Hi,
The reason that documentation says it expects 112x112 is that the official paper of Kinetic400 which demonstrated benchmarks for 3DConvNets, have used random crop to 112x112 as a data augmentation process. Section 4.3 and 4.4 2 of have explained the procedure.
And as you have mentioned, in models with adaptive pooling in the last layer, you can pass inputs with arbitrary size but a minimum as the kernel sizes and strides will make input size zero.
Another instance is all image classification pretrained models that use adaptive pool and expect at least 224x224 input size.
Bests