CoEvoer is a new cross-dependency transformer framework for upper-body expressive human pose and shape estimation that achieves state-of-the-art performance by enabling mutual enhancement between body parts.
Computer vision--ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.
FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.
A GNN learns edge probabilities to prioritize paths in Ford-Fulkerson, reducing augmentations while keeping max-flow/min-cut optimality.
citing papers explorer
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Chatting about Upper-Body Expressive Human Pose and Shape Estimation
CoEvoer is a new cross-dependency transformer framework for upper-body expressive human pose and shape estimation that achieves state-of-the-art performance by enabling mutual enhancement between body parts.
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AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.
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Text-Guided Multi-Scale Frequency Representation Adaptation
FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.
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Graph Neural Network-Informed Predictive Flows for Faster Ford-Fulkerson and PAC-Learnability
A GNN learns edge probabilities to prioritize paths in Ford-Fulkerson, reducing augmentations while keeping max-flow/min-cut optimality.