Generative steganography is a research hotspot particularly with regard to information hiding, which involves the hiding of secret information by generating sufficiently “real” secret media. In recent years, generative steganography schemes have made significant progress in images, but the field of video steganography is still in the exploratory stage. Combined with deep convolutional generative adversarial nets (DCGAN), a semi-generative video steganography scheme based on a digital Carden Grille was proposed. A dual-stream video generation network based on DCGAN was designed to generate three parts of videos: foreground, background, and mask; generation network produced different videos with random noise. The digital Carden Grille is used as the key for embedding and extraction. The sender can generate a digital Carden Grille in the mask through two different methods, reasonably assign the embedding capacity among RGB channels, and use video pixels as the carrier to achieve the information embedding in a semi-generative way. The receiver can determine the embedding position of information through the Carden Grille and extract the secret information hidden in the pixels. Experimental results show that the stego video generated by this scheme has good visual quality, with a Frechet inception distance score of 92. The embedded capacity is better than the existing generative steganography schemes, up to 0.12 bpp. Using Syndrome Trellis Coding, the proposed scheme can transmit secret messages more efficiently and securely.