Comments (1)
Generally speaking, our Ray3D isn't suitable for in-the-wild scenario. For the reason that you have to inject camera intrinsic and extrinsic parameters into the model. If you can perform calibration for the images that you collect from the scenario, you can follow my answer presented in issue #8 to construct a custom dataset and then perform inference.
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Related Issues (18)
- Testing on MPI-INF-3DHP HOT 2
- 3dhp data required HOT 1
- 3dhp 2d keypoints HOT 1
- can you provide a config and pretrained model of 1 frame HOT 3
- 2d keypoint npz for test videos HOT 4
- 关于aggregate_camera HOT 3
- Hope can provide a guide on how to test on custom data HOT 1
- Why do I use the extrinsic and extrinsic parameters of the camera to train the model? HOT 1
- 相机augmentation的问题 HOT 1
- 运行单个视频的问题 HOT 2
- when use in wild videos, what affect its performance? HOT 3
- error results on pretrained model HOT 1
- 关于预训练模型预测时结果错误的问题 HOT 4
- Predictions wont visualize, only GTs do HOT 3
- what is dapalib? I can't find any way to download library named dapalib HOT 1
- Question about the camera parameters of the Human3.6M dataset HOT 3
- The camera parameters within your h36m_dataset.py appear to be inconsistent with those required by VideoPose3D. HOT 1
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