Title: NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses
Venue: NeurIPS 2025
Accepted: yes
Keywords: Human modeling; generalizable rendering
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).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1) 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n-  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\nLIMITATIONS:\nWhile 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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?\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nReconstruction 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n# 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\n\nQUESTIONS:\nSee the weaknesses.\n\nLIMITATIONS:\nSee the weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}