NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses
Authors: Jing Wen
Alex Schwing
Shenlong Wang
Venue: NeurIPS 2025 poster ()
Keywords: Human modeling; generalizable rendering
Primary area: applications

Abstract: We tackle the task of recovering an animatable 3D human avatar from a single or a sparse set of images. For this task, beyond a set of images, many prior state-of-the-art methods use accurate “ground-truth” camera poses and human poses as input to guide reconstruction at test-time. We show that pose‑dependent reconstruction degrades results significantly if pose estimates are noisy.
To overcome this, we introduce NoPo-Avatar, which reconstructs avatars solely from images, without any pose input. 
By removing the dependence of test-time reconstruction on human poses, NoPo-Avatar is not affected by noisy human pose estimates, making it more widely applicable. Experiments on challenging THuman2.0, XHuman, and HuGe100K  data show that NoPo-Avatar outperforms existing baselines in practical settings (without ground‑truth poses) and delivers comparable results in lab settings (with ground‑truth poses).

NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses
Jing Wen Alexander Schwing Shenlong Wang
University of Illinois Oaobana-Champaige
[jw116, aschwing, stenlong)oillinoiss.co https://went .wi.j..gttt....... Sathuu..ooNNoooAAvtttee
Abstract
We tackle the task of recovering an animatable 3D human avatar from a single or a sparse of images. For this task, beyond set of images, many prior state-of-the- art methods use accurate "ground-truth" camera poses and human poses as input to guide reconstruction at test-time. We show that pose-dependent reconstruction degrades results significantly if pose estimates are noisy. To overcome this, we introduce NoPo-Avatar, which reconstructs avatars solely from images, without any pose input. By removing the dependence of test-time reconstruction on human poses, NoPo-Avatar is not affected by noisy human pose estimates. making it more widely applicable. Experiments on challenging THuman2.0, XHuman, and HuGe100K data show that NoPo-Avar outperforms existing baselines in practical settings (without ground-truth poses) and delivers comparable results in lab settings (with ground-truth poses).
1 Introduction
Animatable human rendering aims to 1) reconstruct an animatable 3D representation from given images, and to 2) synthesize novel views of possible novel human poses. The task is of great utility VR/AR applications. Recent advances rendering techniques have significantly improved the realism and fidelity of the rendered results.
Concretely, recent generalizable human rendering approaches 1-10 reconstruct an animatable representation in a single deep net subsecond levels without compromising the rendering quality. In addition, large-scale training enables these methods to perform well even with very sparse inputs. These advances make generalizable human rendering more practical for real-world applications compared to per-scene optimized methods. Despite the pros, most generalizable human rendering approaches assume that accurate camera poses and human poses are available for reconstruction at test-time, These input poses serve as strong guidance in locating the correspondences and gathering the aligned image features. However, the use of accurate input poses during test-time reconstruction introduces a challenge, as illustrated in Fig. M we assess the dependence of the rendering quality test-time input pose quality used for reconstruction, either by 1) injecting Gaussian noise of different standard deviations into accurate ground-truth test-time input poses used for reconstruction: or by 2) using predicted pose for reconstruction at test-time. For the results shown Fig. [ we always used ground-truth -ruuh poses for test-time rendering. The noisier the test-time poses used for reconstruction, the worse the rendering quality the methods achieve. For in-the-wild scenarios where poses are estimated, the performance of existing methods degrades significantly, as shown in Fig. [[a) horizontal axis label "pred". This sensitivity to input poses is not a desirable property. Ideally, we expect the method to produce consistent and high-quality results, no matter the quality of the input poses reconstruction.
pass. This significantly speeds up the reconstruction to
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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160
Inpur
160
160
FDD
130
Outger
110
864.07
Sering
LIFo-GoM
Ours
(a) Rendering quality vs. input-pose noises
(b) Qualitative comparisons
Figure 1: (a) Sensitivity to input pose noises. Previous methods [TO, 28 take camera poses and human poses inputs. We measure their sensitivity to input poses by injecting Gaussian noise of different standard deviations or using a predicted pose. (Averaged over 5 runs for Gaussian noise; std are multiplied by 3 for better visualization.) (b) Comparisons
rendering quality. With the predicted inaccurate input poses, LIFe-GoM cannot produce high-fidelin rendering. In contrast, our methods, which does not take any poses as inputs, produce high-quality rendering.
To achieve this, we develop NoPo-Avatar, which completely eliminates the dependence of the animatable avatar reconstruction on camera poses and human poses. Hence, reconstruction and the subsequent rendering is not affected by the quality of the input poses used for reconstruction, as shown in Fig. [ a). Importantly, we represent our reconstruction in the canonical T-pose, which can be animated to arbitrary novel poses without any post-processing. To obtain the canonical T-pose representation, we design dual-branch model that captures observed details and inpaints missing regions. Specifically, our model consists of two types of branches, a template branch and image branches. The template branch starts from an encoding of the shape in the canonical pose and outputs Gaussians relative to the average SMPL-X template in T-pose. The image branches predict pixel-aligned Gaussians (splatter images) in the same coordinate system the template branch. Not only can we model the fine details in the observed input images via the spatter images obtained from the image branches, but we can also inpaint unseen regions via the template branch. For this, we adopt an encoder-decoder architecture, similar to NoPoSplat [34] the template and the input images are independently embedded in each encoder and interact with each other via cross- gross-attention in the decoder. Our model is trained end-to-end using photometric losses and auxiliary regularization Gaussians' 3D positions as well as linear blend skinning weights. We note that recent works such as LHM [16] and IDOL (37) scale training improve generalization, and, like our method, eliminate the need for pose data. However, unlike IDOL and LHM which operate on a single input image, support multiple input views. More importantly, we introduce dual-branch design. In contrast, LHM and IDOL rely solely on a template branch, which subtly differs from ours in the template encoding and the fusion of template embeddings and image features. We find the image branch enhances reconstruction of details in observed regions.
We evaluate our method THuman2.0, XHuman and HuGe and compare the state-of- the-arts. Our method reconstructs the avatars in high quality without any pose priors, significantly outperforming state-of-the-arts that use predicted poses, as shown in Fig. [b), and on par with those that use ground-truth poses.
Our contributions are twofold:
We propose NoPo-Avatar, a novel model that reconstructs animatable human avatar given only input images. Our model does not use camera and human poses. We demonstrate that the reconstruction obtained without camera and human poses leads to high- quality novel view and novel pose rendering.
2 Related Work
Generalizable human rendering from sparse inputs or single image. Generalizable methods learn inductive biases from large-scale datasets, enabling use these methods case of sparse inputs, or even a single image. We categorize methods tackling this task into one-stage and multi-stage
methods. Multi-stage methods 22 2 12
25) usually generate multiview images or videos using a 2D generative model such diffusion model, and then reconstruct the 3D representation

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from the multiview images. One-stage methods adopt a feed-forward neural network to produce the 3D representation 8
9
36
10
28
38 33
37
or directly generate the representation with a
diffusion model given single image the condition 32) Among these works, several 4
9
28 16
38 reconstruct in canonical pose space, which enables animatation without post-processing such as skeleton binding. Our approach is one-stage method and requires only a single feed-forward pass for reconstruction. We also reconstruct in the canonical pose space to support animation.
To maximize the use of human priors, most of the recent work 4 posed SMPL [ or SMPL-X [4 meshes as input and assumes that accurate human poses and camera poses are available during test-time reconstruction. The SMPL/SMPL-X poses are used to sample aligned features from the inputs explicitly or implicitly. While this assumption eases the task, it also limits generalization to in te-thh-wild subjects, as accurate pose estimation either takes too long or estimated poses are not accurate enough. More recently, RoCSSpla (30 tackles the task of generalizable rendering with inaccurate pose estimation. Differently we eliminate the need for both human pose and camera pose altogether and only operate on images and subject masks. Consequently, our method is not only insensitive to the accuracy of input poses, but also saves the time required for pose estimation.. The very recent two works IDOL [37 and LHM [16] also consider reconstructing in the canonical pose space without use of human poses and camera poses. Our method differs from IDOL and LHM in two main ways: 1) Our method operates on any number of input images, while IDOL and LHM only address the single image setting. leaving extension to multiple images open. 2) We predict two sets of Gaussians, one in the projected UV space and the other aligned with all foreground pixels in the input images, while IDOL and LHM only predict Gaussians in SMPL/SMPI-X's projected UV space. This enables our method to model the observed details.
38
33
takes the
Generalizable scene rendering without pose priors. Our setup aligns with recent efforts in generalizable scene rendering without pose priors. Note that pose priors here refer to camera poses only. The success of DUS(3R [26] sheds light pixel-aligned geometry estimation without knowledge of the relative camera poses. This idea was adopted by generaliable rendering since inaccurate camera localization can potentially lead to corrupted renderings.
While the above works reconstruct the scene in one input image's 's coordinate system, our task is inherently more challenging: the reconstruction is in a canonical T-pose which differs from the human poses in input images. Use of the T-pose reconstruction important as it enables us to animate new poses without post processing.
3 NoPo-Avatar
Given input images [L, I,, E '''''w'' xi''xw>xx x3 and masks [M, M, E (0, 10 xw' indi-
cating the pixels which correspond to a human subject, we aim to synthesize novel views/poses given target camera extrinsics E, intrinsic K, and the target human shape and pose P (B, 9). Here, N is the number of input images, and H and W denote the height and width of the input images. The human shape ,3 and pose 0 are represented in SMPL-X's format. Importantly, unlike prior work [36] [10 288, our method does not use any human poses and camera poses of the input images.
To achieve this goal, we develop NoPo-Avatar, which consists of two modules: reconstruction and rendering. Given target camera intrinsics K, extrinsics E, and human poses P, the rendering module (Render) computes the target image and alpha opacity mask:
(I, = EG;
(I)
from the reconstructed canonical 3D T- T-pose representation G. It is the output of the reconstruction module (Recon), which operates on input images and subject masks, i.e.,
G - G u G Recon ([n) (,)).
The reconstructed 3D T-pose representation & includes two sets of Gaussians represented as splatter images 22]: Gz {G (H w" from the template branch and G (G/ n.ig 1. H² w fmmmjm
from
1j=1
the image branches. We will provide details regarding the two branches Sec. 3.1 H WT and
H', T are the height and width of the template branch and image branches, respectively. Each element of the splatter image consists of the following components:
G (H,", S r o. h w.).
(3)
3

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Canoriall
Template
D
cross attention
Image Token
E
D
shared weight
Image Token N
attention
Animation rendering
E
D
Input Images (w/o poses) VIT
Encoder
Decoder
Head
Per-Branch Gaussian Avatar
Final Avatar & Novel Pose/ View Synthesis
VIT
DPT
Figure 2: Model architecture of the reconstruction module. The reconstruction module reconstructs the canonical T T-pose representation solely from images. follows the encoder-decode. structure and consists of two types of branches; a template branch and image branches. We show two views of the predictions of each branch: the splatter images the 2D format and their visualizations in 3D. Gaussians predicted from all branches are combined and fed into the articulation and rendering.
which refers to either G or n,ij. and subsumes set of variables: μ.a" E IR is the mean of the Gaussian at location "." in branch "*" S. E ER3 and r E E are the scale and rotation representated via quaternions, of the opacity, h- E De refers the spherical harmonics of degree Deg, w. R denotes the LBS weights assigned to the bones with O being the number of bones.
We detail the reconstruction module in Sec. 3.1
the articulation and rendering module in Sec. 3.2
and we describe the training losses in Sec. 3.3
3.1 Reconstruction
The reconstruction module (Recon), defined in Eq. (2) reconstructs the Gaussian primitives Ç in the canonical T-pose space given N input images {I, I and the corresponding subject masks (M, To achieve this we use the architecture illustrated in Fig. 2 Inspired by DUSt3R and its architecture; the encoders embed each input into tokens independently, and the decoders exchange information between inputs. Lastly, we use regression head predict the Gaussian primitives from the tokens.
follow-ups [26] 34 23), we adopt an encoder-decode
Our model consists of two types of branches: the template branch and image branches, one for each input image I,, with n (I....,N .V. Abstractly, the template branch captures the overall structure of the human body and reconstructs the unseen regions of the subject, while the image branches predict pixel-aligned Gaussians that focus only on the visible regions in the input images, yielding fine-ge fineggrined but incomplete reconstruction.
The template encoder is intended to capture articulated human-shape knowledge agnostic to subjects identity here, the average SMPL-X template T -pss.. To this end, we simply implement it as learnable embedding map F? E R H xW3 xC where HJ and WT denote the embedding's height and width, and its channel dimension. Since this embedding takes no input, is identical for all subjects and independent of the input images. The N image encoders share weights and embed each input image I, into tokens with ViT-based architecture, i.e., F! %/o Enc" (I,) x xC. with NY.
Encoder:
Decoder: The ViT-based decoders exchange information across all branches. Formally, let F denote the tokens in the template branch, and let F' refer to those in the n-th image branch, while E {1,.... B} indicates the b-th decoder block. We compute the tokens as follows: At the b-th decoder stage, the decoder block updates each own feature to all other features from stage b - 1. Information across images and between images and the template exchanged and implicitly aligned- this is key to enabling pose independence and allowing the template "inpaint" missing content in unseen regions. Note, we employ separate
cittuure-iiherr F or F!. Fy, cross-attending its

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decoder blocks for the template and image branches, however, all N image image-branch branch decoders are multi-layer transformers with shared weights.
The last modules are the DPT-based 17 prediction heads. As before, we use different heads for the template branch and the N image branches. In the template branch, the network predicts residuals relative to the SMPL-X T-pose template in the average shape. When added the rasterized template in UV space, this yields G² in Eq. (2). The image branches are rather straightforward: in the n-th image branch, we directly predict the Gaussian primitives {G 1 w in Eq. (2). except that multiply the predicted opacities by the input subject mask to exclude the background pixels from rendering. Here, H' and W are the height and width the input image. That is, predict one Gaussian for each foreground pixel in the input image, ensuring that all visible fine-grained details are captured by the representation. Note that the set of Gaussians in the image branches g' is in the canonical space (T-pose), not the space of the input poses, making independent of the human poses in the input images.
Prediction head:
i=1
Finally, we merge the predicted template and image Gaussians into our final reconstructed avatar by taking the union of the two sets: G = G UG'.
3.2 Articulation and Rendering
We now describe how to render the target image using our recovered avatar G, given target ex- trinsies E, intrinsics K, and human shape and pose P (3.0). We use two steps: articulation and splat rendering. In the articulation step, we warp the canonical T-posed Gaussian avatar G into an articulated Gaussian under the target pose via linear blend skinning, i.e., Gp = LBS(G; ( P), where the LBS weights for each Gaussian are part of the reconstruction module's output (shown Eq. 33) Given the warped Gp, we render the image and its mask via Gaussian splatting, (I, M) SplatRender(Gp: E, K). Hence, the Render function defined in Eq. ( can be written as: Render(G; E, K, P) = SplatRender (LBS(G; (G; P); E, K). We discuss the details of this stage and the justification of the design choices in the appendix.
3.3 Training losses
The reconstruction and rendering modules are end-to-end trainable. During training, we assume that we have the corresponding camera poses and human poses for the target ground-truth images, which are only used for rendering during training. Neither at training-time nor at test-time does reconstruction depend any poses of the input images.
Our training loss is
L Liss + Olippps LApips Orchamfer Lehamfer + Oproo Otbss Libs;
where az, hyper-parameters.
Limse and Lipips are the MSE loss and the LPIPS loss between the rendered image and the ground truth Ig. Lehamfer is the chamfer distance between the Gaussians from the template branch and the image branches GD and G².
We use two additional losses. First, the projection loss Lproj encourages each image-branch Gaus- sian can well explain its corresponding image. It consists of two terms: 1) MSE and LPIPS between the n-th input image and the rendering using only Gaussians from image branches. 2) l distance between each pixel (i, and the projected mean of its predicted Gaussian G G
(i.j) Project [LBS( w wh P); K, [|2 Here, LBS(H:; w, P) warps via linear blend skinning with weights w under pose P. and Project(p;; K, E) projects the 3D point to 2D using intrinsics K and extrinsics E. Second, the LBS loss Libe. which encourages the predicted LBS weights w." in Eq. 3 to follow the pseudo LBS weights obtained from the SMPL-X mesh provided in the training data. We call the LBS weights "pseudo" as they are rasterized from the SMPL--X mesh without clothes and are not strictly aligned with the clothed bodies.
5

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w/ ground-truth input poses
w/ predicted input poses
w/o imput poses
Ground truth
GHG
LIFe-GoM
GHG
LIFe-GoM
Ours
Figure 3: Novel view synthesis from sparse input images THuman2.0. Our approach performs on par with the state-of-the-art the lab setting (with ground-truth input poses in the reconstruction phase in test-time). Sometimes, ours even captures sharper details. In the real setting (with predicted input poses in the reconstruction in test-time), the rendering quality of GHG and LIFe-GoM is largely decayed. However, our approach without pose priors does not suffer from the bad poses.
4 Experiments
4.1 Experimental setup
Datasets. We train our model on THuman2.0 335
THuman2.1 35 and HuGe100K (37]. For
evaluation purposes, we adopt THuman2.0, HuuGe00KK and XHuman (21] We follow GHG's split [10] THuman2.0. On HuGe100K, directory into 10 validation subjects, 50 test subjects and the rest for training. Please see the appendix for details.
the scripts provided by IDOL 37 and split each
Baselines, For novel view synthesis from sparse view images we compare to GoMAvatar 27 3DGS- Avatar [15), iHuman 113,, NHP g NIA [9] GHG [TO), and M..M-Go.M (88 on T'Human2.0. We use three input images in this task. Note that all baselines use camera poses and human poses as input for reconstruction in test-time, while do not. Among the baselines, GoMAvatar, 3DGS-Avatar and iHuman require to be optimized for each subject, while others and our method are generalizable. We evaluate two test-time settings: 1) Use of human poses predicted by the state-of-the-art SMPL-X prediction model MultiHMR [I for test-time reconstruction, which is a realistic real-world setup: 2) Use of ground truth human poses provided by the dataset for test-time reconstruction, which is not realistic real-world setup. but nonetheless insightful. The target rendering poses in bost-time are always ground-truths from THuman2.0. We also evaluate on the XHuman dataset for cross-domain novel view synthesis and novel pose synthesis, which we defer to the appendix due to the page limit. For the task of novel view synthesis from a single image, we compare to IDOL. 7 and LHM [16] on HuGe 100K. Like our approach, both methods eliminate the need for input poses during the reconstruction phase at test-time. We train IDOL and our method with the same training set on HuGe 100K. Since LHM does not open-source the training script and the training dataset, we are unable to compare fairly. Hence, we directly assess the released checkpoints on 100K's 's test set.
Evaluation metrics. We assess PSNR. LPIPS*, and FID following GHG [10] on THuman2.0 [35). XHuman 21). and HuGel008 (37]. We additionally compare the reconstruction time, i.c., the time spent to compute the 3D representation given input images.
4.2 Comparisons with baselines
Novel view synthesis from sparse images. We evaluate novel view synthesis from sparse images on the THuman2.0 dataset in Tab. [ using three input views. Other than input images and subject masks, for reconstruction at test-time, GHG and L.Fe-GoM also use the predicted SMPL-X poses from MultiHMR [. the state-of-the-art SMPL-X predictor. For rendering, all methods use the ground-

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Table Comparisons to novel view synthesis from sparse input images on THuman2.0.. All the baselines take the predicted poses in reconstruction in test-time from MultHHMR [I] as inputs. We compare in the setting no test-time optimization, only optimizing the camera poses, and optimizing both camera poses and human poses in test time. Our approach, without pose priors, does not suffer from the errors in the predicted poses. Therefore, ours significantly outperforms the baselines.
w/o test-time optim.
w/ test-time cam pose optim.
w/ test-time all pose optim.
PSNR
LPIPS |
FID,
PSNRT
LPIPS"
FID/
PSNR1
LPIPS'
FID!
GHG
16.96
185.67
71.46
19.50
160.52
70.37
LFfe-GoM
19.70
146.19
63.34
20.52
142.53
62.78
22,59
130.71
60.74
Ours
22.49
105.45
42.19
22.94
103.94
42.25
25.33
92.32
39.66
Table 2: Comparisons on novel view synthesis from sparse input images in reconstruction in test-time on THuman2.0. All the baselines take the ground-truth poses as inputs, which is an unrealistic setting in real-world applications. We use three images as inputs. We compare to baselines w/o test-time pose optimization in the first block and w/ test-time pose optimization in the second block, where we mark the methods with stars. Our approach, without any pose priors, achieves comparable PSNR and better LPIPS" and FID than baselines with ground-trurt input poses.
Method
PSNR
LPIPS'
FID
GoMAvatar
27
23.05
133.98
87.51
3DGS-Avater
15
21.25
160.48
157.21
iHuman
13
22.77
131.67
101.70
w/o test-time
NHP 8
23.32
184.69
136.56
optimization
NIA 9
23.20
181.82
127.30
GHG [10
21.90
133.41
61.67
LIFe-GoM
28
24.65
110.82
51.27
Ours
22.49
105.45
42.19
w/ test-time
LIFe-Go.M
28
25.87
108.67
50.78
optimization
Ours*
25.33
92.32
39.66
truth camera poses and human poses provided by the dataset. Due to potential misalignments in scale and human pose between the predicted poses (used during reconstruction) and the ground-truth poses (used for rendering), we further conduct test-time pose optimization. Specifically, optimize the larget rendering camera and human poses while keeping the reconstructed representation fixed. This procedure is only used for evaluation purposes. Notably, since GHG is designed solely for novel view synthesis and lacks support for animation, it cannot accommodate changes in human pose during test-time optimization. Accordingly, we report three sets of numbers: w/o test-time pose optimization, w/ test-time camera pose optimization, and w/ test-time camera and human pose optimization. Here, "pose" refers to the target pose used in rendering. Our approach consistently outperforms GHG and LIFe-GoM by a margin in all evaluation protocols. We decrease LPIPS' by over 35 points and FID by over 20 points compared to LIFe-GoM. We further compare with the baselines qualitatively in Fig. When the input poses are insufficiently accurate, the baseline methods struggle to reconstruct high-fdelity details, particularly in fine- mee-griined regions such the face and feet. In contrast, our approach remains unaffected.
To complete the quantitative comparison, we also report the standard evaluation setting on THu- man2.0 10 28 in Tab. 2 poses for reconstruction and rendering are both ground truths provided by the dataset. Note that this setting unrealistic in real-world applications, since the ground-truth input poses are not available for reconstruction at test-time and must be predicted by off-the-sheelf tools. Even without any pose priors as inputs, our approach improves upon methods that use ground-truth poses in LPIPS* and FID. Our PSNR is slightly worse than the baselines. This is because PSNR focuses more on pixel-level accuracy. The pose priors provide better alignment between the canonical reconstruction and the target poses (same as input poses). Test-time optimization for target poses in rendering can resolve the potential ambiguity The qualitative comparisons in Fig. 3] shows that our method can 1) capture details better than the baselines, e.g., the prints on the back of the second person; and, aided by the two-branch design, 2) improves inpainting of unseen regions, e.g., the pants in the first example.
our approach, as shown the bottom part of Tab. 2
7

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Table Comparisons on novel view synthesis from a single image on HuGe100K. All methods do not take input poses in the reconstruction phase. Our approach achieves better PSNR, LPIPS* and FID compared to IDOI.. We are not able to fairly compare to LHM due to missing training scripts of LHM. However, our reconstruction is much faster than LHM.
Method
PSNR
LPIPS' 4
FID
Reconstruction time!
LLHM-500M [6
17.48
129.63
25.65
2.69s
LHM-1B IB [6
17.48
128.85
24.72
7.41s
IDOL
337
20.89
111.68
16.91
311.89ms
Ours
23.15
90.63
15.56
321 1.5mss
In terms of the reconstruction time, our method needs 1.32s on an NVIDIA A100 when processing three input images of resolution 1024 x 1024. This is slightly slower than LIFe-GoM's 908ms, but still faster than the fastest per-scene optimization approach, iHuman, which requires more than 7s.
Novel view synthesis from a single image. For novel view synthesis from single image compare to two recent works, IDOL [37] and LHM [16]. All three methods are similar in three aspects: 1) They do not require poses as input for reconstruction; 2) They need SMPL- X shape and pose for rendering; 3) The recon- structions are pre-defined canonical pose.
We split HuGe100K (37] into training, valida- tion and test set using the scripts provided by IDOL, We report the performance on the test set Tab. 3 and qualitative results in Fig. + We use the same training setting for IDOL and
Ground truth L.HM-BB
IDOL
our method. Since LHM training code wasn't available prior to the submission, we are unable to conduct a fair comparison. Therefore, we directly apply the pretrained weights provided by LHM to HuGe ' test set. Our method improves upon IDOL, especially in PSNR and LPIPS* Benefitting from the image branch, our method captures observed details better. To verify, assess results HuGe 100K data separately for all-observed views (front-facing) and for views with little/no overlap with the input image (three back views, camera rotated by at least 120 degree) in Tab. 4 The larger gain in all-observed views than views with little/no overlap with the input image (in LPIPS* -38.72 vs. -99.37 over IDOL, -56.35 vs. -37.84 over LHM) highlights efficacy of our image branch in capturing observed details. We also measure the reconstruction time, i.e., the time taken to compute the canonical representation. Our method uses 321 1588m,, taking a single image of resolution 896 x 640 as input. This is similar to IDOL, and much faster than LHM.
Figure 4: Comparisons novel view synthesis from a single image on HuGel00K. Our model details better than IDOL and LHM. Meanwhile, it can also reconstruct the challenging clothes, such long dresses.
We further conduct the experiment of the single-view setting on THuman2.0 in Tab. [ We use white background following IDOL's default setting. Same as on HuGel BeGGe10KK, 10KK, we train our approach and IDOL on T'uuman.00 while not finetuning LHM. Our approach significantly outperforms IDOL and LHM on PSNR, SSIM and LPIPS*
4.3 Ablation studies
We demonstrate the effectiveness of the key design choices. We use THuman2.1 as the training set and 100 held-out subjects from THuman2.0 as the test set in this section.
A Ablations on the template branch and image branches. Our model consists of two branches: single template branch that injects prior knowledge about the human and inpaints the missing parts, and N image branches that output pixel-aligned Gaussians ensure the fine aine-graieed details observed in the input images are represented. We demonstrate the importance of both types of branches in Tab. and Fig. leett). When input images are very sparse, e.g., one single input image (N = 1), the template branch plays a vital role in inpainting large unseen regions. This is shown in the first block of Tab. 6 the model with only image branches performs the worst, especially in LPIPS* and FID, as it fails to inpaint the missing regions; the model with only a template branch yields results similar to
8

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Table Comparisons on novel view synthesis from single image dividing views all-observed views and views with little/no overlap with the input image on HuGe100K. We split the test views by their overlap with the input image. We evaluate approaches on all-observed views (front-facing, same view as the input images) and views with little/no observation (three back views, camera rotated by least 120 degree from the input views). Compared to the baselines, our approach gains larger in all-observed views than views with little/no overlap with the input image highlights efficacy of our image branch in capturing observed details.
All-observed views
Views with little'no observation PSNR
Method
PSNR
LPIPS*
FID
LPIPS'
FID
LHM-IB [6
18.56
105.86
23.25
17.17
138.34
37.27
IDOL 37
23.16
88.23
20.30
20.38
119.87
26.99
Ours
26.64
49.51
14.81
22.53
100.50
24.14
Table 5: Comparisons on novel view synthesis from a single image on THuman2.0. All methods do not take input poses in the reconstruction phase. Our approach achieves better PSNR, LPIPS* and FID compared to IDOL.
Method
PSNR1
LPIPS
FID
LHM- IB 16
22.03
70.31
63.34
IDOL
37
23.47
66.62
83.37
Ours
24.64
49.69
34.82
the model with all branches. When we increase the number of input images 3, the image branches model most of the regions in better detail. Using the template branch only fails to capture the fine textures, hence performing the worst FID. The model with both types of branches attains the best overall results across different numbers of inputs.
Ablations on auxiliary losses Lproj and Libs in Eq. (4. We report the quantitative results in Tab, 7 The projection loss Lpnj encourages the image branches to predict pixel-aligned Gaussians so that the observed details modeled. Without the project loss, the image branches do not output any visible Gaussians. Only the coarse template Gaussians are used in the rendering, which results in missing fine- tiniine textures, shown in Fig. middle).
Our approach reconstructs the avatar the canonical T-pose, different from poses in the input images. Together with the predicted linear blend skinning weights, this reconstruction can be easily animated to novel poses. Removing Labs does not affect novel view synthesis, but the image branches sometimes reconstruct in the pose shown by the input images instead of the canonical T-pose. Further, the model cannot learn appropriate LBS weights, affecting novel pose synthesis. We visualize the learned LBS weights and the canonical space of the corresponding image branch in Fig. rrihht)
Lehammeer in Eq. 4 speeds up convergence of the image branches at the beginning of the training, but we do not find it useful in the final model.
4.4 Zero-shot downstream tasks
In addition to novel view and pose synthesis, our model can potentially generalize to downstream tasks in zero-shot manner-thal that is, without being explicitly trained for any of them.
Part segmentation. We predict the pixel-aligned linear blend skinning weights together with other Gaussian prim- itives the image branches. The weights can converted to segmentation masks, which indicate the body part that a pixel belongs to. In the second column of Fig. 6. we showcase the parts in different colors.
Part Input image segmentation
One stage Two stagos
Pose estimation
Figure 6: Zero-shot downstream tasks. Our model can be adapted to part segmentation and pose estimation without finetuning.

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Table 6: Ablations on the template branch and image Table 7: Ablations on auxiliary losses branches. Taking N = or N = 3 input images, we train pmoj and Line- We compare three train- and evaluate our approach with the template branch only, ing losses: (a) w/o Tepnj. (b) w/o Lus image branches only and both types of branches. Using and (c) w/ all losses. Without Tppo- the both types of branches offers the best performance across image branches cannot predict pixel- aligned Gaussians. Without Libs the
different numbers of input images.
N inputs
Branch types
PSNR
LPIPS- 4
FID.
image branches fail to reconstruct in
the canonical T-pose.
Template branch only
21.36
121.46
56.01
Image branches only
21.51
134.96
66.53
All branches
21.41
124.36
57.78
Liroj Libs
PSNR
LPIPS" L
FIDI
Template branch only
22.13
108.85
47.66
/
22.09
110.37
48.98
3
Image branches only
22.03
109.23
42.05
/
x
22.18
107.28
43.17
All branches
22.23
106.98
42.18
/
22.23
106.98
42.18
LBS weightts
Canonical rendering
LBS wetghts
Canonical rendering
Templati only
Imtgee
Ground
Lirra)
Imput
Lies
Lyps
only
Figure 5: Ablation studies. Left: Ablations on the template branch and image branches. Taking
single image as input, template branch only cannot model fine details, such as the prints on the T-shirts (orange boxes). Image branches only miss unseen regions (green boxes). Using both branches offers the best overall quality. Middle: Ablation on Looj- Without Lproj. only the template Gaussians are effective in the rendering, leading to blurry results. Right: Ablation on Libs Without supervised with the pseudo LBS weights, the image branch fails to reconstruct in the canonical T-pose and to predict the correct LBS weights.
Human pose estimation. In the image branches, also predict pixel-aligned 3D points in the canonical T-pose. This naturally provides correspondences between the 3D canonical points and the 2D pixel coordinates. Using correspondences, third column of Fig. ("Poee estimation (one stage)").. Since there can be multiple valid poses that satisfy the correspondences, the poses via photometric losses, i.c.. we treat P initialized from the first stage as the optimizable parameters in Eq. (I). The results shown in the last column of Fig. 6 ("Pose estimation (two stages)")). We highlight the improved poses with orange boxes.
can optimize the body poses, shown in the
then adopt second stage for pose optimization, where optimize
5 Conclusions
We propose NoPo-Avatar, novel model that reconstructs the canonical representation from sparse input images, while not using any human poses. Our model consists of two branches: template branch that injects prior knowledge about human shapes and inpaints the missing parts, and image branches that predict pixel-aligned Gaussians to depict the fine-grained details. Our approach achieves comparable results to -htte-af-t which use ground-truth pose priors during test-time reconstruction, and significantly outperforms those that use predicted pose priors during test-time reconstruction.
Broader Impact. The proposed method can benefit AR/VR applications. Concerns remain regarding the identity and authenticity of reconstructed avatars. To address these issues, we advocate for the use of strict licensing agreements and responsible usage practices for the proposed methods.
Acknowledgementss. Work supported in part by NSF grants 2008387, 2045586, 2106825, NIFA award 2020-67021-33799, and OAC 2320345.
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Appendix - NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses
The appendix is structured as follows:
We provide implementation details in Sec.
We elaborate on the baseline setup and experimental details Sec.
B
Due to space limitations in the main paper, we clarify the experimental settings and provide additional results in Sec. C
We discuss the limitations and future works in Sec.
We also provide a webpage index. html in the supplementary. It lists videos for freeview rendering comparisons to basclines, novel pose synthesis, and cross-domain generalization.
A Implementation Details
A.1 Articulation
We describe in detail how we articulate the canonical representation into pose P (3, 0), where 3 is the shape and is the pose represented by the orientation of each joint. We assume that the canonical representation follows the skeleton of the average template shape in T-pose, i.e., the skeleton acquired by setting the shape parameters and pose parameters to all zeros in SMPL-X. When warping with pose P. first reshape the canonical representation consuming the target shape B, and then rotate the reshaped Gaussians T-pose by the pose O.
Step 1: Reshaping by B. People are of various heights and bone lengths. To reshape the canonical representation, we first compute the skeleton of the average shape and the skeleton of shape 3, both in T-pose. Then we calculate the transformation between each pair of corresponding bones in two skeletons, denoted as {st, Rí, 4, ) Here, 8; E R accounts for the scaling factor the bone length, and R2 (O(3) and 1, E R are the rotation and translation, respectively. O denotes the number of bones. In Eq. B). we define the LBS weights as w 1 RR., We then transform the Gaussian's mean , and rescale and rotate Gaussian's 's covariance:
O Σ
O Σ
11
(s; + 4., E.8
Σ w (
R. ST
S R
( (5)
ST R2 is the original covariance matrix, where R. and S are the matrix form of the predicted rotation and scale of the Gaussian. We rescale the covariance matrices accordingly to avoid artifacts due to reshaping.
Step 2: Rotating by 0. 1. and 3 are still in T-pose. We then warp by pose 0, which represents the orientations of each joint. This step is standard linear blend skinning. However, we need to convert the predicted LBS weights associated with bones w E RRO to weights associated with joints. As the skeleton is defined in tree structure, each bone connects parent joint and child joint. We simply assign the LBS weights associated with the bone to its parent joint. We illustrate this choice in Fig.
Alternatively, the reconstruction module could directly predict the canonical representation in the target shape, thereby eliminating the need for Step in the articulation. However, find that decoupling the shape from the canonical representation and retaining Step 1 is empirically important. Due to the decoupling, the canonical representation G from the reconstruction stage follows fixed skeleton, i.e., the skeleton of the average template shape in T-pose, which alleviates the scale ambiguity inherent in this task. We show the artifacts without decoupling the shape from G and Step 1 in Fig. 8 middle) and the improved rendering quality with our design choice in Fig. Bright).
At test-time, we use the predicted user-specified shape and pose 3 and 0, similar to IDOL 37 and LHH [16). These parameters are only used the articulation and rendering stage, not the reconstruction stage. Importantly, our approach remains frse-free in the reconstruction stage.
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Child joint
Child joint
Parent joint
Parent joint
Child Childjoint joint
Child joint
Child joint
LBS weights assigned to bones
LBS weights assigned
joints
Figure 7: Converting LBS weights assigned bones LBS weights assigned joints. We illustrate the LBS weights around the pelvis root. Our model predicts the LBS weights corresponding to the three bones connected to the pelvis root. The pelvis root serves as the parent joint of three child joints. The LBS weights of the three bones (left) are aggregated and reassigned to the pelvis root (right).
wio decoupling shupe from canonical
Decoupling shape from canonical space
Ground-truth
Figure Decoupling the shape from the canonical representation. Without the decoupling (middle), the model sometimes fails to reconstruct the thin structures, e.g., hands. Decoupling the shape from the canonical representation eases the reconstruction ambiguity.
A.2 Training and Inference Details
The learnable embedding in the template encoder F/' is in the shape of 16 x 16 x 1024. We use Olpips 0.05, Ochamfer 0.1, Proo 1.0, and Ctjbs 0.01 Eq.
The model pretrained from NoPoSplat [34]. To reduce training time, we first train using a low- resolution template and images. The model is first trained resolution of 256 256 for 300K iterations, then upsampled to 512 x 512 for another 300K iterations, and finally fine-tuned at full resolution for additional 50K iterations. We train for 80K iterations in the full resolution of 896 x 640 on H00.
We use batch size of 4 for all experiments THuman2.0, THuman2.1. and THuman2.1 + HuGe 100K. The training process takes roughly days, 4 days for each stage. For the comparison to LHM and IDOL HuGel00K, we use a batch size of 16 in the first two stages and 8 in the last stage.
We use two NVIDIA L40S for training at a resolution of 256 x 256, four NVIDIA L40S for 512 512 resolution training, and four NVIDIA H200 for the last stage, i.e., for full resolution training. Inference, including both reconstruction and rendering, requires approximately 24GB of GPU memory for three input images at resolution of 1024 x 1024, and around 1 11GB for a single image at 896 x 640.
A.3 Zero-shot Human Pose Estimation
We show that our model can perform human pose estimation via sallysi--b--synthssis in a zero-shot manner in Sec. 4.4 For this, two-stage optimization process.
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Stage I: Optimization with projection losses. Recall that the image branches in our model predict pixel-aligned Gaussians, which means that we obtain one Gaussian with mean Mi and LBS weights w n.a for each pixel (t, in the n-th image branch. We optimize for the human shape and pose P by minimizing (1, Project Project(LBS(PL...
n.ay. P); E) 1|2 summed over all foreground pixels, where LBS( (4; w, P) warps via linear blend skinning with weights w under pose P, and Project(/;; K, E) projects the 3D point la to 2D using intrinsics K extrinsics E. We set E to the identity matrix and let K have fixed focal lengths and principal points.
Stage 2: Optimization with photometric losses. There can be multiple valid poses that satisfy the correspondences. Meanwhile, the reconstruction from image branches is sometimes unreliable for fine-grained structures. We therefore optimize with a photometric loss in the second stage. Specifically, we treat P as the learnable parameter, initialized by the result of the first stage. We warp the Gaussians by P and fixed LBS weights, render the images, and compute the photometric loss between the rendered images and the ground-truth, which is the image we estimate the pose from in pose estimation.
As shown in Sec. 4.4 optimizing with projection losses solely already leads to good results. This side benefit from our design choice of predicting pixel-aligned Gaussians from the image branches. Adding the second stage which optimizes with photometric losses further improves the estimation. Note that optimizing with photometric losses alone does not lead to meaningful results, since it requires a good initialization.
B Experimental Details
B.1 Datasets
THuman2.0. THuman2.0 provides 526 3D scans as well as the corresponding SMPL-X parameters.
We follow GHG [10] to render them into 64 multiview images in the resolution of 1024 x 1024 and split them into 426 subjects for training and 100 for evaluation. THuman2.0 uses a special license agreemen which we follow.
THuman2.1. 1 extends THuman2.0 to ~200 scans. Note that the 526 scans from THuman2.0 O are included in 1. The newly added subjects are combined with THuman2.0's 426 training subjects as the new training set. The license agreement is the same as THuman2.0.
XHuman. XHuman consists of 20 subjects. It provides the 3D scans and corresponding SMPL--X poses of multiple motion sequences for each subject. We follow LIFe-GoM (28) evaluate the cross-domain generalization in novel view synthesis on XHuman. The pose estimator MultiHMR fails in one sample so that we cannot get the estimated input poses for the reconstruction phase of GHG and LIFe-GoM. So we remove that sample from the test set. We additionally evaluate novel pose synthesis on the XHuman dataset. For each subject, images from the first frame of a sequence are used as input. The two sequences designated as test sequences are selected as target poses for novel pose synthesis. For each target pose, we render images from three different views. XHuman uses special license agreemen(" which we follow.
HuGe100K. HuGe100K is synthetic dataset that contains more than 100K subjects. For each subject, it provides a video of 360-degree freeview rendering, camera poses of each frame and the SMPL-X parameters. We use SAM [6) to acquire the subject masks. The dataset groups the subjects into directories. We follow the scripts provided by IDOL [37] and split each directory into 10 validation subjects, 50 test subjects and the rest for training. We use frame 19 the input view and
sample 6 views as the target views for evaluation. HuGe H00K 10K uses DeepFashion's 's license F which we follow.
B.2 Pose Estimation
The baselines, including GHG 10 and LIFe-GoM the input images as input for the reconstruction phase. As mentioned in the paper, the ground-truth
(28), require camera poses and human poses of
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poses are typically not available in real applications. Instead, the poses have to be estimated by off-the-shelf tools prior reconstruction.
We choose MultiHMR [ as the pose estimator for all our experiments requiring estimated poses. MultiHMR is the state-of-the-art end-to-end pose estimator. Different from RoCiSplat (0), we do not use EasyMoCap] as the pose estimator, since Masyp requires calibrated camera poses, which are unavailable in real settings. Complex pose estimation pipelines such as the one used in ExAvatar [2]] are also not preferred. Though accurate, they take over 20 minutes to acquire the poses applications.
from a monocular video, which is impractical in
MultHHMR predicts per-image poses. Since LIFe-GoM can take images with different poses as input, we feed the poses independently predicted from each view the input poses to LIFe-Gole GHG, however, must take multiview images with the same pose. Therefore, we use the human poses predicted from one view (most frontal view) the input human pose and register the other views.
B.3 Additional Information on Test-time Pose Optimization
We perform test-time optimization of both camera pose and human pose Tab. and Tab. 2 In Tab. [ we use predicted poses for GHG and LIFe-GoM during test-time reconstruction. During rendering, we always use the ground-truths from the dataset as the target poses for evaluation. The poses in the reconstruction stage may not align with the poses in the rendering stage. For example, the predicted poses assume a tall person photographed by a faraway camera, while the ground target poses assume shorter person captured by a closer camera. We want to rule out the potential drop in the evaluation metrics due to such misalignment. Though not taking any poses in the test-time reconstruction, our approach may also have the same misalignment between the reconstruction and the rendering poses. Therefore, we optimize the target camera poses and human poses only for evaluation purposes. We similarly apply test-time optimization to method, shown in Tab. 2 and also apply it to LIFe-GoM using ground-truth input poses for a fair comparison.
C Addtional Experiments
We present additional experiments, including cross-domain generalization results, quantitative analysis of how different types of noise in input poses affect baseline performance during test- time reconstruction, and evidence that injecting noise during training does not necessarily enhance robustness to noisy poses at test time. Additional qualitative video comparisons are provided in the supplementary material at index. html.
C.1 Cross-dataset Evaluation With Sparse Images Input
We show results of experiments with different training settings in Tab. We choose three training settings of different scales: 1) THuman2.0 which has 426 subjects: 2) 1 which has 2345 subjects, around 5x the scale of THuman2.00 0; 3) THuman2.1 + HuGe 100K which has over 100K subjects, over 200x the scale of THuman2.0. Meanwhile. we evaluate on three settings: 1) In-domain evaluation on novel view synthesis: The test set is 100 subjects in THuman2.0. We call it in-domain since the training and test sets both from T.uman2.0. 2) Cross-domain evaluation on novel view synthesis: The test set consists of subjects from XHuman, which differ from the training set. 3) Cross-domain evaluation on novel pose synthesis: The test set also from XHuman, but we evaluate novel poses.
For both in-domain and cross-domain evaluation, novel view synthesis or novel pose synthesis, ours consistently outperforms LIFe-GoM with predicted input poses.
More importantly, we find that our method benefits from scaling up, especially in the cross-domain evaluation. When switching the training data from THuman2.00 to THuman2.1, our approach improves by 0.6 in PSNR for novel view synthesis and by 0.4 for novel pose synthesis in the cross- cross-domain evaluation. Similar improvements can also be observed in LPIPS* and FID scores: IPIPS* improves from 129.0 .04 to 1 19.8 .84 and FID improves from 47.58 to 41.55. However, LIFe-GoM, which incor- porates more hand-crafted inductive biases, does not exhibit such scaling ability. Equipped with an even larger dataset, such as HuGe 100K, we also observe an improvement in PSNR. But LPIPS*
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THuman2.0
THuman2.0 0.
Training set:
THuman2.0
THuman2.1 I
100K
THuman2.0
THuman2.1
100G
Ground truth
LIFe-GoM
Ours
Figure 9: Novel pose synthesis and cross-domain generalization from sparse input images using different training set sizes. Our approach scales with the size of the training set, achieving improved identity recovery, whereas LIFe-GoM does not exhibit this behavior.
Table 8: Comparison on in-domain evaluation and cross-domain generalization using different training sizes. We evaluate in-domain novel view synthesis on T'Human..0 and cross-domain novel view synthesis and novel pose synthesis on XHuman. The input poses to LIFe-GoM are predicted. Our approach improves with larger-scale training data, while LIFe-GoM does not. The lines labeled with indicate results w/ test-time pose optimization. We optimize both camera poses and human poses to eliminate the misalignment between the poses used during the reconstruction and the target poses used for rendering. In all settings, ours consistently outperforms LIFe-GoM.
Training set
THuman2.0
THuman2.1
THuman2.1 IDOL
PSNR
LPIPS*
FIDL
PSNR1 LPIPS*
FID
PSNR1 LPIPS* FID,
In-domain evaluation
THuman2.0, novel view synthesis
LIFe-GoM
19.70
146.19
63.34
19.41
147.75
62.11
19.50
154.21
67.99
LIFe-GoM*
23.01
129.56
60.68
22.86
128.34
58.96
22.65
139.30
65.77
Ours
22.49
105.45
42.19
22.23
106.98
42.18
22.41
114.14
48.69
Ours*
25.33
92.32
39.66
25.94
87.94
38.45
25.73
97.39
46.07
Cross-domain evaluation XHuman, novel view synthesis
LIFe-GoM
20.94
136.42
56.02
20,50
141.24
59,94
20.60
153.43
69.54
L.Fe-GoMM*
24.39
116.25
52.19
24.22
117.07
54.43
23.91
132.16
64.47
Ours
20.96
129.04
47.58
21.56
119.84
41.55
22.52
114.95
43.59
Ours*
25.05
100.86
41.82
26,23
91.17
37.41
26.70
92.18
40.05
Cross-domain evaluation on XHuman, novel pose synthesis
LIFe-GoM
20.95
132.86
45.22
20.49
137.62
47.69
20.56
147.52
$5.05
LIFe-Gob G
23.98
116.42
42.64
23.70
117.81
44.37
23.56
129.09
52.01
Ours
20.93
130.89
38.65
21.36
124.96
34.67
21.74
124.91
38.27
Ours* 24.79
103.09
34.68
25.48
97.81
31.79
25.76
99.12
35.13
and FID do not improve further. This is likely because HuGe100K synthesizes multiview images of avatars using diffusion models. The multiview consistency is hence not guaranteed. Inconsistency in the training data may induce blurred rendering, which eventually affects perceptual- based evaluation metrics. We showcase the scaling ability qualitatively in Fig. Since THuman2.0 and THuman2.1 only contain Asian people, our approach may not generalize well to other ethnicities. Our approach benefits from the increasing diversity in HuGe and reconstructs better in the cross-domain data. LIFe-GoM, due to hand-crafted inductive biases in its architecture, works better when trained with a small-scale dataset.
C.2 Comparison of Different Noise Levels in the Input Poses During Test-time Reconstruction
In we show that the baselines including GHG and LIFe-GoM are sensitive to noise in input poses during test-time reconstruction. We achieve this by injecting Gaussian noise or by predicting the input poses. We provide the quantitative results of injecting Gaussian noise or using predicted poses in Tab. 19, matching the results in Fig. aa). We also provide a qualitative comparison with LIFe-GoM under noisy input poses in Fig. 10 As the noise level of the input poses increases during test-time reconstruction, L.MMe performance degrades noticeably. In contrast, our method, which does not rely on pose priors, remains robust and unaffected by such noise.
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Table 9: Comparison of different noise levels in input poses during test-time reconstruction. GHG and LIFe-GoM take ippu camera and human poses during test-time reconstruction. We evaluate their robustness to noisy input poses by injecting Gaussian noise into ground-truth poses or by predicting the input poses. For each Gaussian noise level, we average over 5 runs and report the means and standard deviations. Our method, which does not take any pose priors, outputs the same result, regardless of the noise in input poses. The numbers reported in this table are the same those
in Fig. a).
Noise level Method
No noise LPIPS*
std=0.01
PSNR
FID.
PSNR
LPPPS*
FID
GHG LIFe-GoM
21.90
133.41
61.67
21.28+00.04
136.71=0.25
62.06=0.24
24.65
110.82
51.27
22.95+00.33
118.34+0.21
52.87=0.08
Ours
22.51
105.85
42.37
22.51
105.85
42.37
Noise level Method
std=0.03
std=0.05
PSNR
LPIPS*
FID
PSNR
LPIPS*
FID
GHG LIFe-GoM
19.73=:.008 20.90
150.08=0.54
64.9920.31
18.55+0.009 19.73:00.08
163.39=0.70
68,45=0.75
133.07=0.35
57.28=0.226
144.13=+0.47
63.60=0.76
Ours
22.51
105.85
42.37
22.51
105.85
42.37
Noise level Method
pred
PSNR
LPIPS*
FID
GHG
16.96
185.67
71.46
LIFe-GoM
19.70
146. 19
63.34
Ours
22.51
105.85
42.37
Input pose:
Ground-truth
std=0.01
std=0.03
sid=0.05
Pred N/A
Ground truth
LIFe-GoM
Ours
Figure 10: Comparison of different noise levels in input poses during test-time reconstruction. We qualitatively demonstrate the performance degradation of LIFe-GoM under noisy input poses during test-time reconstruction. To simulate noisy conditions, we inject Gaussian noise with varying standard deviations or use predicted poses as inputs. In contrast, our method method-relying on no pose priors remains unaffected by pose noise.
C.3 Training LIFe-GoM With Noisy Inputs
We train both GHG and LIFe-GoM using ground- truth input poses, but employ predicted poses during test-time reconstruction in the experiments discussed in the main paper and above. This raises a natural question: is the performance drop caused by the gap in input pose quality between training and testing? In this section, we demonstrate that training with noisy input poses does not improve the models' robustness to pose inaccuracies. fact, training with accurate ground -ouud poses often yields the best performance across all levels of input pose noise during test-time reconstruction.
Taking LIFe-GoM as an example, we train it using 1) ground-truth input poses, 2) input poses with synthetic Gaussian noise of std=0.01, 3) input poses with synthetic Gaussian noise of std=0.03, 4)
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Table 10: Training LIFe-GoM with noisy inputs. We train LIFe-GoM using ground- -ruth input poses, poses with synthetic Gaussian noise, and predicted poses (pred) during the reconstruction stage. Training with ground-truth poses often yields the best performance across varying levels of pose noise at test time. This indicates that the performance drop observed when using predicted poses during reconstruction not due to a seaninnggtssting gap and cannot be mitigated simply by introducing noise during training.
PSNR1
LPIPS
FID,
PSNR
LPIPS" +
FID,
PSNR
LPIPS' J
FID
Test input noise
No noise
std=0.01
std=0.03
No noise
24.13
110.12
49.65
22.95
118.34
52.87
20.90
133.07
57.28
Training input noise
std=0.01
24.11
114.97
52.87
22.74
121.70
53.85
20.67
136.17
57.36
std=0.03
23.80
118.92
55.60
22.58
124.89
56.33
20.54
139.03
59.26
std=0.05
23.38
122.18
57.55
22.32
127.71
58.57
20.41
141.24
61.39
Pred
23.38
121.66
57.34
22.32
127.44
58.22
20.65
139.44
62.38
Test input noise
std=0.05
Pred
No noise
19.73
144.13
63.60
19,70
146.19 19
63.34
Training imput noise
std=0.01
19.48
146.97
62.45
19.63
148.11
64.02
std=0.03
19.18
151.75
62.58
19.63
150.64
66.46
std=0.05
19.10
153.60
64.42
19.54
152.07
68.48
Pred
19.59
148.79
66.80
19.78
149.49
65.77
Table 11: Robustness to noisy training poses. We train our approach with noisy training poses. We add synthetic Gaussian noise the training poses.
Noise in training poses
PSNR
LPIPS
FID
No noise std=0.1
22.49
105.45
42.19
22.59
110.12
49.39
std=0.3
20.58
138.67
73.20
input poses with synthetic Gaussian noise of std=0.05 and predicted poses from MultiHMR, and evaluate each in all levels of noise. The results are shown in Tab. [0] The more noise we add to the input poses during training, the worse the performance we get across all levels of input pose noise during test-time reconstruction. Hence, the performance drop observed when using predicted poses during reconstruction cannot be mitigated simply by introducing noise during training. Our approach, which takes no pose priors, is an option to eliminate the need for accurate camera and human poses during test time. more suitable for real-world applications.
C.4 Sensitivity to noise in training poses
We use the ground-truth poses provided by the dataset during training. To analyze the robustness to poses during training, we add synthetic Gaussian noise. We report the quantitative results in Tab. [T] We find that our approach is robust some noise, e.g., Gaussian noise of std=0.1: On THuman2.00 achieves a PSNR/LPIPS truth training poses reported in Tab. 2 we further perturb the ground-trun -truth poses with more significant Gaussian noise of std=0.3, the PSNR/LPIP*/IID drops to 20.58/134 0...8//38.67773.22 We do not think robustness to the target poses in training is big concern. Collecting high-quality training data one-time effort. Once trained, the model does not rely poses for reconstruction at inference time. This is different from pose-dependent methods, which still require high-quality input poses during inference for reconstruction.
of 22.59/1 10.110.11 12/49.39, compared to 13.49/005.45/422
with ground-
C.5 Validating the identity shift
We assess identity shift quantitatively via the facial verification tool DeepFace (19 20 18), on H0Ge verifies if two images show the same person. We think this is good surrogate for identity shift. We adopt all 400 front-facing HuGel00f images and report the verified-rate, i.e., the percentage of "rendered image" "grouu"nggutttttth image" -pairs recognized the same person, and the cosine distance between the rendered face and the ground-truth -tuth image in VGG-Face's feature space
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Table 12: Validating the identity shift on HuGe100K. To quantify the identity shift, we report the verified-rate and the cosine distance between the rendered image and the ground-trute image in VGG-Face's feature space.
Noise in training poses
Verified-ratef
Cosine distance
LHM-1B
95.75%
0.30
IDOL
93.75%
0.45
Ours
95.25%
0.32
example of Semi transpurent rogions rendering
Rendered front view
Renderad back view
Hufie100K
When trained on HuGe 100K, our model sometimes produces semi- trmi-trrnsparent regions. We suspect the reason is that the data in HuGe100K lack multiview consistency. Right: Taking the front view as the only input view, our model renders sharp front view. But the back view blurry due to inpainting.
Figure 11: Limitations. Left: Incorrect hand geometry. Middle:
in Tab. 12 Our method without dedicated face module achieves verified-rate of 95.25% and an average cosine distance 0.32. LHM-1B 1B has 95, 35.75%/0.30. Note, LHM uses a dedicated face image crop additional imput and feature pyramid for improved facial modeling. Both ours and LHM outperform IDOL's 93.75% 75%/0.45. We think this shows that our model does not suffer from a severe identity shift as results comparable methods with dedicated face module.
D Limitations
Failure in modeling expressions and hands. Our model currently does not support expression retargeting. most of the input images, hands occupy very few pixels or are heavily occluded. Therefore, predicting the LBS weights and corresponding 3D locations in the canonical T-pose for each pixel in the image branches is sometimes challenging. So the hands are not as sharp as other regions, as is shown in Fig. leftt). Similar issues happen in LHM. A possible solution is to train two separate models, especially for faces and hands, respectively, compromising the training time and reconstruction speed.
Sensitivity to inconsistent training data. Our model sometimes predicts blurry results in unseen regions and semi- transparent regions on the boundary when trained on HuGe 100K. Notably, such issues do not happen when training on THuman2.0. We suspect that this is because the HuGel00K data is synthesized by diffusion models. It hence sometimes lacks multiview consistency. We showcase a HuGGe100K human subject which inconsistent in different views in Fig. middlee) As regression- based method, ours is prone to output blurry results, semi- comi-trrnsppreet regions, or small floaters on the boundary. more consistent and qughet-uualtyy training set will resolve this issue.
Blurry inpainting. Even though our model improves upon LIFe-GoM in inpainting small unseen regions, model, details in large unseen regions, e.g., the dotted pattern on the shirt in Fig. righht) Generative models are generally more suitable for hallucinating high-frequency details, which is important for reconstruction and rendering from a single image.
regression-based method, struggles to hallucinate sharp and high-frequency
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Reviews:
[{"review_id": "LkxBMbLN4N", "reviewer": "Reviewer_KMYG", "summary": "This paper introduces NoPo-Avatar, a novel method for reconstructing animatable 3D human avatars from sparse images without relying on human or camera pose priors. The proposed framework reconstructs avatars in canonical T-pose space using a dual-branch architecture: a template branch that captures global body structure and facilitates inpainting of unseen regions, and image branches that predict pixel-aligned Gaussians for visible details.", "questions": "See the weaknesses.", "limitations": "See the weaknesses.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\nI think the paper is overall well-executed — the results are solid, and the framework is intuitive and easy to understand.\n1. NoPo-Avatar eliminates the need for test-time pose and camera pose input, increasing robustness and applicability. The design choice is well-justified through both qualitative and quantitative results.\n\n2. The proposed template + image branch design is elegant and effective. Ablation studies confirm that the combination enhances both detail preservation and inpainting of unseen regions.\nExtensive Evaluation\n\n3. The paper includes comprehensive experiments against strong baselines (LIFe-GoM, IDOL, LHM, GHG, etc.) across multiple datasets and settings (with ground truth poses, predicted poses, no poses at all).\n\n# Weaknesses\n\n1. I would like to see more results on long dresses or loose clothing.\nThese cases are often challenging for avatar reconstruction, and it would help demonstrate the robustness of your method.\n\n2. For the comparison with LHM and IDOL in Fig. 4, could you clarify what the input image is for each method?\nWere the same images used across all methods? Additional detail here would improve the fairness and interpretability of the comparison.\n\n3. A recent paper has tackled a similar problem. I would appreciate it if you could include a comparison with their results.\n[1] (PF-LHM) It would help position your contributions more clearly within the current landscape of pose-free avatar reconstruction.\n\nPF-LHM: 3D Animatable Avatar Reconstruction from Pose-free Articulated Human Images", "was_revised": true, "final_justification": "The author addressed my main concern, so I will keep my original score.", "rebuttal": "Thanks for your time and feedback. We also thank the reviewer for finding our method well-justified, elegant and effective, and the results solid. We answer questions below.\n\n***QA1. I would like to see more results on long dresses or loose clothing. These cases are often challenging for avatar reconstruction, and it would help demonstrate the robustness of your method.***\n\nWe find our method reconstructs long dresses or loose clothing well particularly if samples with loose clothes are part of the training data. We showcase an example of long dresses in Fig. 4. We also provided freeview rendering of long dresses in Part 2 of the supplementary file index.html.\n\n***QA2. For the comparison with LHM and IDOL in Fig. 4, could you clarify what the input image is for each method? Were the same images used across all methods? Additional detail here would improve the fairness and interpretability of the comparison.***\n\nThe input image is the front-facing image of the subject. The same image is used for all methods and the comparison is completely fair. We will emphasize this by revising Fig. 4.\n\n***QA3. A recent paper has tackled a similar problem. I would appreciate it if you could include a comparison with their results. [1] (PF-LHM) It would help position your contributions more clearly within the current landscape of pose-free avatar reconstruction.***\n\nThanks for the reference. We are happy to cite it in the final version. Please note that the paper appeared on arXiv on June 16, i.e., after the NeurIPs submission deadline. The codes have not been released yet. We compare our approach to its previous version (LHM) in Tab. 3."}, {"review_id": "3LNH9hh51v", "reviewer": "Reviewer_VStF", "summary": "This method does not require camera pose or human pose information corresponding to the input image during the reconstruction phase, and can reconstruct drivable 3D human avatars from sparse image inputs. Paper claims it addresses the critical issue of degraded reconstruction quality in existing methods due to their reliance on inaccurate pose estimation.", "questions": "Reconstruction stability regarding standard poses. Results for complex input images (even for use cases where pose estimators trained specifically for them have problems).\n\nComparison with methods using high-precision pose input in the case of simple input images.\n\nProvide scaling experiments regarding computational cost.\n\nCurrent results are good with sparse image input, but overall there is a lot of room for improvement. With better input conditions, such as increasing the number of image inputs, what are the performance boundaries? Can it surpass existing methods?\n\nWhy use T-pose but not da-pose?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The problem addressed by the paper is important, namely the reliance on accurate pose during the reconstruction stage. By resolving this dependency, the method enhances practicality and robustness in real-world scenarios.\n2. The two-branch architecture is reasonable.\n3. The paper conducts experiments on multiple datasets, which is good. It also demonstrates the superiority of the method.\ntest-time pose optimization is interesting.\n\nWeaknesses:\n\nAlthough the paper does not require pose, it does require providing a mask. This demands on the quality of the mask.\n\nApplying a separate branch to each image may lead to an increase in computational cost.\n\nThere is a lack of complex action image input. The paper uses an internal normalization strategy instead of relying on GT pose. This is good, but it raises questions about the normalization process because it cannot guarantee overall performance compared to precise input pose or the normalization results under complex action image input.", "was_revised": true, "final_justification": "After reviewing the authors’ rebuttal and considering the reviews and responses from other reviewers, I have decided to raise my final rating.", "rebuttal": "Thanks for your time and feedback. We also thank the reviewer for finding our method superior and the test-time pose optimization interesting. We answer questions next.\n\n***QB1. Although the paper does not require pose, it does require providing a mask. This demands on the quality of the mask.***\n\nMask prediction is usually more robust than pose estimation. This is intuitive because masks are directly related to input images while poses can be ambiguous, e.g., because of clothing or occlusion. Therefore, we aim to avoid poses as input.\nMoreover, our approach is robust to the mask quality. Note that we use the predicted masks for training and inference on HuGe100K. To further evaluate the robustness, we additionally corrupt the masks by dilating with kernels of size 3 and 5. Our approach achieves a PSNR/LPIPS*/FID of 22.92/93.04/15.69 when the dilation kernel size is 3, and 22.83/95.03/15.80 when the kernel size is 5. Compared to 23.15/90.63/15.56 reported in Tab.3, the FID scores stay almost the same, demonstrating the robustness of our approach w.r.t. mask quality.\n\n***QB2. Applying a separate branch to each image may lead to an increase in computational cost.***\n\nProviding more input images increases computation but also improves quality, as is shown in Tab. 4. Note that our approach is significantly faster than LHM and comparable to IDOL, as shown in Tab. 3. Even with 3 input images of resolution 1024x1024, our approach still achieves 1.32s (Line 259), faster than LHM-500M’s 2.69s obtained with a single image of 896×640 resolution.\n\n***QB3. There is a lack of complex action image input. The paper uses an internal normalization strategy instead of relying on GT pose. This is good, but it raises questions about the normalization process because it cannot guarantee overall performance compared to precise input pose or the normalization results under complex action image input.*** \n\nWe clarify that our method is evaluated on complex action image inputs using the THuman2.0 dataset, which includes challenging poses and occlusions. Pose estimators often fail on THuman2.0, degrading pose-dependent baselines—as shown in Part 1 of our supplementary webpage. This highlights the difficulty of relying on pose estimators for precise input pose under complex motion.\nDespite not using ground-truth poses, our method achieves comparable or better performance. In Tab. 2, we compare our approach to the baselines with precise GT poses and show comparable results with better LPIPS* and FID, indicating better visual quality. Therefore, the overall performance is compelling even for complex action image input. We would like to emphasize that in the real world, input poses are either less precise, or are time-consuming (e.g., 20min) to acquire. So it is impractical to assume precise input poses.\n\n***QB4. Reconstruction stability regarding standard poses. Results for complex input images (even for use cases where pose estimators trained specifically for them have problems).***\n\nIn Fig. 1 and in Tab. 7 of the appendix, we evaluate stability of approaches w.r.t. pose quality. We examine the stability by adding Gaussian noise with different standard deviations or by using predicted poses. Our approach is comparable to the baselines with _ground-truth_ poses as inputs. Please see QB3 for results on complex input images.\n\n***QB5. Comparison with methods using high-precision pose input in the case of simple input images.***\n\nFor simple input images where pose estimation is less likely to fail, our approach achieves superior visual quality compared to pose-dependent approaches. We compare our approach to LIFe-GoM on a subset of HuGe100K’s test data which contains 400 subjects in the simplest A-pose. Both methods are trained on THuman2.1+HuGe100K. Our approach achieves a PSNR/LPIPS*/FID of 22.69/98.80/20.65, significantly outperforming LIFe-GoM’s 18.28/121.63/26.12. We find that LIFe-GoM predicts spatially inconsistent colors due to view-dependent effects in the input images, an issue our method avoids. Additionally, we find LIFe-GoM performs worse than ours in inpainting unobserved regions.\n\n***QB6. Provide scaling experiments regarding computational cost.***\n\n1\\) Scaling the training data as in Tab. 6 in the appendix won’t increase the computational cost during inference. 2) Increasing the number of input images from 1 to 3 increases reconstruction time from 495ms to 1.32s on one NVIDIA A100 when using a resolution of 1024x1024. But it also improves quality, e.g., LPIPS* from 124.36 to 106.98, as shown in Tab. 4.\n\n***QB7. Current results are good with sparse image input, but overall there is a lot of room for improvement. With better input conditions, such as increasing the number of image inputs, what are the performance boundaries? Can it surpass existing methods?***\n\nOur paper focuses on the setting of sparse input images as it is more convenient for real-world applications. In this setting, we improve upon state-of-the-art methods. Our approach also demonstrates better performance with 3 input images compared to using a single image (Tab. 4). We expect further quality improvements with more input images since there is less to hallucinate. Our approach can operate on as many as 18 input images while using ~97GB GPU memory.  Unfortunately, due to the limited resources, we are unable to finish this training.\n\n***QB8. Why use T-pose but not da-pose?***\n\nWe use T-pose since it is the SMPL-X defined canonical space and LIFe-GoM also adopts it. Due to the limited rebuttal time, we are not able to finish the ablation. We suspect a different format won’t make a big difference, as the poses are related via a transformation that can be learnt by our deep nets."}, {"review_id": "EqcwYNpe2R", "reviewer": "Reviewer_LYXM", "summary": "This paper proposes a method called NoPo-Avatar, which aims to reconstruct animatable 3D human avatars from single or sparse image sets without relying on human pose information. By eliminating the dependence on input poses, this method effectively addresses the issue of significant quality degradation in existing methods when pose estimation noise is present, enhancing the model's robustness and generalization ability in practical applications. NoPo-Avatar employs a dual-branch model, including a template branch and an image branch. The template branch captures the overall human structure and reconstructs unseen areas, while the image branch focuses on predicting Gaussian distributions aligned with the input images to capture details. Experiments show that this method achieves excellent performance on datasets such as THuman2.0, XHuman, and HuGe100K. It outperforms existing baselines in practical settings without ground-truth poses and matches methods using ground-truth poses in lab settings.", "questions": "1. Can you show the requirements for input images in real-world scenarios, such as whether the pose needs to be standing, and how the results are in such cases?\n2. Are there any better suggestions for evaluating the accuracy of faces and hands?\n3. During training, is the target pose for rendering GT, or is it robust to noise? Is there any data to support this?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Quality: The paper is technically sound in its implementation. The proposed NoPo-Avatar method outperforms existing technologies in experimental results across multiple datasets, demonstrating its advantages in reconstruction quality and robustness. The authors provide detailed experimental settings and comparative analyses to clearly demonstrate the effectiveness of the method.\n2. Clarity: The paper is well-organized and logically coherent. The entire story is narrated fluently, and the figures intuitively display the structure and characteristics of the method.\n3. Significance: This research holds significant practical implications for fields such as virtual reality (VR) and augmented reality (AR).\n4. Originality: NoPo-Avatar demonstrates innovation in several aspects: 1) Unlike previous methods, it does not require input image pose information, which is a significant advancement because such information is often inaccurate in real-world scenarios. 2) The dual-branch model design cleverly combines template priors and image details, ensuring the model's adaptability to sparse inputs while generating high-quality details.\n\nWeaknesses\n1. Dataset limitations: The datasets used for training and testing include THuman2.0, THuman2.1, and HuGe100K, with additional testing on XHuman. However, I would like to see more real-world, in-the-wild scenarios evaluated, as well as any limitations on the input person's actions, such as whether they can only be in a standing position, etc. These aspects are crucial for practical applications.\n2. Insufficient detail modeling: In the results presented by the authors, the details of hands and faces are lacking. The authors also mentioned the reasons in the limitations section, such as the small part of image occupied by hands which casues challenges in predicting LBS weights and corresponding 3D locations in the canonical T-pose for each pixel in the image branches, and also possible solutions, train extra models for hands and faces beside the body part. Considering the importance of faces and hands in practical applications, I would like to ask if there are any appropriate metrics to evaluate this part for future research? \n3. Rendering condition limitations: Although the input does not require poses, the rendering still needs GT poses. Is this GT pose the same size skeleton as the person being rendered? If so, this may not be the case in practical applications. Is it robust under noisy conditions?", "was_revised": true, "final_justification": "Final Justification: After reading the rebuttal and author responses, I find the authors have addressed all my concerns regarding dataset generalization and robustness to pose noise. I maintain my score of 5 (Accept) as the paper remains technically sound and impactful.", "rebuttal": "***QC1. Dataset limitations: The datasets used for training and testing include THuman2.0, THuman2.1, and HuGe100K, with additional testing on XHuman. However, I would like to see more real-world, in-the-wild scenarios evaluated, as well as any limitations on the input person's actions, such as whether they can only be in a standing position, etc. These aspects are crucial for practical applications.***\n\nOnly plain text is allowed for the rebuttal, so we cannot provide more rendering results. We instead refer to the presented results in our submission. In Part 4 of the supplementary webpage index.html, we demonstrate cross-domain generalization on PeopleSnapshot and UBCFashion datasets. Among the datasets, UBCFashion consists of try-on videos like those on fashion retail websites, close to in-the-wild scenarios.\nOur approach is not limited to standing poses. As results on THuman2.0 data show, our method performs well on input images showing diverse poses.\n\n***QC2. Insufficient detail modeling: In the results presented by the authors, the details of hands and faces are lacking. The authors also mentioned the reasons in the limitations section, such as the small part of image occupied by hands which causes challenges in predicting LBS weights and corresponding 3D locations in the canonical T-pose for each pixel in the image branches, and also possible solutions, train extra models for hands and faces beside the body part. Considering the importance of faces and hands in practical applications, I would like to ask if there are any appropriate metrics to evaluate this part for future research?***\n\nNote that the details are lacking when hands and faces are heavily occluded. Otherwise, the image branches which predict pixel-aligned Gaussians depict details. To evaluate the hands and faces, a possible solution is to compute the PSNR/SSIM/LPIPS* in cropped regions based on hand and face masks.\n\n***QC3. Rendering condition limitations: Although the input does not require poses, the rendering still needs GT poses. Is this GT pose the same size skeleton as the person being rendered? If so, this may not be the case in practical applications. Is it robust under noisy conditions?***\n\nDuring rendering, we require users to provide poses for animation. These can be any desired poses. We use the GT poses in the tables of the paper only for evaluation purposes, so that we can compare the renderings with the ground-truth images and baselines. This evaluation setting is identical to prior baselines.\n\n***QC4. Can you show the requirements for input images in real-world scenarios, such as whether the pose needs to be standing, and how the results are in such cases?***\n\nSee QC1.\n\n***QC5. Are there any better suggestions for evaluating the accuracy of faces and hands?***\n\nA possible solution is to compute the PSNR/SSIM/LPIPS* in cropped regions based on hand and face masks.\n\n***QC6. During training, is the target pose for rendering GT, or is it robust to noise? Is there any data to support this?***\n\nWe use the GT poses provided by the dataset during training. To analyze the robustness to poses during training, we add synthetic Gaussian noise. We find that our approach is robust to some noise, e.g., Gaussian noise of std=0.1: On THuman2.0, it achieves a PSNR/LPIPS*/FID of 22.59/110.12/49.39, compared to 22.49/105.45/42.19 with GT training poses reported in Tab. 1. If we further perturb the GT poses with more significant Gaussian noise of std=0.3, the PSNR/LPIP*/FID drops to 20.58/138.67/73.20.\nWe do not think robustness to the target poses in training is a big concern. Collecting high-quality training data is a one-time effort. Once trained, the model does not rely on poses for reconstruction at inference time. This is different from pose-dependent methods, which still require high-quality input poses during inference for reconstruction."}, {"review_id": "tRWzquiKmV", "reviewer": "Reviewer_7M22", "summary": "This paper proposes NoPo-Avatar, a new method for reconstructing animatable human avatars from sparse images without requiring any human or camera pose information. In contrast to most existing methods, NoPo-Avatar predicts a canonical T-pose avatar directly from images. The method contains two branches. The template branch reconstructs unseen regions by leveraging prior knowledge, while image branches focus on modeling visible details. Extensive experiments on multiple datasets show that the method outperforms existing methods like LHM.", "questions": "-  Unlike LHM, your method does not include a dedicated module (input) for facial modeling. Have you observed any degradation in quality or identity shift, specifically in the face region?", "limitations": "While most limitations are discussed in the supplementary materials, I believe there are additional concerns worth noting. One issue is identity preservation — the reconstructed avatars may exhibit identity shifts, which could hinder real-world applications. Moreover, accurately reconstructing and animating loose clothing remains an open challenge in this field and is not yet adequately addressed by the proposed method.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength**\n\n- Novelty\n\n  - The dual-branch architecture combining a learned template and image-aligned Gaussians is an interesting design and looks effective.\n\n- Technical soundness\n\n  - The architecture and training procedure are well-motivated and carefully designed.\n\n  - Ablation studies convincingly demonstrate the necessity of each component.\n\n- Evaluation results\n\n  - Strong results across several datasets show broad generalization.\n\n  - Comparisons with both pose-free methods (IDOL, LHM) and pose-dependent methods (LIFe-GoM, GHG) are extensive and fair.\n\n- Discussion on Failure Cases\n\n  - The paper has analyzed failure cases in the ablation study, providing further insights about the limitations of the work and the possibility of future work.\n\n- Practical applications\n\n  - The method is well-suited for real-world deployment in AR/VR scenarios due to its pose-free test-time pipeline and fast inference.\n\n**Weaknesses**\n-  The foundation of this paper is somewhat unclear. In the introduction, the authors primarily compare their method against previous approaches that rely on ground-truth or accurately estimated poses, claiming superior performance due to the removal of this dependency. However, recent methods such as LHM and IDOL also eliminate the need for pose inputs. Although the proposed method outperforms these baselines in the experiments, the authors do not provide sufficient analysis or explanation for this performance gain in the introduction.\n\n- In the quantitative results, the method is not compared against IDOL and LHM on the THuman dataset.", "was_revised": true, "final_justification": "The authors' second rebuttal can address my concerns. I would like to keep the rating as \"accept\"", "rebuttal": "Thanks for your time and feedback. We also thank the reviewer for finding our method well-motivated, effective and interesting, and the results strong.\n\n***QD1. The foundation of this paper is somewhat unclear. In the introduction, the authors primarily compare their method against previous approaches that rely on ground-truth or accurately estimated poses, claiming superior performance due to the removal of this dependency. However, recent methods such as LHM and IDOL also eliminate the need for pose inputs. Although the proposed method outperforms these baselines in the experiments, the authors do not provide sufficient analysis or explanation for this performance gain in the introduction.***\n\nThanks for pointing this out! We did not mention LHM and IDOL in the introduction since both works focus on large-scale training of human models, not pose-free reconstruction. Instead, we opted to highlight them in the related work section. We will revise our paper and emphasize the differences in the introduction.\n\n***QD2. In the quantitative results, the method is not compared against IDOL and LHM on the THuman dataset.***\n\nWe follow the split of prior works (e.g., GHG and LIFe-GoM) and compare to multiview baselines on THuman. While IDOL provides the checkpoint in its official repository and the metrics on THuman + HuGe100K’s test set in the paper, we are not able to fairly compare to it since 1) our single-view model reported in Tab. 3 is trained on HuGe100K solely, but IDOL’s checkpoint is trained on HuGe100K + THuman; and 2) we are not able to finish the training on HuGe100K + THuman due to limited resources. Moreover, LHM does not provide the training script so we will only evaluate it on the same setting without training, using the same way as in Tab.3. We will add the comparisons in our final paper.\n\n***QD3. Unlike LHM, your method does not include a dedicated module (input) for facial modeling. Have you observed any degradation in quality or identity shift, specifically in the face region?***\n\nWe do not observe identity shift or degradation in face quality, even without a dedicated facial module, as can be seen in Part 2 of the supplementary webpage index.html. Note that our image branches output pixel-aligned Gaussians, which model more details than the SMPLX template branch, especially for fine-grained regions like faces.\n\n***QD4. While most limitations are discussed in the supplementary materials, I believe there are additional concerns worth noting. One issue is identity preservation — the reconstructed avatars may exhibit identity shifts, which could hinder real-world applications. Moreover, accurately reconstructing and animating loose clothing remains an open challenge in this field and is not yet adequately addressed by the proposed method.***\n\nPlease see QD3 for the identity shift. Our approach can reconstruct loose clothing reasonably, e.g., examples 4, 6, 7, 8 in Part 2 of the supplementary webpage index.html. Our approach adopts LBS for animation. Since loose clothes do not strictly follow LBS, sometimes it may fail to animate challenging poses. This is a common issue, also in baselines such as LIFe-GoM, IDOL and LHM, which we leave for future work. We’ll revise the limitation section to discuss this."}, {"review_id": "KDnS1qfDHw", "reviewer": "Reviewer_Y9Hx", "summary": "The paper proposes a method to generate human representations using Gaussian primitives in a canonical pose, which are then rendered into images via Gaussian splatting. The approach works with either single or multiple input images and notably removes the need for both human pose and camera pose annotations. Additionally, the generated human representations support animation.", "questions": "1) How does the model handle long garments, such as dresses, when predicting residuals in canonical pose? Are there specific mechanisms to account for the complex deformation of such clothing?\n2) Could the authors clarify the role of the canonical UV map in the pipeline? Specifically, how is it used, and what advantages does it provide?\n3) Does the method generalize well to animating subjects wearing dresses in diverse poses(especially for complex leg motion)? If so, could the authors provide examples or further discussion?", "limitations": "The approach relies on a Gaussian primitive template to inpaint missing content in unseen regions, which could lead to blurry renderings when only a single image or multi-view images with limited coverage of the subject are available. Thus the method performs best when sufficient multi-view coverage is provided, while its effectiveness is reduced in sparse or narrowly-viewed scenarios.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1) The proposed method does not require body pose or camera pose annotations, which simplifies the data requirements and increases its applicability in unconstrained settings.\n2) The method provide tools for both body part segmentation and human pose estimation, enhancing the utility of the approach for downstream tasks.\n3) The paper is clearly written and well-structured, making the methodology easy to follow.\n\nWeaknesses:\n1) The paper does not provide qualitative results on in-the-wild images; all evaluations are conducted on datasets collected in controlled laboratory settings. Given that the method does not require pose information, it would be particularly valuable to demonstrate its performance on in-the-wild data to better assess its robustness and practical applicability.\n\n2) Only a limited number of novel views of a single generated subject are presented. It would be meaningful to evaluate and demonstrate multi-view consistency in this setting to further validate the quality and coherence of the reconstruction across viewpoints.\n\n3) Animating Gaussian primitives using Linear Blend Skinning (LBS) may lead to noticeable artifacts, particularly for subjects wearing loose or complex garments such as dresses. This limits the realism and generalization of the animation results for diverse clothing types.\n\n4) There appears to be a typographical error on line 82, which has an extra period at the end of the sentence. Kindly consider revising it for clarity.", "was_revised": true, "final_justification": "The author has pointed out results for multiview consistency cases, and loose clothes cases reconstruction in the supplementary material. For the missing in-the-wild image evaluation, the author explained that the UBCfashion dataset image is closed to in-the-wild level and referred results in supplementary material applying on these images, however, comparisons are not presented against baseline methods. The function of the UV map in the proposed model has been explained and the limitation of the inpainting ability of the proposed model has been discussed by the author. The rating has been increased based on the discussion.", "rebuttal": "Thanks for your time and feedback. We also thank the reviewer for finding our method more applicable and useful for downstream tasks. We answer questions next.\n\n***QE1. The paper does not provide qualitative results on in-the-wild images; all evaluations are conducted on datasets collected in controlled laboratory settings. Given that the method does not require pose information, it would be particularly valuable to demonstrate its performance on in-the-wild data to better assess its robustness and practical applicability.***\n\nOnly plain text is allowed for the rebuttal, so we cannot show more rendering results. We instead refer to the presented results in our submission. In Part 4 of the supplementary webpage index.html, we demonstrate the cross-domain generalization on PeopleSnapshot and UBCFashion datasets. Among those datasets, UBCFashion consists of try-on videos like those on fashion retail websites, close to the in-the-wild scenarios. We kindly hope the reviewer reassesses the remarks on in-the-wild results.\n\n***QE2. Only a limited number of novel views of a single generated subject are presented. It would be meaningful to evaluate and demonstrate multi-view consistency in this setting to further validate the quality and coherence of the reconstruction across viewpoints.***\n\nIn Part 1 and Part 2 of the supplementary webpage index.html, we render 360 degree images and compare to baselines. The results demonstrate the multiview consistency as well as our improvements over baselines.\n\n***QE3. Animating Gaussian primitives using Linear Blend Skinning (LBS) may lead to noticeable artifacts, particularly for subjects wearing loose or complex garments such as dresses. This limits the realism and generalization of the animation results for diverse clothing types.***\n\nLBS is a standard practice in pose-driven 3D animation: It is adopted by all the state-of-the-art baselines including LIFe-GoM, IDOL and LHM. LBS can handle most of the animations, see Part 3 in the supplementary webpage index.html. However, artifacts exist when retargeting loose garments to extreme poses, e.g., legs are widely open. We will revise the limitations to mention this point.\nWe would like to emphasize that the main contribution of this paper is to eliminate the need for pose priors during reconstruction at test time. This is crucial since it is difficult to obtain accurate poses at test time, and pose-dependent methods are sensitive to the quality of the input poses. Meanwhile, the proposed method can also reconstruct loose clothes, as is shown in examples 4, 6, 7, 8 of Part 2 of the supplementary webpage.\n\n***QE4. How does the model handle long garments, such as dresses, when predicting residuals in canonical pose? Are there specific mechanisms to account for the complex deformation of such clothing?***\n\nIn the template branch, we predict residuals which accounts for the difference between the SMPL-X shape and the shape of an avatar with clothes, including complex garments. In the image branches, the model directly predicts the absolute positions that depict the shape of the dresses. We do not use any specific mechanism to model complex clothing. We find that this simple strategy works when trained on datasets that include complex clothing.\n\n***QE5. Could the authors clarify the role of the canonical UV map in the pipeline? Specifically, how is it used, and what advantages does it provide?***\n\nThe canonical UV map is the SMPL-X mesh in its average shape projected in the 2D UV space. It serves as the “initialization” of the Gaussians’ means in the template branch. Since the canonical UV map does not model the clothes and hair of the person, the template branch predicts the residuals of the means to the UV map to model those. Without the canonical UV map, the template branch is required to predict the absolute Gaussians’ means and LBS weights, which is more challenging than predicting the residuals. \n\n***QE6. Does the method generalize well to animating subjects wearing dresses in diverse poses (especially for complex leg motion)? If so, could the authors provide examples or further discussion?***\n\nSee QE3.\n\n***QE7. The approach relies on a Gaussian primitive template to inpaint missing content in unseen regions, which could lead to blurry renderings when only a single image or multi-view images with limited coverage of the subject are available. Thus the method performs best when sufficient multi-view coverage is provided, while its effectiveness is reduced in sparse or narrowly-viewed scenarios.***\n\nOur approach can inpaint unobserved regions. We demonstrate this in Part 2 of the supplementary webpage index.html, where models only take a single front-facing image as input and the entire back side requires inpainting. In the first example, our approach hallucinates the wrinkles on the back while LHM fails. As mentioned in the limitations, we agree that the inpainting can be blurry. We think it is mainly due to the regression-based losses during the training. This is a shared issue in IDOL, LHM, LIFe-GoM, and our method. A possible solution is to use diffusion models to inpaint high-frequency details, which we leave for future work."}]

Author rebuttal:

Meta-review: This paper eliminates pose dependencies for avatar reconstruction while maintaining competitive performance. Reviewers agreed that the idea is novel and practical, the technical design is elegant, the evaluation is thorough and convincing, the writing is clear, and the applications are promising. 

Initial concerns focused on comparisons with other **pose-free** methods, handling of challenging cases such as loose clothing, evidence on **in-the-wild** datasets, potential identity shift problem, and robustness consideration. Most of these issues were addressed during the rebuttal and discussion through the presentation of additional quantitative results and clarification. While some limitations remain (e.g., loose clothing, hand/face detail, and in-the-wild evaluation), the authors have acknowledged these issues and proposed potential solutions, which the reviewers found acceptable. 

The final ratings converged to a positive consensus (3 borderline acceptances and 2 acceptances). Therefore, **it's a clear acceptance**, considering its contributions, quality of the rebuttal, and the consensus. The additional clarifications and comparisons from the discussion are requested to be incorporated into the final version.
Decision: Accept (poster)