Hi, thanks for your awesome work.
I have a question as an extension to Issue #8 .
I understand that the provided pretrained model can be used recursively to produce results for more moments than t=0.5.
However as far as I understood, according to the paper and your responses, it seems like the conclusion leads to: 'although the provided model is for predicting an intermediate frame t=0.5, it can be rebuilt for estimating frame at random t.'
I thus tried to reproduce your model for arbitrary time t based on your code and figured that it doesn't seem possible to be extended for arbitrary time.
Specifically, I doubt that Eq. 15 of the paper cannot hold for arbitrary time t.
The pixel intensity seems to be intended to sum to 1. The equation holds as long as t=0.5, but for example, when t=0.3, M_t1=0, M_t0=1, the pixel intensity becomes 1.4 .
I wanted to ask if there is anything that I misunderstood or got wrong...
Please, correct me if I am wrong.
Hi, thanks for your awesome work.
I have a question as an extension to Issue #8 .
I understand that the provided pretrained model can be used recursively to produce results for more moments than t=0.5.
However as far as I understood, according to the paper and your responses, it seems like the conclusion leads to: 'although the provided model is for predicting an intermediate frame t=0.5, it can be rebuilt for estimating frame at random t.'
I thus tried to reproduce your model for arbitrary time t based on your code and figured that it doesn't seem possible to be extended for arbitrary time.
Specifically, I doubt that Eq. 15 of the paper cannot hold for arbitrary time t.
The pixel intensity seems to be intended to sum to 1. The equation holds as long as t=0.5, but for example, when t=0.3, M_t1=0, M_t0=1, the pixel intensity becomes 1.4 .
I wanted to ask if there is anything that I misunderstood or got wrong...
Please, correct me if I am wrong.