Comments (2)
Thank you for your interests in our work!
We were mainly using the first strategy (similar to GraphCMR) for both human mesh and hand mesh estimation tasks.
We later explored the second strategy (w/ conv1d layer) and see if we can improve further. It seems like it can slightly improves human mesh estimation about ~0.5 PA-MPJPE, but no improvement for hand mesh estimation.
We think these probably suggest that fine-grained local visual details are important to our tasks. This motivates us to explore better strategy with grid features (i.e., add grid features as input tokens and allow interactions between all the joints, vertices and grid features). We presented more details in our recent paper Mesh Graphormer.
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Thanks for the quick reply!
Interesting! I'll read the new paper later :)
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Related Issues (20)
- This repo is missing important files
- How to visualize attention correlation as the paper
- Training on single dataset HOT 1
- Output Score / Confidence
- Project dependencies may have API risk issues
- Cannot render my hand by my photo HOT 1
- About the template vertices and joints
- Question about training with hand dataset? HOT 1
- Can't download 'https://datarelease.blob.core.windows.net/metro/datasets/filename.tar'
- pre-trained models!ERROR 400: Bad Request.
- Demo Lack of three-digit reconstruction effect,缺少三位重建 HOT 1
- COCO SMPL Data Missing
- About DDP train on the specified gpu
- It is stopped in 2023-10-31 16:00:44,169 METRO Inference INFO: Using 1 GPUs
- Exception: Unable to get url: http://files.is.tue.mpg.de/mloper/opendr/osmesa/OSMesa.Linux.aarch64.zip HOT 1
- Question about the learning rate adjustment strategy
- Question about weight decay setting
- Pre-trained model licensing
- How to do single-player multi-card training? HOT 1
- Apex version incompatible HOT 2
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