Being　capable　of　extracting　more　information　than　2D　Convolutional　Neural　Networks　(CNNs),　3D　CNNs　have　been　playing　a　vital　role　in　video　analysis　tasks　like　human　action　recognition,　but　their　massive　operations　hinder　the　real-time　execution　on　edge　devices　with　constrained　computation　and　memory　resources.　Although　various　model　compression　techniques　have　been　applied　to　accelerate　2D　CNNs,　there　are　rare　efforts　in　investigating　hardware-friendly　pruning　of　3D　CNNs　and　acceleration　on　customizable　edge　platforms　like　FPGAs.　This　work　starts　from　proposing　a　kernel　group　row-column　(KGRC)　weight　sparsity　pattern,　which　is　fine-grained　to　achieve　high　pruning　ratios　with　negligible　accuracy　loss,　and　balanced　across　kernel　groups　to　achieve　high　computation　parallelism　on　hardware.　The　reweighted　pruning　algorithm　for　this　sparsity　is　then　presented　and　performed　on　3D　CNNs,　followed　by　quantization　under　different　precisions.　Along　with　model　compression,　FPGA-based　accelerators　with　four　modes　are　designed　in　support　of　the　kernel　group　sparsity　in　multiple　dimensions.　The　co-design　framework　of　the　pruning　algorithm　and　the　accelerator　is　tested　on　two　representative　3D　CNNs,　namely　C3D　and　R(2+1)D,　with　the　Xilinx　ZCU102　FPGA　platform　for　action　recognition.　The　experimental　results　indicate　that　the　accelerator　implementation　with　the　KGRC　sparsity　and　8-bit　quantization　achieves　a　good　balance　between　the　speedup　and　model　accuracy,　leading　to　acceleration　ratios　of　4.12×　for　C3D　and　3.85×　for　R(2+1)D　compared　with　the　16-bit　baseline　designs　supporting　only　dense　models.　IEEE