SemanticGen: Video Generation in Semantic Space
State-of-the-art video generative models typically learn the distribution of video latents in the VAE space and map them to pixels using a VAE decoder. While this approach can generate high-quality videos, it suffers from slow convergence and is computationally expensive when generating long videos. In this paper, we introduce SemanticGen, a novel solution to address these limitations by generating videos in the semantic space. Our main insight is that, due to the inherent redundancy in videos, the generation process should begin in a compact, high-level semantic space for global planning, followed by the addition of high-frequency details, rather than directly modeling a vast set of low-level video tokens using bi-directional attention. SemanticGen adopts a two-stage generation process. In the first stage, a diffusion model generates compact semantic video features, which define the global layout of the video. In the second stage, another diffusion model generates VAE latents conditioned on these semantic features to produce the final output. We observe that generation in the semantic space leads to faster convergence compared to the VAE latent space. Our method is also effective and computationally efficient when extended to long video generation. Extensive experiments demonstrate that SemanticGen produces high-quality videos and outperforms state-of-the-art approaches and strong baselines.
694b614d746a34b55dd53d1a
Jianhong Bai
694b614d746a34b55dd53d1b
Xiaoshi Wu
694b614d746a34b55dd53d1c
Xintao Wang
694b614d746a34b55dd53d1d
Fu Xiao
694b614d746a34b55dd53d1e
Yuanxing Zhang
694b614d746a34b55dd53d1f
Qinghe Wang
claimed_verified
2025-12-25T20:45:45.478Z
646f3418a6a58aa29505fd30
https://cdn-avatars.huggingface.co/v1/production/uploads/646f3418a6a58aa29505fd30/1z13rnpb6rsUgQsYumWPg.png
QINGHE WANG
Qinghew
user
Qinghew
694b614d746a34b55dd53d20
Xiaoyu Shi
694b614d746a34b55dd53d21
Menghan Xia
694b614d746a34b55dd53d22
Zuozhu Liu
694b614d746a34b55dd53d23
Haoji Hu
694b614d746a34b55dd53d24
Pengfei Wan
694b614d746a34b55dd53d25
Kun Gai
SemanticGen: Video Generation in Semantic Space
State-of-the-art video generative models typically learn the distribution of video latents in the VAE space and map them to pixels using a VAE decoder. While this approach can generate high-quality videos, it suffers from slow convergence and is computationally expensive when generating long videos. In this paper, we introduce SemanticGen, a novel solution to address these limitations by generating videos in the semantic space. Our main insight is that, due to the inherent redundancy in videos, the generation process should begin in a compact, high-level semantic space for global planning, followed by the addition of high-frequency details, rather than directly modeling a vast set of low-level video tokens using bi-directional attention. SemanticGen adopts a two-stage generation process. In the first stage, a diffusion model generates compact semantic video features, which define the global layout of the video. In the second stage, another diffusion model generates VAE latents conditioned on these semantic features to produce the final output. We observe that generation in the semantic space leads to faster convergence compared to the VAE latent space. Our method is also effective and computationally efficient when extended to long video generation. Extensive experiments demonstrate that SemanticGen produces high-quality videos and outperforms state-of-the-art approaches and strong baselines.
SemanticGen addresses slow convergence and computational costs in video generation by using a two-stage diffusion model approach that first generates semantic features and then VAE latents, leading to faster convergence and high-quality results.
VAE space VAE decoder semantic space diffusion model semantic video features bi-directional attention