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ARXIV:2605.09856 · HUMAN MESH RECOVERY · SUBMITTED 12 MAY · 20:14 UTC · FRESHNESS FRESH
ARXIV:2605.09856HUMAN MESH RECOVERYSUBMITTED 12 MAY · 20:14 UTCFRESHNESS FRESHTao Tang · Hong Liu · Xinshun Wang · Wanruo Zhang · arXiv
MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency.
Opportunity summary
Pain MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency.
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Blocker Evidence unverified
MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency. Inspired by the rapid advancements in human motion prediction, we discover that compared to occluded image features, pose sequence inherently…
Although recent studies have made remarkable progress in human mesh recovery, they still exhibit limited robustness to occlusions and often produce inaccurate poses and severe motion jitter due to the insufficient spatial features for…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Extensive experiments demonstrate that MoPO achieves state-of-the-art performance on both occlusion-specific and standard benchmarks, significantly enhancing the accuracy and temporal consistency of occluded human…
Human Mesh Recovery moved forward this cycle; last verified May 2026. Public score 9.0/10. Production flags indicate code availability.
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MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency.
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10.48550/arXiv.2605.09856MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency.
Abstract
Although recent studies have made remarkable progress in human mesh recovery, they still exhibit limited robustness to occlusions and often produce inaccurate poses and severe motion jitter due to the insufficient spatial features for occluded body parts. Inspired by the rapid advancements in human motion prediction, we discover that compared to occluded image features, pose sequence inherently contains reliable motion prior for estimating occluded body parts. In this paper, we incorporate Motion Prior for Occluded human mesh recovery, called MoPO. Our MoPO mainly consists of two components: 1) The motion de-occlusion module, where we propose a spatial-temporal occlusion detector to detect joint visibility, and then we propose a lightweight motion predictor to complete the occluded body parts by predicting the most plausible joint positions based on history poses. 2) The motion-aware fusion and refinement module, which fuses the completed joint sequence with image features to estimate human shape and initial human pose. Moreover, the completed joint sequence is further used to refine the final human pose through inverse kinematics, which provides the occlusion-free motion prior for regressing human poses. Extensive experiments demonstrate that MoPO achieves state-of-the-art performance on both occlusion-specific and standard benchmarks, significantly enhancing the accuracy and temporal consistency of occluded human mesh recovery. Our code and demo can be found in the supplementary material.
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PROBLEM
MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency. Inspired by the rapid advancements in human motion prediction, we discover that compared to occluded image features, pose sequence inherently contains reliable motion prior for e...
METHOD
Although recent studies have made remarkable progress in human mesh recovery, they still exhibit limited robustness to occlusions and often produce inaccurate poses and severe motion jitter due to the insufficient spatial features for occluded body parts. Inspired by the rapid a...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Extensive experiments demonstrate that MoPO achieves state-of-the-art performance on both occlusion-specific and standard benchmarks, significantly enhancing the accuracy and temporal consistency of occlu...
WHY NOW
Human Mesh Recovery moved forward this cycle; last verified May 2026. Public score 9.0/10. Production flags indicate code availability.
The motion de-occlusion module, where we propose a spatial-temporal occlusion detector to detect joint visibility, and then we propose a lightweight motion predictor to complete the occluded body parts by predicting the most plausible joint positions based on history poses.
Directly stated in the abstract and described as a key component of the method.
partial
The motion-aware fusion and refinement module, which fuses the completed joint sequence with image features to estimate human shape and initial human pose. Moreover, the completed joint sequence is further used to refine the final human pose through inverse kinematics, which provides the occlusion-free motion prior for regressing human poses.
Directly stated in the abstract as a core component of the method.
partial
Extensive experiments demonstrate that MoPO achieves state-of-the-art performance on both occlusion-specific and standard benchmarks, significantly enhancing the accuracy and temporal consistency of occluded human mesh recovery.
Directly stated in the abstract, but specific benchmark names and numeric results are not provided in the excerpt.
partial
significantly enhancing the accuracy and temporal consistency of occluded human mesh recovery.
Directly stated in the abstract as a key result.
partial
we discover that compared to occluded image features, pose sequence inherently contains reliable motion prior for estimating occluded body parts.
Stated as a discovery in the abstract, but the evidence is qualitative rather than quantitative.
partial
they still exhibit limited robustness to occlusions and often produce inaccurate poses and severe motion jitter due to the insufficient spatial features for occluded body parts.
Stated as a limitation of prior work in the abstract, but no specific prior methods or quantitative comparisons are given in the excerpt.
partial
The motion de-occlusion module, where we propose a spatial-temporal occlusion detector to detect joint visibility, and then we propose a lightweight motion predictor to complete the occluded body parts by predicting the most plausible joint positions based on history poses.
Directly stated in the abstract with clear description of components.
partial
The motion-aware fusion and refinement module, which fuses the completed joint sequence with image features to estimate human shape and initial human pose. Moreover, the completed joint sequence is further used to refine the final human pose through inverse kinematics, which provides the occlusion-free motion prior for regressing human poses.
Directly stated in the abstract.
partial
Extensive experiments demonstrate that MoPO achieves state-of-the-art performance on both occlusion-specific and standard benchmarks, significantly enhancing the accuracy and temporal consistency of occluded human mesh recovery.
Explicitly stated in the abstract, but specific benchmark numbers are not provided in the excerpt.
partial
significantly enhancing the accuracy and temporal consistency of occluded human mesh recovery.
Directly stated in the abstract, but without specific quantitative metrics in the excerpt.
partial
we discover that compared to occluded image features, pose sequence inherently contains reliable motion prior for estimating occluded body parts.
Stated as a discovery in the abstract, but not backed by specific evidence in the excerpt.
partial
they still exhibit limited robustness to occlusions and often produce inaccurate poses and severe motion jitter due to the insufficient spatial features for occluded body parts.
Stated as a limitation of prior work, but no specific references or data are provided in the excerpt.
partial
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MoPO enhances occluded human mesh recovery using motion prior for improved accuracy and consistency.
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Human Mesh Recovery
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