Comments (3)
@ykk648 The learning-based methods will show their effectiveness when the inputs are quite noisy. If the inputs look good, traditional filters or interpolation manners will also work well.
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Hi @ykk648,
They have different focuses.
For SmoothNet, it is used for smoothing jitters to get a smooth curve or pose sequence, which generalizes well across different 2D,3D, or 6D modalities (share weights across spatial dimensions). It needs to input the whole detected poses.
For DeciWatch, it focuses on high efficiency with lower mean Flops and faster inference speeds via skipping and estimating highly sparse input frames, e.g., estimating one frame in every ten frames. It inputs sparse detected poses. Because DeciWatch utilizes the continuity of human motions, it can also output a smooth pose sequence after RecoverNet.
Therefore, you can choose which method to use depending on your needs.
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@ailingzengzzz Thanks for your response, I have tested DeciWatch under mmhuman3d framework , the results looks similar to slerp
for me, it can replace filter to some extent , SmoothNet looks like a CNN-version filter with motion supervision, I'm willing to test it after next update of mmhuman3d.
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Related Issues (20)
- How to do a single denoising prediction for a given set of joint (x,y) values? HOT 1
- How's the smooth effect on root joint transitions? HOT 1
- SmoothNet vs. Transformer as the denoise and recover net HOT 4
- 论文后续有何改进的方向? HOT 1
- Add a script for own video input in .mp4 etc. and/or webcam input HOT 1
- How to add custom data? HOT 1
- 数据处理的问题 HOT 1
- Use my own dataset to generate negative optimization HOT 1
- 能否生成与smpl兼容的2d关节点?Can 2d joint points compatible with smpl be generated? HOT 1
- MMpose integration HOT 2
- Train on custom data? HOT 1
- Questions about network structure HOT 2
- Is action_name required for the dataset? HOT 3
- How to print 15 key points with simplebaseline_resnet 101_mpii?
- Can Deciwatch be executed online? HOT 2
- Inference on custom videos and multi people tracking HOT 2
- 3D custom data format HOT 3
- Bad deciwatch output HOT 5
- RuntimeError: "baddbmm_cuda" not implemented for 'Int' 在 GPU 上无法运行 HOT 2
- How to use multiple GPUs to accelerate training? HOT 1
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