Direct Reward Fine-Tuning on Poses for Single Image to 3D Human in the Wild
Authors: Seunguk Do
Minwoo Huh
Joonghyuk Shin
Jaesik Park
Venue: ICLR 2026 Poster (2026)
Keywords: single-view 3D human reconstruction
image-to-3D
multi-view diffusion model
alignment
post training
Primary area: generative models
TL;DR: We propose a direct alignment algorithm to revise a single-view 3D human reconstruction model on diverse human poses.

Abstract: Single-view 3D human reconstruction has achieved remarkable progress through the adoption of multi-view diffusion models, yet the recovered 3D humans often exhibit unnatural poses. This phenomenon becomes pronounced when reconstructing 3D humans with dynamic or challenging poses, which we attribute to the limited scale of available 3D human datasets with diverse poses. To address this limitation, we introduce DrPose, Direct Reward fine-tuning algorithm on Poses, which enables post-training of a multi-view diffusion model on diverse poses without requiring expensive 3D human assets. DrPose trains a model using only human poses paired with single-view images, employing a direct reward fine-tuning to maximize PoseScore, which is our proposed differentiable reward that quantifies consistency between a generated multi-view latent image and a ground-truth human pose. This optimization is conducted on DrPose15K, a novel dataset that was constructed from an existing human motion dataset and a pose-conditioned video generative model. Constructed from abundant human pose sequence data, DrPose15K exhibits a broader pose distribution compared to existing 3D human datasets. We validate our approach through evaluation on conventional benchmark datasets, in-the-wild images, and a newly constructed benchmark, with a particular focus on assessing performance on challenging human poses. Our results demonstrate consistent qualitative and quantitative improvements across all benchmarks. Project page: https://seunguk-do.github.io/drpose.

Published as a conference paper at ICLR 2026
DIRECT REWARD FINE-TUNING ON POSES FOR SINGLE IMAGE TO 3D HUMAN IN THE WILD
Seunguk Do, Minwoo Huh, Joonghyuk Shin, Jaesik Park Scoul National University
Figure/ 1: We propose DrPose (Direct Reward Fine-tuning on Poses), a method to post-train a multi- view diffusion model for enhanced posture of reconstructed 3D humans in dynamic and acrobatic scenarios.
ABSTRACT
Single-view 3D human reconstruction has achieved remarkable progress through the adoption of multi-view diffusion models, yet the recovered 3D humans of- ten exhibit unnatural poses. This phenomenon becomes pronounced when re- constructing 3D humans with dynamic or challenging poses, which we attribute the limited scale of available 3D human datasets with diverse poses. To ad- dress this limitation, we introduce DrPose, Direct Reward fine-tuning algorithm Poses, which enables post-training of multi-view diffusion model on diverse poses without requiring expensive 3D human assets. DrPose trains a model us- ing only human poses paired with single-view images, employing direct reward fine-tuning to maximize PoseScore, which is our proposed differentiable reward that quantifies consistency between a generated multi-view latent image and a ground-truth human pose. This optimization is conducted on DrPose15K, novel dataset that was constructed from an existing human motion dataset and a pose- conditioned video generative model. Constructed from abundant human pose se- quence data, DrPose15K exhibits a broader pose distribution compared to exist- ing 3D human datasets. We validate approach by evaluating conventional benchmark datasets, in-the-wild images, and a newly constructed benchmark, with particular focus challenging human poses. Our results demonstrate consistent qualitative and quantitative improvements across all henchmarks. Project page: attps://seunguk do github. io/ drpose
INTRODUCTION
3D human models are essential assets across multiple industries, including visual media production (such games and movies), product and industrial design, and e-commerce platforms for fashion. While multi-view scanning systems and manual design processes currently dominate 3D human crafting workflows, single-view 3D human reconstruction technology has garnered attention due to rapid technical advances and its practical advantages in scenarios where capturing multiple camera angles is either impractical impossible.

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Recent advances in this technology have been driven by the adoption of sooagg--o-muuti-yeee (12MV) diffusion models, which have enhanced reconstruction quality for occluded body parts invisible in
the imput image (Pan et al. 2024 Peng et al. 2024 [i et al. 2024b He et al. 2024 Xue al. 2024
Ho et al. 2023aa. This approach typically employs a two-stage pipeline: first generating multi- view images from single input using diffusion model, then lifting these views into 3D space through either implicit reconstruction (Saito et al., 2019; 9 Ho et alla, 2023a) or explicit reconstruction techniques (Li et al. 2024b; Palfinger 2022 Xiu et al. (022a). Compared previous works, which directly reconstruct 3D structure from the imput-view view feature (Saito et al. 2019, 2020) or works utilizing an estimated SMPL model (Xiu et al., (022b multi-view diffusion diffusion-based approaches have the benefit of using the powerful prior of diffusion models for the fine details on the unseen regions from the input-view.
Despite these advancements, a bottleneck persists that limits real-world applicability. Recovered 31D humans with a multi-view diffusion model often exhibit unnatural postures, especially when target poses are dynamic and challenging, such extreme athletic movements acrobatic postures. We argue that this limitation stems from the limited scale of publicly available training datasets (uu al. 2021 Han et al. 2023 Ho et al. 2023b) with diverse poses. This scarcity arises from the costs of recruiting diverse subjects and capturing them in varied poses using multi-view stereo setups, which further compounded by privacy concerns that complicate the release of public data.
Our key insight for overcoming this challenge is to align an 12MV diffusion model with 3D human poses from existing human motion dataset (Lin et al., 2023) that provides broader coverage of pose distributions than existing 3D human datasets. To this end, we first generate single-view im- age for each pose using pose-conditioned diffusion model (Men al. 2025), constructing a dataset we call DrPose 15K. We then post-train the 12MV diffusion model on this dataset using our proposed method DrPose, a direct reward fine-tuning algorithm on poses. DrPose maximizes PoseScore, our proposed differentiable reward function that quantifies the consistency between ground-trutt 3D poses and the latent images generated by the 12MV model.
Our evaluation demonstrates that 12MV models post-trained with DrPose achieve improvements in single-view 3D human reconstruction quality both quantitatively and qualitatively. These im- provements are consistent across all datasets, including conventional benchmarks (Yu et al.) [2021] Ho et al. 2023b), in-the-wild images, and MixamoRP, our new evaluation benchmark designed to assess performance complex and dynamic human poses.
Our key contributions are:
We propose DrPose, novel post-training algorithm for enhancing the alignment of an image-to-mult--view (12MV) model with natural poses dynamic and complex scenarios.
We construct DrPose15K 15K, dataset comprising human poses from a motion dataset (Lin) et al. 2023) paired with generated single-view images conditioned on each pose.
Through quantitative evaluation, we demonstrate that our method achieves consistent im- provements across all datasets, including conventional benchmarks and our proposed Mix- amoRP.
RELATED WORKS
2.1 SINGLE-VIEW 3D HUMAN RECONSTRUCTION
Single-view 3D human reconstruction remains a long-standing challenge in computer vision and graphics. Early approaches focused on recovering parametric human models (Loper et al. 2023 Pavlakos et 2019) but often lacked fine-grained details such as clothing and facial features (Bogo let al. 2016) Zhang et al., 2021: 2023a; (Sun et al.] 202T). A major advance was introduced by PIFu (Saito et al. 2019). which demonstrated that detailed 3D human shapes could be learned from a single image using implicit functions trained on 3D scan datasets. This inspired numerous ex-
tensions, including methods that (1) utilize normal maps to enhance surface quality (Sito et al.)
2020 Xiu et al. 2022b a), (2) utilize SMPL prior (Xiu et al.) (2022b a. Zhang et al. 2023b Zhuang
et al. 2025), (3) recover relightable textures (Alldieck et al. (022). and (4) generate animation-
ready avatars (Huang et al. 2020; He et al.] [2021] 1, Peng et al. 2024). Recently, generative models
have further advanced the field by improving reconstruction quality for previously unseen views by
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adopting score distillation sampling (Huang al. 2023: Wang al. 2023 AlBahar et al. 2023
Wang et al. 2024) or training a view diffusion model (Pan et al. 20224] Peng et al. 2024 Li
et al. [224bb] He et al. 2024; Xue et al., 2024 Hu al. 2025). However, when these models receive images of out-of-distribution poses as input, their outputs exhibit unnatural postures. To address this, propose new approach that leverages motion data (Lin al. (023) to augment pose coverage
and fine-tune multi-view diffusion models, thereby improving performance on diverse poses.
2.2 DIRECT REWARD FINE-TUNING OF DIFFUSION MODEL
Recent research has explored methods for post-training diffusion models align them with human preferences better, building on the success of reinforcement learning techniques in large language models. This alignment process typically involves three key components: (1) starting with pre- trained text-to-image diffusion model, (2) developing a reward model that evaluates attributes such as aesthetic quality, detail fidelity, and semantic alignment, and (3) optimizing the diffusion model to maximize these reward signals. Initial approaches utilized reinforcement learning (RL) objectives to maximize human preferences (Lee al. 2023 Black et al. 2023 Fan et al.] (023). Building on human preference data, researchers have developed differentiable neural networks that can evaluate input images (Xu al., 2023] Kirstain al.) 2023; Wu al.) 2023). Leveraging these advances, direct reward fine-tuning methods have recently emerged that post-train diffusion models using dif- ferentiable reward scores (Prabhudesai et al. 2024 Clark et al.) 2023 Wu et al. 2024), thereby demonstrating faster convergence than RL-based approaches. In this work, adopt DRTune (Wul ct al. 2024), a state-of-the-ee reward fine-tuning method, as the foundation for DrPose.
3 METHOD
This section describes our proposed method for aligning an image-to-man mmage-to-multi-view (I2MV) diffusion model to natural postures in dynamic or complex cases, thereby enhancing the quality of its integrated single-view 3D human reconstruction pipeline. We first present DrPose, novel post-training algorithm that aligns multi-view diffusion models to produce natural and accurate poses across diverse scenarios (Sec.3.1 Next, we describe the construction of Dr- 15se15K, our proposed training dataset with diverse pose coverage that DrPose operates on (Sec. (3.2). Finally, we describe the 3D reconstruction pipeline that uses an 12MV diffusion model post-trained via DrPose with explicit carving (Sec. 3.3
Algorithm 1 DrPose
Dataset: Image-pose pairs D = (Iz, 0:)
Inputs: 12MV diffussio model with initial weights wo. reward model r, the number of training timesteps K, maximum early stop timestep
Initialize We
while not converged
8 = randint [1 T
K +1
tuain {s +
R -i 0,1, K 1}
tmin = randint (1, 11
(I, ~ D
Xr ~ IN.,
LK.
for { T', T'...... ? é= <((stop.grac((t))... if & Itain then
3.1 DRPOSE
é = stop_grad(()
We introduce DrPose (Direct Reward Fine-tuning on Poses), a post-training algorithm for an I2MV diffu- sion model on a dataset, D = {l; 0.), where I;, are input image and the ground-truth human pose. The core idea is to maximize the consistency be- tween the generated multi-view latent images from I; and 0;, thereby better aligning the 12MV diffusion model with diverse poses in using PoseScore, our proposed differentiable reward.
else
éo Eu,, (stop grad(a))) I,t)
Lai = Lxi + E([Eé éo
to = (x: oa8)/ax
l = Imi then
20 do break
F:-1 a4-1 + 0z-1ê
Crewndt r(tn, B) w + w 1 (Croward + WKL LKL)
Building on DRTune (Wu et al. 2024), the previous
reward fine-tuning algorithm, 2o is generated from noise xT (O, I) while blocking the gra- dients of the denoising network input and sampling denoising steps for training, as described in Algo. This strategy enables optimization of early denoising steps while maintaining computa- tional efficiency. Then the reward loss Lyeward = - r(zo. is computed using PoseScore, a differentiable reward function denoted as
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Denoising Multi-view U-Net
Equally spaced steps for backpropagation
Leward
Generate (Sec. 3.2)
0
x
X
X
Xgg
Lxe
total
Xr
Xx+1
X
X---1
trainable module
Reference Denoising Multi-view U-Net
pretrained module
Figure 2: Illustration of DrPose presented in Algo I A Denoising multi-view U-Net ew is trained minimize Cieal Lrwward + WKL CKL- Multi-view latent images zg are generated from ør N(0, I), and Lrewant is computed from and the ground-truth 3D human pose 0. For efficiency, only subset of denoising steps sampled for training. Concurrently, LKI. computed as the KI. divergence between ew and the frozen reference U-Net Ewo at intermediate denoising steps. For clarity, only the 6 multi-view images are shown.
Differentiable Reward. To quantify the consistency between a multi-view latent image zo and GT pose develop PoseScore, a differentiable reward model denoted r. To compute consis- tency, it first projects both æo and onto the Taet and Tskel images, which depict the human skeletal structure. To convert æt into fatet U-Net based skeletal image predictor Jokl pretrained on existing 3D human datasets (Ho et al.) 2023b; Yu et al., 2021). Moreover, Iskel can be drawn from the pose parameter by projecting the 3D human joints I()) onto the image planes corresponding the viewpoints of the generated images. Then the reward is computed follows:
r r( 0) = -E([ faal - Taell)) = - -Ellgaa
R(J(O))II,,
(1)
where is the rendering of the 3D human joints into the skeletal images into the viewpoints of æpp
KL divergence regularization. As with previous direct reward fine-tuning methods (Wu et al. 2024 Prabhudesai et al.) (004), we find that training the denoising U-Net with PoseScore, differ- entiable e reward, leads to reward hacking, whereby image quality degrades while the reward score continues to increase. To address this, augment the training objective with KL divergence regularization term LKL alongside Lywaard This regularization computes E(I - è êoll), where é rep- resents the predicted noise from the trainable diffusion model some timestep ( buain: and én is the corresponding prediction from the initial diffusion model. This constraint prevents the model's generated images from deviating excessively from its original results while optimizing for reward maximization.
To summarize, DrPose operates the steps in Algorithm D to minimize the following objective:
min (1,0)~ [Creward (1,0) WKL CKL (4)).
3.2 CONSTRUCTION OF DRPOSE15K
We construct DrPose15K, a training dataset containing dynamic and challenging 3D human poses paired with single-view images, by leveraging Motion-X (Lin et al. 2023), a human motion dataset, and MIMO (Men et al. 2025). pose-conditioned mnnge-to-video(22V model, as illustrated in Figure From the Motion-X X dataset, we utilize the AIST (Li et al. 202I) subset due to its com- prehensive coverage of diverse pose distributions. To reduce redundancy among the 300K available poses, we apply farthest farthest-point sampling to select 1.5K poses. Then, we add the 9 temporal neigh- bors for each selected pose to create pose sequence for input to the MIMO, yielding total of 15K
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c--1
1++
THuman2.1
CustomHumans
DRPosel 5K
Pose-conditioned Video Generator (MIMO)
2K samples (546=
0.6K samples (std=7.623)
15K samples
(std=11.53)
Subject Image
Motion {0:
Scene Images (White background)
Figure 4: Comparison of pose diversity between conventional 3D human datasets (Yu et al.) 2021 Ho et al., 2023b) and our proposed Dr- Pose15t 15K. Our dataset has higher standard de- viation of SMPL-X joint locations than other datasets.
Figure 3: Construction process for DrPose 15K. We employ a cose-conditioned video generator model (Men et al. (2025) to generate single-view images from 3D human poses.
poses. Finally, we use MIMO to animate full-body human images from Generated Photos accord- ing to these pose sequences, generating corresponding single-vies sigle-vieww/o images for each 3D pose in our dataset.
To quantitatively assess the pose diversity of DrPose15K compared to conventional 3D human datasets (Ho et al.] 2023h Yu et al. 2021), compute the standard deviation of SMPL-X joint positions across each dataset, focusing exclusively on the 22 body joints while excluding facial and hand joints. Note that for conventional datasets, we include both training and test splits in this anal- ysis. As shown in Figure 4 DrPose 15K exhibits a 1.73x larger standard deviation compared to THuman2.1 1 (Yu et 2021). Moreover, with 14.7K poses, compared 647 in CustomHumans and 2,445 in 1.uman2.1, DrPose15K provides broader coverage of pose distributions.
3.3 3D HUMAN RECONSTRUCTION WITH EXPLICIT CARVING
We show the overview of the pipeline for reconstruction of 3D humans from a single-view image in F In this pipeline, we employ an explicit carving to fit the generated multi-view images into humans, following Li et al. (2024b). The pipeline first generates both normal maps and RGB images from the input image using a multi-view diffusion model post-trained with our DrPose. 3D human mesh recovery then proceeds through three sequential steps: SMPL-X initialization, differenniable remeshing (Palfinger 2022), and appearance fusion. This approach delivers superior geometric detail compared to methods using pretrained implicit networks (Ho et al. 2023a Pan et al. 2024)
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
DrPose During the post-training, we employ the DDIM sampler with T 20 total denoising steps and = training steps. We set the maximum early-stopping timestep to m = and weight the KI divergence loss tigL 0.01. For computing LXL. mean squared error estimate || ēoll.
Denoising U-Net To evaluate our proposed method DrPose, we apply it to two image-to-multi- view (12MV) diffusion models, Fra3D (Li et al. 2024a) and PSHuman (Li et al. 2024b), respec-
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Post-trained with
color fusion
Section 3.1
single-view pose estimation
Image to to- Salliiii HAMKAW PAA
Input Image
Generated RGB & Normals
Initial Geometry
Optimized Geometry
Textured 3D Human
differentiable remeshing
Figure 5: Overview our 3D human reconstruction pipeline. In this pipeline, the multi-view normal and RGB images are generated from the input image using an mmage-to-multi-nn (I2MV) diffusion model. These images are then converted into a 3D representation using explicit human carving (Li et al. (2024b). In this work, we propose post-training the 12MV diffusion model to achieve better alignment with accurate poses in dynamic and acrobatic scenarios. For clarity, only of the 6 multi- view images are displayed for normal maps and RGB images.
tively, following their original architectures and initializing the weights wh accordingly. The model fine-tuned on a single NVIDIA H200 GPU using a batch size of with gradient accumulation over steps for 18K iterations.
Differentiable Reward For computing the reward, we use binary cross entropy loss and LPIPS to estimate || - I - The skeletal images Lxet and Ikel both have 23 channels, with each channel corresponding to joint. We use T (Yu et al. 2021 and the training subset of Cus- tomHumans (Ho et al.] (023b) as our training datasets, comprising approximately 3K scans. To get six-view normal and color images, we render the 3D scans using Blender's s Cycles engine (Com- munity (018)) with an orthographic camera configuration. The reward model is trained on four NVIDIA RTX 6000 Ada GPUs with a batch size of 16 over 10K iterations.
4.2
SINGLE-VIEW 3D HUMAN RECONSTRUCTION
4.2. 1
BASELINES
We compare approach against single-view 3D human reconstruction methods guided by
SMPL [iu et al. 2022a Ho et al. 2023a). as well as multi-view diffusion-based methods (Wul
al. 2023
Li 2024a b).
ECON (Xiu et al., 2022a) estimates front and back depth maps using an estimated SMPL- X prior, then fuses these depth maps for a complete 3D human body. It does not support texture reconstruction and trains depth estimation network on 500 scans from THu- man2.0 Yu et al. 2021).
SITH (Ho al.) 2023a) generates 512x512 px. RGB images for front and back views using an estimated SMPL-X prior, subsequently converting them to 3D via an SDF network. The diffusion model is trained THuman2.0.
(Xue et al. 2024) produces four 256x256 px. RGB multi-view images, which then converted to 3D using 3DGS reconstruction network. The multi- view diffusion model is trained on 6K human scans, combining both public datasets and commercial datasets.
HHD(Human
Era3D (Li et al. 2024a) generates six 512x512 px. normal and RGB images using a diffusion network trained Objaverse (Deitke fine-tune this model on 3K scans from THuman2.1 I and CustomHumans (Ho et al. 2023b)
(003). For fair comparison,
datasets.
PSHuman (i et al. 2024b) produces six 768x768 px. normal and RGB images using a diffusion network trained on THuman2.1 and CustomHumans datasets.

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Figure 6: Representative visualizations for the MixamoRP benchmark. The 18 meshes shown were randomly sampled from the complete dataset containing 60 meshes.
4.2.2 BENCHMARKS
All baseline models in 4.2. and ours are evaluated quantitatively in the following three benchmarks:
THuman2.1-tess --eet contains 60 human scans selected from the full THuman2.1 Yu et al,
2021 dataset. The split follows Li al. (2024b).
CustomHumann-tesst contains 60 human scans selected from the full CustomHumans dataset, which consists of 600 human scans. The split follows Ho et al. (2023a).
MixamoRP
proposed benchmark containing 60 human scans, constructed by assign- ing 60 distinct poses collected from Mixamo animation, to 15 different Renderpeople 3D models, with 4 poses per model(see Fig. 6 for its visualization).
Test splits from CustomHumans (Ho et al. 2023b) and THuman2.1 (Yu et al. 2021 are commonly used benchmarks for evaluating single-view human reconstruction methods. While these bench- marks include dynamic poses such as dancing jumping, they lack extremely challenging config- urations (see Figure such as breakdancing or bat swinging.
To establish rigorous evaluation criteria for 3D human recon- struction under extreme pose variations, we introduce MixamoRP, novel benchmark specifically designed assess reconstruction performance challenging pose configurations. To this end, we animate commercial rigged 3D models from Renderpeople (Renderpeople) (023) using Mix- amo [Inc.. 2025) animations. Table character names, animations, descriptions, and frame indices for reproducibility, and Fig. presents representative visualizations of MixamoRP.
Construction of MixamoRP
4.2.3 EVALUATION PROTOCOL
We evaluate all baselines using their default configurations, with both Era3D and PSHuman variants employing 40 denoising steps and a classifier free guidance (Ho & Salimans, 2022) scale 3.0.
For each mesh scan, render input images from evenly distributed azimuthal views, yielding 180 input views per benchmark. To evaluate geometric accuracy, we report three metrics in Table Chamfer Distance (CD), Normal Consistency (NC), and F-Score. To compute the Chamfer Distance, we uniformly sample 100K points per mesh
For appearance evaluation, report three metrics in Table PSNR. SSIM, and LPIPS. To compute these metrics, we render images of both the prediction and the ground truth from 6 evenly distributed azimuthal views distinct from the input views.

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Table 1: Quantitative comparisons of geometry quality on single-view human reconstruction bench- marks. Our proposed benchmark MixamoRP is described in Section 4.2.2 Era3D* represents the original Era3D model fine-tuned on CustomHumans and THuman2.1 training splits using conven- tional DDPM loss. Ours (Era3D) denotes the Era3D model post-trained with our proposed DrPose on DrPose15ke
THuman2. 1-test
CustomHumans-test
MixamoRP
Method
CDL
NCt
f-Scoret
CD4
NC1
f-Scoret
CDL
NCt
f-Scoret
ECON
101.6465
0.6311
8.5244
126.1430
0.6205
6,4700
166.5384
0.5705
5,2173
SiTH
63.3041
0.6790
14.9221
71.9378
0.6713
12.7957
158.2729
0.5685
6.5176
H3D
75.8328
0,5959
12.2189
94.0864
0,5872
10,5563
149.2832
0,5400
7.2219
Era3D*
55.4071
0.6976
16.2928
63.1260
0.6914
14.1348
150.0118
0.5916
7.3487
PSHuman
52.9643
0.7194
18.6308
52.2187
0.7272
18,5624
137.2814
0,5876
8,2065
Ours (Era3D)
41.1700
0.7265
20.7102
44.3811
0.7310
20.1153
126.0622
0.5887
8.3071
Ours
42.0529
0.7252
19.6811
44.1326
0.7336
18,9600
126.5312
0.5998
8.8185
Table 2: Quantitative evaluation of 3D human reconstruction quality. Six RGB views evenly dis-
tributed in azimuth are rendered to compute appearance metrics. Our proposed benchmark Mix- amoRP is described in Section 4.2.2 Era3D* represents the original Era3D model fine-tuned on
CustomHumans and THuman2.1 training splits using conventional DDPM loss. Ours (Era3D) de- notes the Era3D model post-trained with our proposed DrPose on DrPose15K.
THuman2.
Custom/Humans-test
MixamoRP
Method
PSNRt
SSIMt
LPIPS
PSNRT
SSIMt
LPIPS|
PSNRT
SSIM
LPIPS.
SITH
17.7473
0.8829
0.1371
18.7462
0.8599
0.1533
15.6096
0.8118
0.2182
H3D
17.3290
0.8656
0.1561
17.6244
0.8328
0.1882
15.2037
0.7915
0.2440
Era3D*
17.7662
0.8826
0.1410
18.6322
0.8581
0.1584
17.5337
0.8623
0.1519
PSHuman
18.3880
0,8922
0.1286
18,9082
0.8612
0.1538
17.5931
0.8638
0,
Ours (Era3D)
20.3563
0.9050
0.1134
18.9286
0.8644
0.1508
17.5064
0.8662
0. 0.1474
Ours
20.8594
0.9078
0.1063
19.1887
0.8638
0.1476
17.6632
0.8646
0.1465
4.2.4 RESULTS
As Table D and Table 2 present, our results demonstrate that DrPose consistently improves recon- struction quality of the base model across all benchmarks. We attribute this to our proposed DrPose's ability enhance the accuracy reconstructed posture on diverse poses, as seen in Figure 7 and g
Ablation Study the base model We conduct an ablation study on the base model by post- training Era3D* using DrPose. As reported in Table and Table 2, the ErasD-based model shows similar performance across all benchmarks. However, because the PSSHuman-bsse model qualita- tively yields better results on face regions, we chose PSHuman as our base model.
In Figure and Table we analyze the trained Askel of PoseScore introduced in Section 3.1 The evaluation is conducted on SMPI. and sean mesh pairs from the test splits of CustomHu- mans and THuman2.1. The scan meshes are rendered as multi-view normal and color im- ages, which are then encoded as latent images using PSHuman's 's VAE. These latent images are fed into Isket to produce skeletal images. Both the quantitative metrics and qualitative results demonstrate that gikel is sufficiently reliable to serve as a measure of consistency between latent images and poses.
Table 3: Quantitative evaluation of Jskel in Pos- eScore
Benchmark
PSNR
SSIM
LPIPS
THluman2.1-test
22,4807
0.9337
0.0580
Custom//umans-test
24.4081
0.9536
0.0430
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Input
SiTH
Fra3D
PSHuman
Ours
GT
Figure 7: Qualitative evaluation on the CustomHuman dataset. Era3D* denotes Era3l) fine-tuned on CustomHumans and 1 datasets.
Input
SiTH
Fra3D*
PSHuman
Ours
Figure 8: Qualitative evaluation the internet-source images. Era3D* denotes Era3D fine-tuned on CustomHumans and THuman2.1 datasets.
5 CONCLUSION
We propose novel approach to improve the pose accuracy of 3D humans reconstructed by multi- view diffusion models. Our method comprises three key contributions: (1) DrPosel5K, dataset featuring diverse poses with corresponding single-view images, (2) DrPose, algorithm that en- ables post-training of multi-view diffusion models on this dataset: and (3) MixamoRP, benchmark for evaluating reconstruction under challenging poses. Our post- trained model shows consistent quality improvements across all benchmarks. We discuss the ethics and reproducibility statements in Appendix A.6 and A.7

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Input
Geometry
Texture
Figure 9: Qualitative results of DrPose in-the-wil wild internet images.
Normal
RGB
Pred
Figure 10: Visualization gskel in PoseScore. Iskel converts the multi-view latent images encoded from the normal and RGB images using the base model's VAE.
Limitations Similar previous singleimaage-t-333 human modeling approaches, pipeline requires segmented input images. When input images contain imperfect segmentation, artifacts such floating geometry appear in the boundary regions of the generated 3D humans, illustrated in Figure
Although DrPose employs gradient stopping and gradient checkpointing techniques, it requires sub- stantial GPU memory, as it generates 24 images of size 768x768 px through an iterative denoising process compute PoseScore. Also, our KL-divergence term incurs additional GPU memory and computation overhead, as it requires storing the initial denoising U-Net and performing noise predic- tion each iteration, although it mitigates reward hacking. We expect that efficiency improvements in future multi-view diffusion models will alleviate these issues.
ACKNOWLEDGEMENTS
This work was supported by IITP grant (No. RS-2021-11211333, Artificial Intelligence Gradu- ate School Program at Seoul National University) (5%), NRF grant (N0.2033RA1122078811 (50%), and IITP grant (No.RS-0025-0330777 viewpoint immersive visualization for extended reality) (45%), funded by the Korea Government (MSIT).
Realworld multi-space fusion and 6DoF free-
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A
APPENDIX
A.1 ADDITIONAL RESULTS
We provide additional qualitative comparisons in Figures 15 and T6
A.2
LIMITATIONS
Our pipeline inherits limitations from prior iaalll-imaaee--3-3 approaches, shown in Figure [] First, imperfect input segmentation causes floating geometry artifacts at boundaries. Second, while improving overall shape and pose, our method struggles with fine details like hands.
Input Image
PSHuman
Ours
Figure 11: Our pipeline is sensitive to the quality of the segmented masks, producing artifacts.
A.3 ANALYSIS ON EFFICIENCY
We report the latency of each model for reconstructing a single sample in Table Ours has the same efficiency PSHuman since it base model, while Ours (Era3D) has the same efficiency as the Era3D.
Table 4: Reconstruction latency per sample for each model.
ECON
SiTH
Era3D
PSHuman
Ours (Era3D)
Ours
183.27 sec.
117.35 sec.
15.24 sec.
42.70 see.
15.24 sec.
42,70 sec.
A.4 NETWORK ARCHITECTURE
Figures 12 and 14 show the network design for the Denoising U-Net in the multi-view diffusion used in the pipeline illustrated in Fig 5] the Denoising U-Net in the MIMO Men et al. (2025), pose-conditioned video generator, and the skeletal image predictor in the PoseScore introduced in
3.c
A.5 USE OF LARGE LANGUAGE MODELS
We employ a large language model to refine the manuscript text by correcting grammatical error and enhancing sentence fluency. The LLM is not involved in research ideation, methodology devel- opment, experimental design, the generation of original content. All intellectual contributions, including the research direction, analyses, and conclusions, are made entirely by the authors.
A.6 ETHICS STATEMENT
Demographic Bias Our base model, PSHuman (Li et al. 2024b), is trained on THuman2.1 YYu et al. 2021) and CustomHumaas (Ho et al. 2023b), which exhibit demographic imbalances. THu- man2.1 contains 2,445 human subjects who are predominantly of Asian ethnicity, while CustomHu- mans, though more ethnically diverse, comprises only 647 subjects. This imbalanced representation may result in biased reconstruction performance that favors demographics overrepresented in the training data, leading reduced quality and accuracy for underrepresented groups
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Potential for Misuse
The generated 3D human models pose risks for creating misleading harm- ful content. These reconstructions can be integrated into 3D scenes and animated using standard rigging techniques, potentially enabling the creation of disinformation or deepfake content.
The automation capabilities of image-to-3D human modeling technology may impact employment in creative industries, affecting 3D artists, character designers, and digital con- tent creators who specialize in human modeling. While this technology can enhance productivity and accessibility, it also raises questions about the displacement of skilled professionals.
Industrial Impact
A.7 REPRODUCIBILITY STATEMENT
DrPose 15K is constructed from the publicly available Motion-X - dataset (Lin et 2023) and MIMO
model (Men et al. 2025). MixamoRP is constructed from scans of RenderPeople (Renderpeople:
2023 and motions from Mixamo [Inc. 2025): ; while both resources
available, RenderPeople is commercial product. In Section S we explain the high-level concepts underlying our approach and
provide pseudocode and experimental details in Algorithm and S. to ensure reproducibil-
ity.
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Denoising U-Net
Inpul Image
Latent Image
Latent Image
x--1
concatenate
Figure 12: Architecture of the Denoising U-Net for multi-view diffusion in our pipeline inspired by Li et al. (2024b) (illustrated F)g The denoising U-Net follows the architecture of PSHuman (Li et al.) 2024b). The input image is conditioned into the denoising process through two parallel path- ways: (I) VAE encoder & encodes the input image, which then concatenated with the latent image Ty. (2) A CLIP image encoder т encodes the input image, and the generated tokens are fed into the cross-attention layers of the denoising U-Net.
Latent Video
Denoising U-Net
Latent Video
Vt-1
Co
Cya
Cza
Figure 13: Architecture of the denoising U-Net for the pose-conditioned image-to-video model used in the DrPose15k construction process (illustrated in Fig. The denoising U-Net follows the architecture of MIMO (Men al., (2025) and takes three conditioning signals: (I) scene code C so, (2) motion code * mo, and (3) identity code C id. The scene code C * so is first concatenated with the latent video V; and then added the motion code mo. The identity code id is fed into the cross-atee aotsetattnnionn layers of the denoising U-Net. Note that the temporal layers of the denoising U-Net Guo et 2023) are omitted this figure.
Skeletal Image Predictor
Latent Image
Skeletal Image
Figure 14: Architecture of the Skeletal Image Predictor in our PoseScore introduced in Sec 3.1 The network follows U-Net architecture with an encoder-decoder structure. Multi-view latents are processed through initial convolutions, then flattened and passed through four downsampling blocks (reducing spatial resolution from 64x64 to 4x4 while increasing channels from 32 to 512), followed by four upsampling blocks with skip connections that restore the original resolution. The output produces predicted skeletal images for all views simultaneously.
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Input
SiTH
Fra3D*
PSHuman
Ours
Figure 15: Additional qualitative evaluation the internet-source images. Era3D* denotes Era3D fine-tuned on CustomHumans and T'Human.1 1 datasets.
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Input
SITH
Era3D*
PSHuman
Ours
Figure 16: Additional qualitative evaluation on the internet-source images. Era3D* denotes Era3D fine-tuned on CustomHumans and 1'ummn2.1 1 datasets.
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Table 5: MixamoRP dataset specification
Character
Animation
Description
Frame number(s)
Carla
Drop Kick
35, 46, 62
Carla
Start Plank
137
Claudia
Freehang Climb
47, 67
Claudia
Flying Knee Punch Combo
29, 79
Eric
Swing To Land
Swing Backflip To Crouched Land Sitting To Standing
26, 58
Eric
Standing Up
41, 88
Henry
Situp To Idle
15, 49, 70
Henry
Female Standing Pose
On Left Leg, Right Hand...
Johanna
Twist Dance
163
Johanna
Jump Push Up
25
Johanna
Sitting Laughing
67
Johanna
Praying
...Pryyer To Standing Up
Kumar
Rifle Turn And Kick
40, 48
Kumar
Dancing Twerk
179
Kumar
Crouch Turn Left 90
Turning 90 Degrees Left
Michael
Pain Gesture Breakdance 1 1990
20
Michel
...aansttand Spin Start
1, 82, 100
Mira
Change Direction
25
Mira
Mma Kick
Mma Medium Kick
15, 22
Mira
Beckoning
26
Otto
Throw Grenade
While In Prone Position
65
Otto
Run Backwards
..Backwardds To Crouched Stop
37
Otto
Hurricane Kick
Otto
Grabbing Ammo
74
Sebastian
Pistol Kneeling Idle
Sebastian
Crawling
34
Sebastian
Dig And Plant Seeds
15, 70
Sheila
Shuffling
33
Sheila
Great Sword Slash
Great Sword Combo Slash Sword And Shield High Attack
47, 55, 62
Sydney
Sword And Shield Attack
17,26
Sydney
Running Jump
Jumping From A Sprint Afoxe Samba Reggac Dance
7,22
Tiffany
Samba Dancing
139
Tiffany
Stable Sword Inward Slash
5,
Toshiro
Martelo 2
17
Toshiro
Jump Attack
11, 27, 53
Victoria
Great Sword Crouching
...Sword Crouch To Block
10
Victoria
Giratoria
61
Victoria
Jab Cross
Boxing Jab Cross Medium
22
Victoria
Jump
Jump In Place
35
20


Reviews:
[{"review_id": "GaXlmWPdwq", "reviewer": "Reviewer_iYqC", "summary": "This paper proposes a strategy to enable robust human geometry reconstruction under extreme poses. To achieve this, the DRPose15k dataset and a differentiable reward model fine-tuning strategy are introduced.", "questions": "- Which pose-conditioned image-to-video diffusion model was used to create the DRPose15k dataset?\n\n- What happens if the pose-conditioned image-to-video diffusion model fails for extreme poses? Do you manually filter out those noisy outputs? How do such outliers affect model performance?\n\n- Typo in Fig. 2 caption: “Kmage-to-multi-view” → “Image-to-multi-view.”\n\n- Suggestion: In Fig. 5, add a brief explanation of the algorithm. The figure should be self-explanatory.\n\n- Suggestion: Please include architectural details (e.g., video diffusion model, denoising UNet, skeleton decoder, etc.) in the appendix for reproducibility.", "limitations": "", "rating": 10, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- Very well motivated — recent diffusion-based methods fail on complicated poses (e.g., acrobatic poses).\n\n- Well-designed and effective RL-based reward model that is differentiable.\n\n- Strong improvement over SOTA methods (SiTH, PSHuman, etc.).\n\n- Thoroughly studied recent literature.\n\n- The DRPose15k dataset provides extreme-pose ground-truth SMPL-image pairs, which are valuable to the community. The idea of using a pose-conditioned video diffusion model to create this dataset is brilliant, as going in the opposite direction does not work well (i.e., curating extreme-pose RGB images and then using off-the-shelf SMPL estimators).\n\n- Evaluated on multiple datasets, validating the generalizability of this method.\n\n- Overall, I enjoyed reading this paper. Solid results with strong motivation and a well-designed, effective RL-based strategy. I vote for the acceptance of the paper. As this paper can enlighten future works, I encourage the authors to improve the reproducibility in the final revision by providing more implementation details.", "weaknesses": "- Colors seem washed out; I have noticed this in recent diffusion-based single-view human reconstruction methods. Why is this happening, and how can it be improved?\n\n- Reproducibility concern: As suggested in the “Suggestions” section, it would be great if architectural details could be included in the appendix of the final revision.\n\n- Otherwise, I am happy with the current submission.", "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": "lvkQvfOm1Z", "reviewer": "Reviewer_hy1Y", "summary": "This paper tackles the problem of unnatural poses in single-view 3D human reconstruction, particularly for dynamic and challenging movements. The authors propose DRPOSE, a direct reward fine-tuning method for image-to-multi-view (I2MV) diffusion models that doesn't require expensive 3D human assets. Key contributions include: \n\nDRPOSE15K: A dataset of 15K diverse poses paired with single-view images, constructed from Motion-X motion data and the MIMO pose-conditioned video generator\n\nDRPOSE Algorithm: Post-training method using a differentiable POSESCORE reward that measures consistency between generated multi-view latents and ground-truth poses\n\nMIXAMORP Benchmark: New evaluation benchmark for challenging human poses\nConsistent improvements across benchmarks (THuman2.1, CustomHumans, MIXAMORP)", "questions": "Would some samples of DRPOSE15K be shown in to see the quality? Would this be open-sourced?\nHow different is the proposed method compared to MagicMan which also finetunes MV-diffusion on human dataset?\nHow loose clothes is handled, how appearance is handled?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.Well-motivated focus on pose quality limitations in existing approaches, with quantitative evidence (1.73× larger pose diversity in DRPOSE15K vs THuman2.1)\n\n2.Creative data construction: Leveraging motion capture + video generation to avoid expensive 3D scanning.", "weaknesses": "Circular dependency in data quality: DRPOSE15K relies on MIMO to generate training images, creating a potential bottleneck. If MIMO produces unrealistic appearances for extreme poses, the model learns from flawed data. This isn't adequately discussed or validated.\n\nInsufficient reward model analysis: no ablation on reward formulation alternatives (e.g., direct 3D keypoint prediction, discriminator-based rewards).\n\nInsufficient failure cases are shown, it is hard to evaluate, but the geometry shown in supp videos are pool such as the artifacts in hands, legs and clothes.\n\nMissing references:\nSIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction. In CVPR 2024\nTeCH: Text-guided Reconstruction of Lifelike Clothed Humans. In 3DV 2024\nGeneralizable Human Gaussians from Single-View Image. In ICLR 2025\nHumangif: Single-view human diffusion with generative prior. In Arxiv 2025\nWonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction. In Arxiv 2025", "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": "tA7KQ34Ecb", "reviewer": "Reviewer_eWSd", "summary": "This paper presents DRPOSE, a framework designed to improve single-image 3D human reconstruction, particularly for challenging poses. The authors identify limitations in existing datasets and corresponding methods for training and evaluating models for this task. To address these issues, they introduce a post-training approach for image-to-multi-view (I2MV) diffusion models using a differentiable reward function, POSESCORE, to better align the model outputs with ground-truth poses. Furthermore, they contribute DRPOSE15K, a diverse pose dataset for training, and MIXAMORP, a benchmark for evaluating performance on extreme poses. The proposed system is evaluated on several datasets, showing consistent improvements in pose accuracy and reconstruction quality.", "questions": "Your method defines the reward as r(x_0, θ) but uses it to derive the loss function L_reward = 1 - r(x_0, θ). Why do you choose to frame it as a reward instead of directly treating it as a loss?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Comprehensive Motivation and Contribution:\n\nThe paper starts with a clear motivation, highlighting the limitations of existing datasets and evaluation protocols for single-image 3D human reconstruction. To address these, the authors propose a systematic set of contributions: a novel post-training method (POSESCORE-driven fine-tuning), a new training dataset (DRPOSE15K), and a tailored evaluation benchmark (MIXAMORP). The contributions are well-aligned with the stated motivation and provide a holistic solution to the identified challenges.\n\n- Dataset Contributions (Training and Evaluation):\n\nDRPOSE15K significantly expands the diversity of human poses compared to prior datasets like THuman2.1, offering a valuable resource for training models on challenging poses. Additionally, the MIXAMORP benchmark addresses a critical gap in evaluating reconstruction performance under extreme pose variations, making it a practical addition for future research in this area.", "weaknesses": "- Pipeline Novelty:\n\nWhile the paper provides a systematic solution, the overall pipeline lacks substantial innovation. The framework is primarily built on an image-to-multi-view (I2MV) diffusion model followed by existing 3D reconstruction techniques. The pipeline primarily follows previous designs(Like PSHuman), with the main novelty lying in the reward-based fine-tuning (POSESCORE) and the dataset contributions.\n\n- Limited Impact of Individual Contributions:\n\nThe reward function POSESCORE, while effective, is relatively straightforward and lacks deeper theoretical exploration. Similarly, the dataset and benchmark contributions, while useful, are incremental rather than transformative.\n\nOverall, this is a well-executed paper, though not a groundbreaking one.", "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": "ND1j604n2N", "reviewer": "Reviewer_uquA", "summary": "This paper addresses the challenge of reconstructing 3D humans from single images, specifically focusing on \"hard poses\" (e.g., dynamic or acrobatic) where existing methods often fail. The paper's main contributions are twofold: 1) The proposal of DRPOSE15K, a new synthetic dataset constructed using an existing motion dataset and a pose-conditioned video generative model, which is designed to have higher pose diversity than previous datasets. 2) The introduction of DRPOSE, a direct reward fine-tuning algorithm that post-trains a multi-view diffusion model to better align with the diverse poses in the new dataset. The stated goal is to improve the quality and pose accuracy of 3D human reconstructions, particularly for these challenging in-the-wild cases.", "questions": "1. Why was a 2D skeleton-image-based alignment chosen for the pose reward? Why not use a more direct 3D-based reward, such as using a pre-trained HMR model to estimate SMPL parameters from the generated images and then applying a 3D joint loss? Is there an ablation study demonstrating that the skeleton-based approach is superior?\n2. Why do the quantitative and qualitative results for the PSHuman baseline appear significantly worse in this paper than those presented in the original PSHuman paper and its official project page?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- **Task:** The paper focuses on \"hard poses,\" which is a core, unsolved problem for most single-image 3D human reconstruction methods.\n- **Dataset:** The creation of the DRPOSE15K dataset, which features a measurably higher diversity of poses compared to existing 3D human datasets, is a valuable and positive contribution to the field.\n- **Idea:** The core concept of using direct reward fine-tuning to improve a multi-view generator's alignment with specific poses is intuitive.", "weaknesses": "**Major:**\n\n1. **Questionable Novelty & Base Model Choice:** \nThe paper's core motivation is undermined by the choice of MIMO as the base pose-conditioned video generator, which produces results with poor multi-view consistency. This is surprising, as other recent models cited in the paper (e.g., the finetuned-MVChamp in IDOL (Instant Photorealistic 3D Human Creation; CVPR’25), original Champ, MimicMotion, SV3D) appear to offer superior texture and consistency. This raises a critical question: **If a stronger, state-of-the-art video generator were used, would the proposed DRPOSE fine-tuning still be necessary?** To validate the generality and necessity of the reward fine-tuning, the authors should test their method on a stronger video generation backbone (e.g., Champ) and present additional results.\n\n2. **Missing Critical Baseline:** As mentioned before, the paper fails to compare against IDOL, which is a highly relevant and important baseline. IDOL also employs a similar concept of data synthesis using a generative video model and achieves state-of-the-art results.\n3. **Extremely Poor Qualitative Results:** The visual quality in the teaser (Fig. 1) and other figures shows severe texture and color inconsistency across views. \n4. **Missing Ablation Study:** \n    - The primary technical contribution is the reward fine-tuning (DRPOSE) to improve the multi-view generator. However, the paper lacks an ablation study that *isolates* the effect of this fine-tuning. There should be a direct evaluation (using video generation metrics and pose alignment metrics) of the multi-view generator *before* and *after* the DRPOSE fine-tuning, disentangled from the final reconstruction results, to prove its efficacy.\n    - The paper does not provide an ablation study (either qualitative or quantitative) on the impact of the KL divergence regularization term ($L_{KL}$). I’m wondering how much this component contributes or what happens if it is removed.\n5. **Dataset Realism:** The textures on the synthesized humans in DRPOSE15K look non-realistic. Does this lack of texture fidelity in the training data limit the model's ability to generalize and produce realistic textures for in-the-wild test images?\n\n**Minor:**\n\n1. **Inadequate Literature Review:** The paper fails to cite or discuss an important category of recent, high-quality optimization-based methods (SDS-based), including:\n    - TeCH: Text-guided Reconstruction of Lifelike Clothed Humans\n    - Human-SGD: Single-Image 3D Human Digitization with Shape-Guided Diffusion\n    - GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data\n    - Relevant Gaussian-based reconstruction work, such as the aforementioned IDOL, is also omitted.\n2. **Methodological Unclarity & Typos:** \n    - The method description is unclear at points (e.g., the sudden introduction of $x_0$ in L295 without clear definition).\n    - L174: \"kmage\" should be \"image-to-multi-view\"\n    - L231: \"Algrotihm\" should be \"Algorithm\"\n3. **Insufficient Efficiency Analysis:** In Appendix A.4, a total latency is reported. This would be more useful as a detailed time breakdown for the two main stages: 1) multi-view video generation and 2) human carving/reconstruction.", "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)