of PCFA. Given that DINOv2 has been demonstrated to effectively capture 2D-to-3D correspondences [58], we adopt it as the image encoder for our PCFA module. To further validate this design choice, conduct additional experiments the 4D-Dress dataset using alternative

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Published as a conference paper at ICLR 2026
Inputs
Resalts
Inputs
Results
Figure 12: Generation Results on Highly Occluded Inputs.
Figure 13: Generation Results on Missing Part.
image encoders, including CLIP [68] and DINOv1 [8]. As presented in Tab. 6, DINOv2 consistently outperforms both alternatives on multi-view image generation quality.
PSNR1
SSIM
[.PIPS]
PSNRT
SSIMI
LPIPS,
GPU,
DINO<2
25,848
0,920
0.0576
24.978
CLIP
23.876
0.904
0.0767
25.848
0.920
0.0576
20.88
DINOv1
24.170
0.907
0.0745
3.0
25.837
0.920
0.0569
23.12
Table 6: Comparison of Different Image Encoders for PCFA. Table 7: Comparison of Different Number of Selected Features.
Number of Selected Features. We set the default value of y to 2.0 to control the number of reference features selected in the topk selection. To determine the optimal configuration, we evaluate different values of y shown in Table 7. The results demonstrate that y 2.0 achieves the best trade-off' between generation quality and GPU memory efficiency.
Unconstrained Inputs vs. Sin- gle Front View. Compared to single full-body front-view in-
PSNR
SSIM1
Chamfer,
P2S
Normal
Front
Back
Front
Back
Pront
Back
25.257
25.483
0.906
0.0724
0.0733
1.119
0.0122
puts, unconstrained photos are casier collect and capture richer information about side and back views. Using the compre- hensive information from uncon- strained photos leads to better re- construction results. Table 8 compares YP2You against standard single front-view based methods on 4D-Dress dataset, including ICON [92], ECON [93], PIFuHD [72], PSHuman [48], and Hu- man3Diff [96]. Our method achieves the best performance in both rendering quality and 3D accuracy, particularly for back-view rendering results, demonstrating the value of unconstrained inputs.
PSHumam
25,384
23.382
0 0.898
0.885
0.0934
0,1121
2.756
2.926
0.0189
Human3Diff
23.335
20720
0.853
0.872
0,1118
0.1248
4.275
4,322
0.0227
ICON
4.352
4.331
0.0388
ECON
3.780
3.642
0.0178
PIFaHD
0.154
8: Unconstrained Photos Single Front View. indicates our method uses
unconstrained photos inpur, while other methods use single full-body front view input.
Generated Results in Extreme Situation. UP2You is robust to input variations and can effectively extract information from highly occluded photos. Figure 12 presents an example where one input image captures only the foot region, while other images lack this body part, demonstrating the capability of our method to handle inputs with high occlusion ratios. Figure 13 further examines scenarios where body parts are not fully visible across all images (e.g. the foot region). Due to diffusion hallucination, the generated results exhibit a somewhat reasonable structure; however, the texture blended from other visible parts (more cases shown in Sup.Mat." 's Fig. 29). Therefore, inputs with complete body part coverage are more suitable for You to achieve optimal results.
Animation. Since we adopt the A-Pose as the default target pose, the reconstructed mesh is naturally suited for animation. The textured mesh generated by YPu can be easily animated using third- party tools such as Mixamo [6]. Moreover, the aligned SMPL-X parameters provided by You enable animation based on skin weight transfer [1]. Finally, as UP2You can transform unconstrained inputs into different target pose configurations, animated rendering results can also be directly performed by itself, as demonstrated in Mat. 's Fig. 21.
5 CONCLUSION
UP2You acts as a "data rectifier," converting unconstrained photos into orthogonal views suit- able for MVS. It is efficient (1.5 minutes per per- son on one GPU), achieves SOTA quality, and well preserves identity and clothing style across diverse input forms and pose conditions. also enables free 3D virtual try-on (Fig. 14, more in Mut. 's Fig. 30). Limitations and future work discussed in Mat.' Appendix H.
Figure 14: 3D Virtual Try-On.
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Published as a conference paper at ICLR 2026
6 ACKNOWLEDGEMENTS
We thank Siyuan Yu for the help in Houdini Simulation, Shunsuke Saito, Dianbing Xi, Yifei Zeng for the fruitful discussions, and the members of Endless Al Lab for their help data capture and discussions. This work is funded by the Research Center for Industries the Future (RCIF) at Westlake University, the Westlake Education Foundation.
REFERENCES
[1] Rinat Abdrashitov, Kim Raichstat, Jared Monsen, and David Hill. Robust skin weights transfer via weight inpainting. SIGGRAPH Asia 2023 Technical Communications, 2023. 10, 26
[2] Yuval Alaluf, Elad Richardson, Gal Metzer, and Daniel Cohen-Or. neural space-time time representation for text-to-image personalization. Transactions on Graphics (TOG), 2023. 4, 19
[3] Badour AlBahar, Shunsuke Saito, Hung- Yu Tseng, Changil Kim, Johannes Kopf, and Jia-Bin Huang. Single-image 3d human digitization with shape- Conference on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia),
shape-guided guided diffusion. In International
2023. 3
[4] Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, and Dani Lischinski. Break- a-scene: Extracting multiple concepts from a single image. In International Conference on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia), 2023. 4, 19
[5] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang. Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308. 2023. 19
[6] Sue Blackman. Rigging with mixamo. In Unity for Absolute Beginners. Springer, 2014. 10, 26
[7] Zeyu Cai, Duotun Wang. Yixun Liang, Zhijing Shao, Ying-Cong Chen, Xiaohang Zhan, and Zeyu Wang. Dreammapping: High-fidelity text-to-3d generation via variational distribution mapping. In Pacific Conference on Computer Graphics and Applications (PG), 2024. 4
[8] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Interna- tional Conference on Computer Vision (ICCV), 2021. 10
[9] Bowei Chen, Brian Curless, Ira Kemelmacher- Saaillmaahhr-SSlizeeannn and Steven M Seitz. Total selfie: generating full-body selfies. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 4, 19
[10] Bardienus Pieter Duisterhof, Lojze Zust, Philippe Weinzaepfel, Vincent Leroy, Yohann Cabon, and Jerome Revaud. Mast3r-sfm: a fully-integrated solution for unconstrained structure-from- motion. International Conference 3D Vision (3DV), 2025. 4
[11] Facebook.
BINOv2-Large dinov2- - 2023. 20
[12] Yao Feng, Vasileios Choutas, Timo Bolkart, Dimitrios Tzionas, and Michael J Black. Collabo- rative regression of expressive bodies using moderation. In International Conference on 3D Vision (3DV), 2021. 4
[13] Yarden Frenkel, Yael Vinker, Ariel Shamir, and Daniel Cohen-Or. Implicit style-content separation using b-lora. In European Conference on Computer Vision (ECCV), 2024. 4, 19
[14] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 2022. 4, 19
[15] Xiangjun Gao, Xiaoyu Li, Chaopeng Zhang, Qi Zhang, Yanpei Cao. Ying Shan, and Long Quan. Contex-humanl Free-view rendering of human from single image with texture- consistent synthesis. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
11

---

Published as a conference paper at ICLR 2026
[16] Daniel Garibi, Shahar Yadin, Roni Paiss, Omer Tov, Shiran Zada, Ariel Ephrat, Tomer Michaeli, Inbar Mosseri, and Tali Dekel. Tokenverse: Versatile multi-concept personalization in token modulation space. Transactions Graphics (TOG), 2025. 4, 19
[17] Omer Grainik, Guy Gafni, and Ariel Shamir. Semantify: Simplifying the control of 3d morphable models using clip. In International Conference on Computer Vision (ICCV), 2023.
7.
[18] Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. Vid2avatar: 3d avatar reconstruction from videos in the wild via self-supervised scene decomposition, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 1,
[19] Chen Guo, Junxuan Li, Yash Kant, Yaser Sheikh, Shunsuke Saito, and Chen Cao. Vid2avatar- pro: Authentic avatar from videos the wild via universal prior. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025.
[20] Sang-Hun Han, Min-Gyu Park, Ju Hong Yoon, Ju-Mi Kang, Young-Jae Park, and Hae-Gon Jeon. High-fidelity 3d human digitization from single 2k resolution images. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 20
[21] Xinyang Han, Zelin Gao, Angjoo Kanazawa, Shubham Goel, and Yossi Gandelsman. The more you see in 2d the more you perceive in 3d. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
[22] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2016.
20
[23] Xu He, Zhiyong Wu, Xiaoyu Li, Di Kang, Chaopeng Zhang, Jiangnan Ye, Liyang Chen, Xiangjun Gao. Han Zhang, and Haolin Zhuang. Magicman: Generative novel view synthesis of humans with 3d-aware diffusion and iterative refinement. In AAAI Conference on Artificial Intelligence, 2025. 1, 3.7
[24] Hsuan-I Ho, Lixin Xue, Jie Song, and Otmar Hilliges. Learning locally editable virtual humans. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 7,
[25] Jonathan Ho, Ajay Jain, and Picter Abbeel. Denoising diffusion probabilistic models. In Conference on Neural Information Processing Systems (NeurIPS), 2020. 3, 19
[26] Fa-Ting Hong, Zhan Xu, Haiyang Liu, Qinjie Lin, Luchuan Song, Zhixin Shu, Yang Zhou, Duygu Ceylan, and Dan Xu. Free-viewpoint human animation with pose-correlated reference selection. In Proc. IEEE Conf. on Computer Vision and Pullern Recognition (CVPR), 2025. 5, 6
[27] Li Hu, Xin Gao, Peng Zhang, Ke Sun, Bang Zhang, and Liefeng Bo. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proc. IEEE
Conf. on Computer Vision and Puttern Recognition (CVPR), 2024. 2, 5, 19
[28] Liangxiao Hu, Hongwen Zhang, Yuxiang Zhang, Boyao Zhou, Boning Liu, Shengping Zhang, and Liqiang Nie. Gaussianavatar: Towards realistic human avatar modeling from single video via animatable 3d gaussians. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 1,3
[29] Han Huang, Liliang Chen, and Xihao Wang. Unconfuse: avatar reconstruction from uncon- strained images. European Conference on Computer Vision (ECCV), 2022. 3
[30] Xin Huang, Ruizhi Shao, Qi Zhang, Hongwen Zhang, Ying Feng, Yebin Liu, and Qing Wang. Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
[31] Yangyi Huang, Hongwei Yi, Yuliang Xiu, Tingting Liao, Jiaxiang Tang, Deng Cai, and Justus Thies. Tech: Text-guided reconstruction of lifelike clothed humans. In International Conference on 3D Vision (3DV), 2024. 3
12

---

Published as a conference paper at ICLR 2026
[32] Zehuan Huang, Yuan-Chen Guo. Haoran Wang, Ran Yi, Lizhuang Ma, Yan-Pei Cao, and Lu Sheng. Mv-adapter: Milwi-view consistent image generation made easy. In International Conference on Computer Vision (ICCV), 2025. 2, 4. 19, 20
[33] Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, and Tony Tung. Arch: Animatable reconstruction of clothed humans. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. 3
[34] Andrew Jaegle, Felix Gimeno, Andy Brock. Oriol Vinyals, Andrew Zisserman, and Joao Carreira. Perceiver: General perception with iterative attention. In International Conference Machine Learning (ICML.) 2021. 2,
[35] Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. Instantavatar: Learning avatars from monocular video in 60 seconds. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 1,3
[36] Yudong Jin, Sida Peng, Xuan Wang, Tao Xie, Zhen Xu, Yifan Yang, Yujum Shen, Hujun Bao, and Xiaowei Zhou. Diffuman4d: 4d consistent human view synthesis from sparse-view videos with spatio-temporal diffusion models. arXiv preprint arXiv:2507. 13344, 2025.
[37] Oren Katzir, Or Patashnik, Daniel Cohen-Ori -r, and Dani Lischinski. Noise-free score distillation. In International Conference Learning Representations (ICLR), 2024. 4
[38] Byungjun Kim, Patrick Kwon, Kwangho Lee, Myunggi Lee, Sookwan Han, Daesik Kim, and Hanbyul Joo. Chupa: Carving 3d clothed humans from skinned shape priors using 2d diffusion probabilistic models. International Conference on Computer Vision (ICCV), 2023.
[39] Xianghao Kong, Qiaosong Qi, Yuanbin Wang. Anyi Rao, Biaolong Chen, Aixi Zhang, Si Liu, and Hao Jiang. Profashion: images. arXiv preprint arXiv:2505.065337 2025. 5,
fashion video generation with multiple reference
[40] Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun- Zhu. Multi- concept customization of text-to-image diffusion. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. 4, 19
[41] Nupur Kumari, Xi Yin, Jun-Yan Zhu, Ishan Misra, and Samanch Azadi. Generating multi- image synthetic data for text-to-image customization. arXiv preprint arxiv:2502.01720, 2025. 4, 19
[42] Black Forest Labs. Flux. htt.
wwtp://w..ooooooaao.o....aaac.. forest waa-laaaasalaaa 2024.
3, 19
[43] Boqian Li, Haiwen Feng, Zeyu Cai, Michael J Black, and Yuliang Xiu. Etch: Generalizing body fitting to clothed humans via equivariant tightness. In International Conference Computer Vision (ICCV), 2025. 4
[44] Boqian Li, Xuan Li, Ying Jiang, Tianyi Xie, Feng Gao, Huamin Wang. Yin Yang, and Chenfanfu Jiang. Garmentdneamer: 3dgs guided garment synthesis with diverse geometry and texture details. In International Conference 3D Vision (3DV), 2025. 3
[45] Jiefeng Li, Chao Xu, Zhicun Chen, Siyuan Bian, Lixin Yang, and Cewu Lu. Hybrik: A hybrid analytical-neura inverse kinematics solution for 3d human pose and shape estimation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021. 4
[46] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models. In International Conference on Machine Learning (ICML), 2023. 2,
[47] Peng Li, Yuan Liu, Xiaoxiao Long, Feihu Zhang, Cheng Lin, Mengfei Li, Xingqun Qi, Shanghang Zhang, Wei Xue, Wenhan Luo, et al. Era3d: High-resolution multiview diffusion using efficient row-wise attention. Conference on Neural Information Processing Systems (NeurIPS), 2024. 19
13

---

Published as a conference paper at ICLR 2026
[48] Peng Li, Wangguandong Zheng, Yuan Liu, Tao Yu, Yangguang Li, Xingqun Qi, Xiaowei Chi, Siyu Xia, Yan-Pei Cao, Wei Xue, et al. Pshuman: Photorealistic single-image 3d human reconstruction using cross-scale multiview diffusion and explicit remeshing. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025. 1, 2, 10, 19
[49] Yihui Li, Chengxin Lv, Hongyu Yang, and Di Huang. Micro-macro wavelet-based gaussian splatting for 3d reconstruction from unconstrained images. In AAAI Conference Artificial Intelligence, 2025. 4
[50] Tingting Liao, Hongwei Yi, Yuliang Xiu, Jiaxiang Tang, Yangyi Huang, Justus Thies, and Michael J Black. Tada! text to animatable digital avatars. In International Conference on 3D Vision (3DV), 2024.
[51] Tingting Liao, Yujian Zheng, Yuliang Xiu, Adilbek Karmanov, Liwen Hu, Leyang Jin, and Hao Li. SOAP: Style-omniscient animatable portraits. In International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), 2025.
[52] Lixiang Lin, Songyou Peng. Qijun Gan, and Jianke Zhu. Fasthuman: Reconstructing high- quality clothed human in minutes. In International Conference on 3D Vision (3DV), 2024.
[53] Xian Liu, Xiaohang Zhan, Jiaxiang Tang, Ying Shan, Gang Zeng, Dahua Lin, Xihui Liu, and Ziwei Liu. Humangaussian: Text-driven 3d human generation with gaussian splatting. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 3
[54] Xiaoxiao Long, Yuan-Cher Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Saig-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image 3d using cross-domain diffusion. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 6
[55] Jia Lu, Taoran Yi, Jiemin Fang, Chen Yang, Chuiyun Wu, Wei Shen, Wenyu Liu, Qi Tian, and Xinggang Wang. Snap-snap: Taking two images to reconstruct 3d human gaussians in milliseconds. arXiv preprint arXiv:2508. 14892, 2025. 1,
[56] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020. 4
[57] 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. Transactions on Machine Learning Research Journal (TMLR), 2024.
[58] Evin Pınar Örnkk, Yann Labbé, Bugra Tekin, Lingni Ma, Cem Keskin, Christian Forster, and Tomas Hodan. Foundpose: Unseen object pose estimation with foundation features. In European Conference on Computer Vision (ECCV), 2024.
[59] Zhuoyang Pan, Angjoo Kanazawa, and Hang Gao. SOAR: Self-occluded avatar recovery from a single video in the wild. arXiv preprint arXiv:2410.23800, 2024. 3
[60] Hao Yang Peng, Jia-Peng Zhang, Meng-Hao Guo, Yan-Pei Cao, and Shi-Min Hu. Charactergen: Efficient 3d character generation from single images with multi-view pose canonicalization. Transactions on Graphics (TOG), 2024.7 2
[61] Sida Peng. Chen Geng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Xiaowei Zhou, and Hujun Bao. Implicit neural representations with structured latent codes for human body modeling. Transactions on Puttern Analysis and Machine Intelligence (TPAMI), 2023.
[62] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In International Conference on Learning Representations (ICLR), 2023. 2,
14

---

Published as a conference paper at ICLR 2026
[63] Guocheng Qian, Kuan-Chich Wang, Or Patashnik, Negin Heravi, Daniil Ostashev, Sergey Tulyakov, Daniel Cohen-Or, and Kfir Aberman. Omni-id: Holistic identity representation designed for generative tasks. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025. 4, 19
[64] Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
[65] Lingteng Qiu, Xiaodong Gu, Peihao Li, Qi Zuo, Weichao Shen, Junfei Zhang, Kejie Qiu, Weihao Yuan, Guanying Chen, Zilong Dong, et al. Lhm: Large animatable human reconstruc- tion model from single image in seconds, In International Conference Computer Vision (ICCV), 2025. 1,
[66] Lingteng Qiu, Peihao Li, Qi Zuo, Xiaodong Gu, Yuan Dong, Weihao Yuan, Siyu Zhu, Xi- aoguang Han, Guanying Chen, and Zilong Dong. Pf-lhm: 3d animatable avatar reconstruction from pose-free free articulated human images. arXiv preprint arXiv:2506. 13766, 2025.
[67] Lingteng Qiu, Shenhao Zhu, Qi Zuo, Xiaodong Gu, Yuan Dong, Junfei Zhang, Chao Xu, Zhe Li, Weihao Yuan, Liefeng Bo, et al. Anigs: Animatable gaussian avatar from a single image with inconsistent gaussian reconstruction. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025.
[68] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference Machine Learning (ICML), 2021. 10
[69] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 3, 19
[70] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aber- man. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. 2, 4, 19
[71] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In International Conference Computer Vision (ICCV), 2019. 3
[72] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel- aligned implicit function for high-resolution 3d human digitization. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. 3, 10
[73] Viraj Shah, Nataniel Ruiz, Forrester Cole, Erika Lu, Svetlana Lazebnik, Yuanzhen Li, and Varun Jampani. Ziplora: Any subject in any style by effectively merging loras. In European Conference on Computer Vision (ECCV), 2024. 4, 19
[74] Ruizhi Shao, Youxin Pang, Zerong Zheng, Jingxiang Sun, and Yebin Liu. 360-degree human video generation with 4d diffusion transformer. Transactions on Graphics (TOG), 2024. 7, 20
[75] Fei Shen and Jinhui Tang. Imagpose: A unified conditional framework for pose-guided person generation. Conference on Neural Information Processing Systems (NeurIPS), 2024. 4, 19
[76] Tianchang Shen, Jun Gao, Kangxue Yin, Ming- Yu Liu, and Sanja Fidler. Deep marching tetrahedra: hybrid representation for high-resolution 3d shape synthesis. Conference Neural Information Processing Systems (NeurIPS), 2021.
[77] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zerol23++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15 /5/ 2023. 19
15

---

Published as a conference paper at ICLR 2026
[78] Yichun Shi, Peng Wang. Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi- view diffusion for 3d generation. In International Conference on Learning Representations (ICLR), 2024. 19
[79] Yukai Shi, Jianan Wang, Boshi Tang, Xianbiao Qi, Tianyu Yang. Yukun Huang, Shilong Liu, Lei Zhang, Heung- Yeung Shum, et al. Toss: High-quality text-guided novel view synthesis from single image. In International Conference on Learning Representations (ICLR), 2024.
19
[80] Noah Snavely, Steven M Seitz, and Richard Szeliski. Photo tourism: Exploring photo collec- tions in 3d. In International Conference on Computer Graphics and Interactive Techniques (SIGGRAPiT), 2006. 4
[81] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations (ICLR), 2021. 3, 19
[82] Stabilityai. Stable-Diffusionn2--BBBss https: httss///hggginngggggeee co/ wta.ssaaalltti
st stable-di Ffusion 2- bbase,, 2023. 20
[83] Jiapeng Tang, Davide Davoli, Tobias Kirschstein, Liam Schoneveld, and Matthias Niessner. Gaf: Gaussian avatar reconstruction from monocular videos via multi-view diffusion. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025. 3
[84] Luming Tang, Nataniel Ruiz, Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, et al. Realfill: Reference- driven generation for authentic image completion. Transactions on Graphics (TOG), 2024. 2,
4, 19
[85] Andrey Voynov, Qinghao Chu, Daniel Cohen-Or, and Kfir Aberman. p+: Extended textual conditioning in text-to-image generation. arXiv preprint arXiv:2303.0995 2023, 4, 19
[86] Duotun Wang. Hengyu Meng, Zeyu Cai, Zhijing Shao, Qianxi Liu, Lin Wang, Mingming Fan, Xiaohang Zhan, and Zeyu Wang. Headevolver: Text head avatars via expressive and attribute-peeserving mesh deformation. In International Conference on 3D Vision (3DV), 2025. 3
[87] Jianyuan Wang. Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025. 4,7
[88] Wenbo Wang, Hsuan-I Ho, Chen Guo, Boxiang Rong, Artur Grigorev, Jie Song, Juan Jose Zarate, and Otmar Hilliges. 4d-dress: A 4d dataset of real-world human clothing with semantic annotations. In Proc. IEEE Conf. on Computer Vision and Puttern Recognition (CVPR), 2024.
7, 20
[89] Yufu Wang, Yu Sun, Priyanka Patel, Kostas Danilidiis, Michael Black, and Muhammed Kocabas. Prompthmr: Promptable human mesh recovery. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2025. 4, 8
[90] Chung-Y Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher- Shlizerman. Humannerf: Free-viewpoint rendering of moving people from monocular video. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022.
[91] Kailu Wu, Fangfu Liu, Zhihan Cai, Runjie Yan, Hanyang Wang, Yating Hu, Yueqi Duan, and Kaisheng Ma. Unique3d: High-quality and efficient 3d mesh generation from a single image. Conference on Neural Information Processing Systems (NeurIPS), 2024. 6
[92] Yuliang Xiu, Jinlong Yang. Dimitrios Tzionas, and Michael J Black. Icon: Implicit clothed hu- mans obtained from normals. In Proc. IEEE Conf. on Computer Vision and Puttern Recognition (CVPR), 2022. 3, 10
[93] Yuliang Xiu, Jinlong Yang, Xu Cao, Dimitrios Tzionas, and Michael J Black. Econ: Explicit clothed humans optimized via normal integration. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 1, 1, 6, 7, 10
16

---

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[94] Yuliang Xiu, Yufei Ye, Zhen Liu, Dimitrios Tzionas, and Michael J Black. Puzzleavatar: Assembling 3d avatars from personal albums. Transactions on Graphics (TOG), 2024. 2, 3, 4,
7, 20
[95] Yifang Xu, Benxiang Zhai, Yunzhuo Sun, Ming Li, Yang Li, and Sidan Du. Hifi-portrait: zero-shot identity-preseredd portrait generation with high-fidelity multi-face fusion. In Proc. IEEE Conf. on Computer Vision and Puttern Recognition (CVPR), 2025. 4, 19
[96] Yuxuan Xue, Xianghui Xie, Riccardo Marin, and Gerard Pons-Moll, Human-3diffusionn Realistic avatar creation via explicit 3d consistent diffusion models. Conference on Neural Information Processing Systems (NeurIPS), 2024. 10
[97] Xihe Yang, Xingyu Chen, Daiheng Gao, Shaohui Wang, Xiaoguang Han, and Baoyuan Wang. Have-fun: Human avatar reconstruction from few-shot unconstrained images. In Pruc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 3
[98] Wanqi Yin, Zhongang Cai, Ruisi Wang, Ailing Zeng, Chen Wei, Qingping Sun, Haiyi Mei, Yanjun Wang, Hui En Pang, Mingyuan Zhang, et al. Smplest-x: Ultimate scaling for expressive human pose and shape estimation. arXiv preprint arXiv:2501.099
2025. 4
[99] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proc. IEEE
Conf. on Computer Vision and Pattern Recognition (CVPR), 2021. 7, 20
[100] Xin Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang. Song-Hal Hai Zhang, and Xiaojuan Qi. Text- to-3d with classifier score distillation. In International Conference on Learning Representations (ICLR), 2024. 4
[101] Zhiyuan Yu, Zhe Li, Hujun Bao, Can Yang, and Xiaowei Zhou. Humanram: Ferd-forward human reconstruction and animation model using transformers. In International Conference on Computer Graphics and Interactive Techniques (SIGGRAPII), 2025. 1,3
[102] Yifei Zeng, Yuanxun Lu, Xinya Ji, Yao Yao, Hao Zhu, and Xun Cao. Avatarbooth: High- quality and customizable 3d human avatar generation. arXiv preprint arXiv:2306009864 2023.
2, 3, 7,7
[103] Yu Zeng, Vishal M Patel, Haochen Wang, Xun Huang, Ting-Chun Wang. Ming- Yu Liu, and Yogesh Balaji. Jedi: Joint-image diffusion models for finetuning-fret personalized text-to- image generation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 4, 19
[104] Hongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, and Zhenan Sun. Pymaf: 3d human pose and shape regression with pyramidal mesh alignment feedback loop. In International Conference on Computer Vision (ICCV), 2021. 4
[105] Jingbo Zhang, Xiaoyu Li, Qi Zhang, Yanpei Cao, Ying Shan, and Jing Liao. Humanref: Single image to 3d human generation via reference-guinn diffusion. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
[106] Peng Zheng, Dehong Gao, Deng-Ping Fan, Li Liu, Jorma Laaksonen, Wanli Ouyang, and Nicu Sebe. Bilateral reference for high-resolution dichotomous image segmentation. CAAI Artificial Intelligence Research, 2024.7 21
[107) Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. Pamir: Parametric codel-condiiioned im- plicit representation for image-based human reconstruction. Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. 3
[108] Xiangyu Zhu, Tingting Liao, Xiaomei Zhang, Jiangjing Lyu, Zhiwen Chen, Yunfeng Wang, Kan Guo, Qiong Cao. Stan Z Li, and Zhen Lei. MVP-Human Dataset for 3-D clothed human avatar reconstruction from multiple frames. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2023. 3
[109] Zhuofan Zong. Dongzhi Jiang, Bingqi Ma, Guanglu Song, Hao Shao, Dazhong Shen, Yu Liu, and Hongsheng Li. Easyref: Omni- mani-generalized group image reference for diffusion models via multimodal Ilm. In International Conference on Machine Learning (ICML), 2024. 4, 19
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Appendix
Table of Contents
A Use of Large Language Models
19
B Related Work
19
B.1 Subject-driven and ID-consistent Image Generation
19
C Priliminary
19
D Implementation Details
20
D.1 Model Structure
20
D.2
Dataset
20
D.3
Training Details
................................
20
D.4
Evaluation Metries
................................
20
D.5
Inference Process
21
E
Additional Visual Comparisons
22
E.1
Qualitative Comparisons
22
E.2
Correlation Maps
25
E.3
Animation Results
26
F
Additional Ablation Studies
27
F.1
Visual Results of Different Orthogonal Images Generation Designs
27
F.2
Robustness Target Pose Condition.
28
F3
Analysis Shape Predictor.
30
F4
Visual Results with Different Number Inputs
More Generation Results of UP2You
32
Limitations and Future Works
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A USE OF LARGE LANGUAGE MODELS
We used a large language model to assist with copy editing grammar checking, wording suggestions, and minor style and clarity improvements after the scientific content, methodology, analyses, and conclusions had been written by the authors.
B RELATED WORK
B.1 SUBJECT-DRIVEN AND ID-COSSISTENT IMAGE GENERATION
With the advent of powerful generative models [25, 42, 69, 81], subject-driven image generation has made remarkable progress in recent years. Various approaches have been proposed to generate images of specific subjects, such optimizing specialized tokens encode subject concepts [2, 14, 85), learning personalized modulation vectors for each concept [16], or fine-tuning pre-trained diffusion models [4, 13, 40, 70, 73] using handful of reference images. Additionally, methods like JeDi [103] and SynCD [41] utilize global self-attention mechanisms to effectively fuse information from multiple images of target subject, while EasyRef [109] leverages Vision Aision-Language Models (VLMs) [5].
For human-centric generation, several methods have been developed to handle identity preservation. For instance, Omni-ID [63], IMAGPose [75], and HiFi-Portrait [95] utilize specialized image encoders to process multiple reference images for IDD -D-preserving image synthesis. However, extending these techniques the full body is non-trivial, as the human body's s highly articulated structure and non- rigid deformations introduce significant challenges for feature fusion. To tackle this, approaches like Total Selfie [9]. and RealFill [84] employ few-shot personalization via fine-tuning [70] to capture consistent identities, including both facial features and overall appearance. Nevertheless, these methods are tailored for 2D image generation and lack the mechanisms needed to ensure cross-view consistency the precise latent feature aggregation required for high-fidelity 3D reconstruction.
C PRILIMINARY
We review the fundamentals of multi-view diffusion models [47, 77-79], with a particular focus on MV-Adapter [32], which serves the foundation for the multi-view generation of YouY
Multi- Diffusion Models. Multi-view diffusion models extend single-view generation by introducing multi-view attention mechanisms, enabling the synthesis of images that consistent across different viewpoints. Several works [78, 79] generalize the self-attention mechanism of standard diffusion models to operate over all pixels from multiple views. Specifically, given f the input to the attention block, multi-view self-attention concatenates features from M views, allowing the model capture global dependencies. However, this approach incurs significant computational overhead due to the need to process all pixels across all views. To mitigate this, row-wise self- attention [47, 48] leverages geometric correspondences between orthogonal views. For example, Era3D [47] restricts attention to the current view and corresponding rows from other views, which is well-suited for orthogonal multi-view generation and substantially reduces computational cost.
Building on row-wise self-attention, MV-Adapter [32] introduces an moull-to-mulliview (I2MV) generator with a parallel attention architecture. The original self-attention block is modified as:
gself Se fAttn( MVAttn( + RefAttn( + fin
(5)
Here, MVAttn represents the row-wise self-attention mechanism, while fetttn is a cross- attention module that integrates the reference image feature F into fin The feature is extracted from the input image I using the reference network R. [27]: F RR(T). The 12MV generation process in MV-Adapter is formulated as V D(F, P(P)), where = {V1, V VM) denotes the set of generated multi-view images, D represents the multi-view diffusion model, P (P P2....... P specifies the target viewpoint conditions, and P is the condition encoder that fuses viewpoint con- ditions into D. In MV-Adapter, only MVAttn, Re Rnt and P. are trained for I2MV generation. Each is encoded as camera ray representation, referred to as a "raymap". Typically, M = 6 orthog- onal views generated, corresponding the target view angles {0', 45"
40% 90" 135", 180 270%).
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Given the efficient plug-and and-play adapter training mechanism of MV-Adapter combined with the robust feature extraction capabilities of ReferenceNet for processing unconstrained photographs, we adopt MV-Adapter
multi-view diffusion model architecture. Furthermore, considering focus on human-centric tasks, we utilize SMPL-- normal rendering as the viewpoint condition P.
D IMPLEMENTATION DETAILS
D.1 MODEL STRUCTURE
We adopt the framework architecture of MV-Adapter [32] with the stable-di dafffusion-22 2-nn------asee version [82] the foundation for both multi-view im- age and normal generation. The number of selected reference features y is set to 2.0 during both training and inference phases. We employ the DINOv2-Large [11] variant of the DINOv2 encoder gdd For the pose image encoder gıe,, we implement a lightweight ResNet [22] architecture. The learnable shape tokens T E IR 102001021 configured align with the dimensions of gree, and the perceiver blocks S comprise 6 layers cross-attention.
D.2 DATASET
We train our view image generation, normal map generation, and shape prediction models using the THuman2.1 [99], Human4DiT' [74], 2K2K [20], and CustomHumans [24] datasets. Since our task requires handling scenarios where individuals with the same identity appear in different poses, we manually filter the data and group samples by identity. The final training dataset com- prises 6,921 scans spanning 2,091 distinct identities, For each scan, we render 6 orthogonal views ((0", 45% 90% 135% 180% 270")) of both images and normal maps, along with the corresponding SMPL-X normal rendering. Additionally, we render 8 views of each scan using randomly selected perspective cameras to provide "unconstrained photos". During orthogonal image generation training. for each case, randomly select 3 to 8 reference images from other cases sharing the same identity,
For evaluation, we select identities from Puzzle1O1 [94] and additionally choose "A-pose" con- figurations from all 68 identities in 4D-Dress [88], while utilizing the remaining poses as reference views. To ensure that SMPL-X camera normal rendering accurately represents viewpoint information, we rotate all scans so that the front view corresponds to zero azimuth. Beyond synthetic data, we also collect an in-the-wild dataset comprising 12 identities for further evaluation, ensuring robust evaluation in diverse scenarios.
D.3 TRAINING DETAILS
Caormal We train the image and normal generation models end-to-end using denoising losses L. and L respectively. During training, - jointly optimizes the components p TT, Wg. Wk. AvgPool, ptth and Deth In normal maps generation training. Ciarmall optimizes proomaa and normal For shape prediction, we employ the loss function L, = ( T( to compute the vertex-wise distance between SMPL-X meshes generated from the predicted shape parameters gred and the ground-truth shape parameters 8
The complete training process for the image and normal generation models requires approximately 3 and 2 days, respectively, on NVIDIA 5880 GPUs We employ a batch size of per GPU under bfloat16 mixed precision and train for 50,000 iterations. All pose, input, and output image resolutions are consistently set to 768 x 768. The reference images for both image and normal generation are also configured at 768 768 resolution, while the target orthogonal view angles follow the same configuration as MV-Adapter. The shape prediction model undergoes training for 100,000 iterations on NVIDIA 5880 GPUs with batch size of 8 per GPU, requiring approximately 10 hours. We apply a constant learning rate of x 10 with warm-up for training all models.
D.4 EVALUATION METRICS
We employ three complementary metrics to assess geometric accuracy: (1) Chamfer distance (bidirectional point-to-surface distance in em), which measures overall geometric similarity; (2) P2S
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distance (unidirectional point-ttooootttttooo point-to-surface distance in cm), which captures reconstruction completeness; and (3) L2 error for Normal maps rendered from four canonical views ({0", ((0,,00, 180%, 2200)) }),
which evaluates fine-graines surface detail preservation.
We render multi-view color images from the same four canonical viewpoints and evaluate appearance fidelity using three established image quality metrics: PSNR (Peak Signal-to-Noiss Ratio) for pixel-level accuracy, SSIM (Structural Similarity) for structural consistency, and LPIPS (Learned Perceptual Image Patch Similarity) for perceptual similarity.
For the in-the-wild dataset, which lacks 3D ground truth, we assess reconstruction quality using perceptual similarity metrics CLIP-I and DINO computed between the generated front view and the captured reference front view image with A-pose.
We further evaluate shape prediction accuracy by computing vertex-to-vertex (V2V) distances between predicted and ground truth SMPL-X meshes under canonical T-pose (zero pose and expression).
Unconeminee
Mosh Reconstruction
Torget Teatured Mesh
Photos
Shape Prediction (Sec 3.3)
Roadering
images
Maps Generation (Sec 3.2
Generated Normals
Generation (Sec
Images V
Figure 15: Inference Process of UP2You. Given only unconstrained photos I as inputs, You generate high-quality textured mesh.
D.5 INFERENCE PROCESS
The inference process YPuYou for unconstrained photo inputs is illustrated in Fig. 15, which mainly consists of four steps follows:
(1) Use S estimate SMPL-X shape parameters grood from I, and initialize the SMPL-X mesh with gered and a predefined pose (e.g., A-pose with zero expression) obtain the pose condition P.
(2) Generate multi-view images using Desb conditioned on and P.
(3) Generate multi-view normal maps N using Dromaal conditioned on V and P.
(4) Reconstruct the textured mesh using the initialized SMPL-X mesh V, and N.
For data pre- and post-process post-processing, we employ [106] remove backgrounds from input unconstrained photos. Additionally, the reference masks are resized and adapted to the correlation maps C to enhance the model's s focus on foreground regions.
Inference Time. The complete pipeline requires approximately 1.5 minutes generate textured mesh from single unconstrained input. Specifically, the shape prediction step takes about second, multi-view image generation requires approximately 15 seconds, normal map generation takes about seconds, and mesh reconstruction, along with other processing steps (e.g., foreground segmentation, data postprocessing, and file saving), takes nearly minute.
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E ADDITIONAL VISUAL COMPARISONS
E QUALITATIVE COMPARISONS
We present additional qualitative comparison results Figs. 16 to 19, including mesh reconstruction, front-view 3D human reconstruction, and shape prediction comparisons. Please zoom in for details.
Inputs
Ours
AvatarBooth
PuzzleAvatar
GT
Figure 16; More Qualitative Comparisons on 4D-Dress and PuzzlelOI datasets.
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Inputs
Ours
AvatarBooth
PuzzleAvatar
Reference
Figure 17: More Qualitative Comparisons on in-the-wild dataset.
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Input
PSHuman
Ours
Figure 18: More Qualitative Comparisons of Single Image 3D Human Reconstruction with PSHuman.
Low
High
Semantity
PrompdHMR
Failed
Inputs
Figure 19; Error Maps Shape Prediction.
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E.2 CORRELATION MAPS
Pose-dependent correlation maps generation is an important module of UP2You, as the first part of the proposed PCFA, predicts the most relevant regions of input unconstrained photos for the conditioned pose. With the latter feature selection strategy, PCFA can focus on informative features for viewpoint generation. In Fig. 20, we provide more results of the generated correlation maps.
T
Figure 20; Visualize Results of Correlation Maps. Given the input reference images and target for multi-view image generation, the predicted correlation maps can effectively identify and discriminate correlated regions within reference inputs. For example, when generating the front-view, reference rogions that correspond front-facing views exhibit higher correlation values, demonstrating the model's ability to selectively attend to relevant spatial information.
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E.3 ANIMATION RESULTS
We present an animation sample generated by YPuYou using the same reference with different target poses, shown in Fig. 21. Notably, You maintains identity consistency well across different target poses. However, since this approach just reconstructs a textured mesh independently for each frame, temporal consistency of the rendered images and mesh topology is not guaranteed. For production-qualin animated sequences, we recommend using professional animation methods and tools [1, 6] for textured mesh animation.
Pose A
Pose B
Pose C
Pose
Figure Animation Results Textured Mesh Generated by UP2You.
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F ADDITIONAL ABLATION STUDIES
F.1 VISUAL RESULTS OF DIFFERENT ORTHOGONAL IMAGES GENERATION DESIGNS
Here, we present the generated visual results in Fig. 22 for different design choices in the multi-view image generation model. As indicated earlier, approach A directly concatenates all reference features for viewpoint generation, which may provide irrelevant features during generation and lead poor results. Approach B averages all reference features global guidance. This method ís time-efficient loses important color features and generates suboptimal results, Approach uses a weighted sum strategy to aggregate reference features after computing the correlation map, which loses details in some regions since regions with high correlation values may overlap. Approaches D and utilize CLIP and DINOv2 features, respectively, rather than ReferenceNet in method. CLIP features have low resolution and are difficult to preserve details such as facial and clothing textures, while DINOv2 is texture-insensitive and thus diffieult restore reference textures accurately.
Ours
Figure 22: Visual Comparisons of Different Multi-View Image Generation Designs.
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F.2 ROBUSTNESS OF TARGET POSE CONDITION.
While previous experiments highlight the strong generation ability of You, most target poses are in the "A-pose" con- figuration. Since 4D-Dress provides ground-truth multi-view images of persons with different poses, we further robust- ness by randomly selecting three diverse target poses per iden- tity from the 4D-Drrss dataset and evaluating our multi-view image generation performance. As shown in Tab. 9, UP2 maintains high-quality results across varied target poses using the same unconstrained photo inputs. Figure 23 further demonstrates the visual results, where identity consistently preserved across different poses. In addition, Figure 24 shows the generation results on subjects with loose clothing and complex target poses, further validating the generation capability and robustness of UP2You.
PSNRT
SSIMI
LIPIPS.
Pose
24.519
0.904
0.0715
Table 9: ID Consistency. UP2You achieves high- quality multi- view image generation
4D-Dress dalaset three different pase condition.
Pose A
Pose B
Posc
Pose A
Posc
Posc C
Figure Robustness target pose conditions. Our mothod can generate bigh-quality multi-viow images under different pose conditions with the reference inputs, demonstrating that identity information is effectively disentangled from conditions approach.
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Unconstrained Inputs
Target Pose
Generated Mesh
Unconstrained Inputs
Target Pose
Generated Mesh
Figure 24; Generation Results with Loose Clothing and Complex Target Pose.
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F.3 ANALYSIS OF SHAPE PREDICTOR.
To evaluate whether our shape predictor can regress con- sistent shape parameters, we
shape prediction model using different groups unconstrained reference inputs from the same identity. As shown in Tab. 10, our method achieves stable shape predictions across all input groups. Since the aggregated pixel-level features from reference inputs may contain information about personal shape characteristies, the multi-view image generation model in You exhibits some degree of robustness to shape variations. However, in extreme cases, more accu- rate shape predictions significantly enhance the quality of the final 3D human generation. We evaluate the impact of our shape predictor on the overall inference pipeline of You and find that incorporating the proposed shape predictor leads surable improvements in generation quality on the in-the-wild dataset. As demonstrated in Fig. 25, shape predictor enables more identity-consisten results for individuals with extreme body shapes, while Tab. 11 provides quantitative evidence that the proposed shape predictor improves performance on the tettthelllee dataset.
Group
Ref
Ref C
V2VL (mm)
7.485
7.503
7,443
Table 10: Shape prediction consistency dataset. We input three different groups of 12 reference inages
4D-Dress
shape predictor. vertex in vertex (V2V) errer of the predicted results shows stable values with low variance, demonstrating that our shape predictor is robust unconstrained reference puts.
person
Shape Predictor
wio Shape Predictor
Referemee
w/ Slope Poedictor
win Stage Predictorr
Ours Image
Ours Mesh
Our: Image
Ours Mesh
0.988
DINOT
0.132
0.916
0.927
0.911
Tablc Effects of Shape Predictor in-the-wild Dataset. Generation results with the sid of shupe predictor performance.
Figure 25: Shape Predictor Helps to Generate More dlentity-Consistent Results for People Extreme Shape.
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F.4 VISUAL RESULTS WITH DIFFERENT NUMBER OF INPUTS
In YP2Y as more unconstrained photos are provided as input, additional details can be extracted and refined in orthogonal views, thereby improving the reliability of the generated results. We demonstrate this principle through an illustrative example in Fig. 26.
Refs
Refs
Refs
12 Refs
GT
Figure 26: Generated Multi-View Image Results with Different Number of References, With references input, results noticed and genorated by our model, like facial details and cloching patterns.
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G MORE GENERATION RESULTS OF UP2YOU
Figures 27 and 28 present comprehensive generation results of You on two representative cases, including the reference images, generated multi-view images and normal maps, well as the rendered images and normal maps after mesh reconstruction. Figure 29 demonstrates that UP2You is robust to diverse inputs. performing well even in extreme scenarios such imputs missing the face, lower body, or upper body. Additionally, Figure 30 provides further examples of 3D virtual try-on applications.
Inputs
Ours Image
Generate
Ours Normal Generate
Ours Image Rendering
Ours Normal Rendering
Figure 27: Generated Results
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Inputs
Ours Image Generate
Ours Normal Generate
Ours Image Rendering
Ours Normal Rendering
Figure 28: Generated Results of UP2You.
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Inputs
Results
No Face
No Lower Body
No Upper Body
1
A
Higure 29; More Gemeration Cases with Invisible Parts, UP2You generates reasonable results with different kinds of invisible scenarios.
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Figure 30: More examples Virtual Try-On.
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H LIMITATIONS AND FUTURE WORKS
While our method shows promising results in generating high-quality 3D human avatars from unconstrained photos, there are still limitations that we plan to address in future work:
Dependence on 3D Data for Training: Our method relies on dataset of 3D human models for training the diffusion model. Acquiring high-quality 3D data can be challenging and may limit the diversity the generated avatars. future work, we aim to explore semi-supervised or unsupervised approaches that can leverage large-scale 2D image or video datasets to reduce this dependence on 3D data.
Texture Misalignment: Our method generates 6 orthogonal views for mesh reconstruction and texturing, which is insufficient for high-quality texture baking. Texture misalignment issues may arise some cases (Fig. 31). future work, we plan adopt video generation
models as the base framework for dense view synthesis to address this limitation.
Multiple Inference Stages: When processing in-the-wild photos, our mesh reconstruction pipeline involves four sequential stages: shape prediction, multi-view image generation, multi-view normal map generation, and mesh reconstruction. This multi-stage inference approach slows down the generation process and may introduce cumulative errors. We plan to develop a forward model that directly predicts the final results.
Figure 31: Failure Cases of UP2You. Since only orthogonal views {0", 45", 90%, 135", 180%, 270") are generated, the backside texture of generated humans lacking guidance, making the problem of texture misaligament.
36


Reviews:
[{"review_id": "CGXdPcaP3E", "reviewer": "Reviewer_FySx", "summary": "This paper proposes a method for 3D reconstruction from a human mesh from relatively unconstrained photographs with the only requirement being that the person must be wearing the same clothes. The overall pipeline consists of three stages:\n\n1) First, from unconstrained photographs, six full body views are generated (with the appropriate inpainting)\n2) Second, using the aforementioned images and coarse SMPL normals, detailed normals are predicted\n3) Finally, features from the first step are used to refine the global shape using a perceiver like architecture.\n\nQualitative and quantitatively the proposed method outperforms prior work and is relatively fast.", "questions": "See weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) The paper is well written and easy to follow\n2) The methodology is well motivated\n3) The single great insight of this paper is the discovery that correlation maps derived from DINOv2 via regression of its features helps generate multi-view images\n4) Strong quantitative and qualitative results", "weaknesses": "One weakness of this paper is the lack of proper ablation regarding two of it’s non-trivial decisions\n\n1) Why DINOv2 features? A plethora of semantic segmentation networks exists (including DINOv1) and at least some of them must be ablation to demonstrate the efficacy of constructing features maps via DINOv2\n\n2) The justification for using the Perciever model for shape refinement if relatively ad-hoc, it would be great if the authors could also share with more baselines such as a transferformer or a few MLPs", "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": "iPHChMqhaq", "reviewer": "Reviewer_zhP9", "summary": "This paper introduces a new method, called UP2You, for reconstructing 3D humans from unstructured inputs with various poses, cameras, and visible body parts. To achieve this goal, the authors separate the procedure into two parts: multi-view orthogonal images generation and 3D mesh reconstruction. The major point lies in the generation of high-quality multi-view images. Specifically, (1) the authors propose a correlation map prediction that allows for selecting the most informative features from input images. Furthermore, this technique helps maintain the computational efficiency while having more input images. (2) MVDiffusion model is applied to generate the multi-view images based on the selected feature, pose, and normal maps. Extensive experiments have been proposed to demonstrate the performance of UP2You, presenting much better performance than existing methods.", "questions": "Besides the weaknesses listed above, the reviewer has one more question: is the current version of UP2You capable of animating the reconstructed human mesh, considering the input contains various poses?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The authors propose a novel and efficient method for reconstructing 3D humans from unstructured input images. Additionally, the reconstruction quality and efficiency are demonstrated to be much better than existing SOTAs.\n\n+ The proposed correlation map prediction is useful in finding the most informative features, improving the efficiency of the networks.\n\n+ The paper is well-writen and easy to follow", "weaknesses": "The reviewer believe the method is well-designed and may have some concerns regarding the experiments:\n\n- The method lacks the qualitative and quantitative comparisons with standard human reconstruction, such as PIFu, ICON, ECON. This would help demonstrate the reason why we need this type of method with unstructured inputs. \n\n- The paper would benefit from more results with loose clothing, complex poses as in ECON to demonstrate the performance.\n\n- The paper may also benefit from the evaluation of the pose variations and occlusion ratio presented in the input? For example, (1) can UP2You handle inputs with largely different poses? (2) what if some parts of the human body are not presented in the input images? (3) what's the maximal occlusion ratio in the used test set? One example might be: the input contains an image which captures only the foot part, while the other images don't contain the foot.", "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": "07MBVZPbvX", "reviewer": "Reviewer_8gj9", "summary": "UP2You is a tuning-free method for reconstructing high-fidelity 3D clothed portraits from unconstrained 2D photos. It uses a \"data rectifier\" paradigm (via modules like PCFA and a perceiver-based shape predictor) to convert raw, unstructured inputs into orthogonal multi-view images and normal maps, enabling fast (1.5 minutes per person) and high-quality reconstruction. It outperforms baselines on geometric accuracy and texture fidelity across datasets like PuzzleIOI and 4D-Dress.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Processes unconstrained photos quickly (1.5 minutes total) with nearly constant memory usage regardless of input count.\n2. Surpasses SOTA methods in key metrics (e.g., 15% lower Chamfer distance on PuzzleIOI, 21% higher PSNR on 4D-Dress) via selective feature aggregation.\n3. Supports arbitrary pose control and training-free 3D virtual try-on without relying on pre-captured body templates.", "weaknesses": "1. In 3D clothed portrait reconstruction, how does the rectifier ensure data quality when converting unconstrained photos into orthogonal views? What mechanisms maintain multi-view texture consistency and preserve fine details like clothing patterns and facial features, avoiding distortion or misalignment?\n\n2. For unconstrained inputs with unknown or noisy camera parameters, how does the rectifier align orthogonal views with accurate intrinsic and extrinsic settings? Does it use camera calibration or pose/shape priors (e.g., SMPL-X normal maps)? Is the alignment achieved via iterative optimization or direct mapping?\n\n3. How does this rectification improve the geometric accuracy of subsequent 3D reconstruction, such as reducing mesh carving or texture baking errors?\n\n4. The paper examines reference image count and feature aggregation (e.g., averaging vs. PCFA) but lacks ablation on key PCFA hyperparameters—like γ (feature retention ratio) or transformer block number. How do these affect reconstruction accuracy (e.g., smaller γ causing detail loss or larger γ adding noise), and why were the final values chosen?", "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": "vJ5UMhpdT4", "reviewer": "Reviewer_VnCH", "summary": "The paper introduces UP2You, a tuning-free method for reconstructing high-fidelity 3D textured meshes of clothed humans from unconstrained photo collections (e.g., partial views, occlusions, varying poses, and viewpoints). Unlike prior approaches that rely on personalization via fine-tuning (e.g., DreamBooth + SDS optimization, which take hours), UP2You adopts a \"data rectifier\" paradigm: it aggregates features from input photos using a Pose-Correlated Feature Aggregation (PCFA) module within an MV-Adapter diffusion backbone to generate clean orthogonal multi-view RGB images and normals in seconds. It also includes a perceiver-based multi-reference shape predictor to estimate SMPL-X parameters without ground-truth templates. The pipeline culminates in mesh carving and texture baking adapted from PSHuman.", "questions": "1. Could you elaborate on the novelty of PCFA beyond standard attention mechanisms in diffusion models? How does it differ from similar feature selection in prior works like ReferenceNet or multi-view adapters, and what ablations justify its design over simpler top-k strategies without pose correlation?\n2. The pipeline relies on MV-Adapter finetuning for rectification and PSHuman for reconstruction—how much of the performance gains stem from these existing tools versus UP2You's contributions? Would the method work with alternative backbones, or is it tightly coupled to diffusion-based generation?\n3. For the shape predictor, how robust is it to very sparse inputs (e.g., 1-2 photos with heavy occlusions)? The paper mentions consistency across inputs, but could you provide metrics on shape estimation variance or comparisons to single-image HMR methods like BEDLAM or HYBRIK?\n4. The weakness in innovation seems tied to the serial A+B nature (diffusion rectification + existing recon); how does UP2You advance beyond a straightforward combination of MVDiffusion and PSHuman, especially since normals/meshes are generated using adapted priors from these?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The tuning-free design enables fast reconstruction (1.5 minutes total, with rectification in <15 seconds), scaling well to varying numbers of inputs (1 to dozens) with near-constant memory via PCFA's selective aggregation. This makes it suitable for real-world scenarios like personal photo albums, outperforming slow optimization-based methods like PuzzleAvatar (4+ hours).\n2. PCFA effectively fuses pose-correlated features from diverse photos, leading to better identity preservation and 3D consistency. Quantitative gains over SOTAs (e.g., PuzzleAvatar, AvatarBooth, PSHuman) in geometry and texture metrics, plus qualitative scaling with more inputs, demonstrate robustness to occlusions, croppings, and viewpoints.\n3. The perceiver-based shape predictor eliminates the need for pre-captured templates or ground-truth shapes, enabling flexible applications like random pose animation and multi-garment virtual try-on—all without additional training.", "weaknesses": "1. Limited core innovation in the pipeline: The method heavily builds on existing components (e.g., MV-Adapter for diffusion-based multi-view generation, PSHuman for final mesh reconstruction), with novelty primarily in PCFA and the shape predictor. This feels like a serial integration (rectification via finetuned diffusion + off-the-shelf reconstruction), potentially lacking a unified, groundbreaking advance beyond combining A+B-style modules.\n2. Dependency on diffusion priors and potential hallucinations: As a diffusion-based approach, it inherits risks of inconsistent outputs in extreme cases (e.g., highly occluded or sparse inputs), and the paper does not deeply address failure modes or ablation on the limits of rectification without further personalization.\n3. Evaluation scope and generalizability: Benchmarks are limited to specific datasets (PuzzleIOI, 4D-Dress, in-the-wild captures); lacks broader comparisons to recent diffusion-free methods or real-time systems. Runtime claims (1.5 minutes) may not hold on lower-end hardware, and virtual try-on is demonstrated but not quantitatively evaluated against specialized try-on models.", "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)